conference
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NeurIPS
| 2,023
| 70,456
|
Softmax Output Approximation for Activation Memory-Efficient Training of Attention-based Networks
|
In this paper, we propose to approximate the softmax output, which is the key product of the attention mechanism, to reduce its activation memory usage when training attention-based networks (aka Transformers). During the forward pass of the network, the proposed softmax output approximation method stores only a small fraction of the entire softmax output required for back-propagation and evicts the rest of the softmax output from memory. Then, during the backward pass, the evicted softmax activation output is approximated to compose the gradient to perform back-propagation for model training. Considering most attention-based models heavily rely on the softmax-based attention module that usually takes one of the biggest portions of the network, approximating the softmax activation output can be a simple yet effective way to decrease the training memory requirement of many attention-based networks. The experiment with various attention-based models and relevant tasks, i.e., machine translation, text classification, and sentiment analysis, shows that it curtails the activation memory usage of the softmax-based attention module by up to 84% (6.2× less memory) in model training while achieving comparable or better performance, e.g., up to 5.4% higher classification accuracy.
|
[
"Neural Networks",
"Natural Language Processing",
"Model Optimization",
"Memory-Efficient Computing"
] | |
NeurIPS
| 2,022
| 53,156
|
On the Stability and Scalability of Node Perturbation Learning
|
To survive, animals must adapt synaptic weights based on external stimuli and rewards. And they must do so using local, biologically plausible, learning rules -- a highly nontrivial constraint. One possible approach is to perturb neural activity (or use intrinsic, ongoing noise to perturb it), determine whether performance increases or decreases, and use that information to adjust the weights. This algorithm -- known as node perturbation -- has been shown to work on simple problems, but little is known about either its stability or its scalability with respect to network size. We investigate these issues both analytically, in deep linear networks, and numerically, in deep nonlinear ones.We show analytically that in deep linear networks with one hidden layer, both learning time and performance depend very weakly on hidden layer size. However, unlike stochastic gradient descent, when there is model mismatch between the student and teacher networks, node perturbation is always unstable. The instability is triggered by weight diffusion, which eventually leads to very large weights. This instability can be suppressed by weight normalization, at the cost of bias in the learning rule. We confirm numerically that a similar instability, and to a lesser extent scalability, exist in deep nonlinear networks trained on both a motor control task and image classification tasks. Our study highlights the limitations and potential of node perturbation as a biologically plausible learning rule in the brain.
|
[
"Computational Neuroscience",
"Neural Networks",
"Biological Learning Models",
"Synaptic Plasticity"
] | |
ICML
| 2,022
| 16,835
|
EqR: Equivariant Representations for Data-Efficient Reinforcement Learning
|
We study a variety of notions of equivariance as an inductive bias in Reinforcement Learning (RL). In particular, we propose new mechanisms for learning representations that are equivariant to both the agent’s action, as well as symmetry transformations of the state-action pairs. Whereas prior work on exploiting symmetries in deep RL can only incorporate predefined linear transformations, our approach allows non-linear symmetry transformations of state-action pairs to be learned from the data. This is achieved through 1) equivariant Lie algebraic parameterization of state and action encodings, 2) equivariant latent transition models, and 3) the incorporation of symmetry-based losses. We demonstrate the advantages of our method, which we call Equivariant representations for RL (EqR), for Atari games in a data-efficient setting limited to 100K steps of interactions with the environment.
|
[
"Reinforcement Learning",
"Representation Learning",
"Symmetry and Group Theory in Machine Learning"
] | |
NeurIPS
| 2,022
| 64,155
|
Rethinking Learning Dynamics in RL using Adversarial Networks
|
Recent years have seen tremendous progress in methods of reinforcement learning. However, most of these approaches have been trained in a straightforward fashion and are generally not robust to adversity, especially in the meta-RL setting. To the best of our knowledge, our work is the first to propose an adversarial training regime for Multi-Task Reinforcement Learning, which requires no manual intervention or domain knowledge of the environments. Our experiments on multiple environments in the Multi-Task Reinforcement learning domain demonstrate that the adversarial process leads to a better exploration of numerous solutions and a deeper understanding of the environment. We also adapt existing measures of causal attribution to draw insights from the skills learned, facilitating easier re-purposing of skills for adaptation to unseen environments and tasks.
|
[
"Reinforcement Learning",
"Adversarial Machine Learning",
"Multi-Task Learning",
"Meta-Reinforcement Learning"
] | |
ICLR
| 2,024
| 17,915
|
Bandits Meet Mechanism Design to Combat Clickbait in Online Recommendation
|
We study a strategic variant of the multi-armed bandit problem, which we coin the strategic click-bandit. This model is motivated by applications in online recommendation where the choice of recommended items depends on both the click-through rates and the post-click rewards. Like in classical bandits, rewards follow a fixed unknown distribution. However, we assume that the click-rate of each arm is chosen strategically by the arm (e.g., a host on Airbnb) in order to maximize the number of times it gets clicked. The algorithm designer does not know the post-click rewards nor the arms' actions (i.e., strategically chosen click-rates) in advance, and must learn both values over time. To solve this problem, we design an incentive-aware learning algorithm, UCB-S, which achieves two goals simultaneously: (a) incentivizing desirable arm behavior under uncertainty; (b) minimizing regret by learning unknown parameters. We approximately characterize all Nash equilibria of the arms under UCB-S and show a $\tilde{\mathcal{O}} (\sqrt{KT})$ regret bound uniformly in every equilibrium. We also show that incentive-unaware algorithms generally fail to achieve low regret in the strategic click-bandit. Finally, we support our theoretical results by simulations of strategic arm behavior which confirm the effectiveness and robustness of our proposed incentive design.
|
[
"Mechanism Design",
"Online Algorithms",
"Game Theory",
"Multi-Armed Bandit Problems",
"Recommender Systems"
] | |
ICLR
| 2,024
| 19,129
|
Simplicial Representation Learning with Neural $k$-Forms
|
Geometric deep learning extends deep learning to incorporate information about the geometry and topology data, especially in complex domains like graphs. Despite the popularity of message passing in this field, it has limitations such as the need for graph rewiring, ambiguity in interpreting data, and over-smoothing. In this paper, we take a different approach, focusing on leveraging geometric information from simplicial complexes embedded in $\mathbb{R}^n$ using node coordinates. We use differential $k$-forms in $\mathbb{R}^n$ to create representations of simplices, offering interpretability and geometric consistency without message passing. This approach also enables us to apply differential geometry tools and achieve universal approximation. Our method is efficient, versatile, and applicable to various input complexes, including graphs, simplicial complexes, and cell complexes. It outperforms existing message passing neural networks in harnessing information from geometrical graphs with node features serving as coordinates.
|
[
"Geometric Deep Learning",
"Topological Data Analysis",
"Simplicial Complexes",
"Differential Geometry",
"Graph Neural Networks"
] | |
NeurIPS
| 2,022
| 54,858
|
NS3: Neuro-symbolic Semantic Code Search
|
Semantic code search is the task of retrieving a code snippet given a textual description of its functionality. Recent work has been focused on using similarity metrics between neural embeddings of text and code. However, current language models are known to struggle with longer, compositional sentences, and multi-step reasoning. To overcome this limitation, we propose supplementing the query sentence with a layout of its semantic structure. The semantic layout is used to break down the final reasoning decision into a series of lower-level decisions. We use a Neural Module Network architecture to implement this idea. We compare our model - $NS^3$ (Neuro-Symbolic Semantic Search) - to a number of baselines, including state-of-the-art semantic code retrieval methods, such as CodeBERT, CuBERT and GraphCodeBERT, and evaluate on two datasets - Code Search Net (CSN) and Code Search and Question Answering (CoSQA). On these datasets, we demonstrate that our approach results in higher performance. We also perform additional studies to show the effectiveness of our modular design when handling compositional queries.
|
[
"Natural Language Processing",
"Information Retrieval",
"Software Engineering",
"Semantic Code Search",
"Neuro-symbolic Computing"
] | |
NeurIPS
| 2,022
| 55,254
|
A Spectral Approach to Item Response Theory
|
The Rasch model is one of the most fundamental models in item response theory and has wide-ranging applications from education testing to recommendation systems. In a universe with $n$ users and $m$ items, the Rasch model assumes that the binary response $X_{li} \in \{0,1\}$ of a user $l$ with parameter $\theta^*_l$ to an item $i$ with parameter $\beta^*_i$ (e.g., a user likes a movie, a student correctly solves a problem) is distributed as $\mathbb{P}(X_{li}=1) = 1/(1 + \exp(-(\theta^*_l - \beta^*_i)))$. In this paper, we propose a new item estimation algorithm for this celebrated model (i.e., to estimate $\beta^*$). The core of our algorithm is the computation of the stationary distribution of a Markov chain defined on an item-item graph. We complement our algorithmic contributions with finite-sample error guarantees, the first of their kind in the literature, showing that our algorithm is consistent and enjoys favorable optimality properties. We discuss practical modifications to accelerate and robustify the algorithm that practitioners can adopt. Experiments on synthetic and real-life datasets, ranging from small education testing datasets to large recommendation systems datasets show that our algorithm is scalable, accurate, and competitive with the most commonly used methods in the literature.
|
[
"Item Response Theory",
"Educational Testing",
"Recommendation Systems",
"Machine Learning Algorithms",
"Markov Chains",
"Statistical Modeling"
] | |
ICML
| 2,023
| 24,962
|
End-to-End Full-Atom Antibody Design
|
Antibody design is an essential yet challenging task in various domains like therapeutics and biology. There are two major defects in current learning-based methods: 1) tackling only a certain subtask of the whole antibody design pipeline, making them suboptimal or resource-intensive. 2) omitting either the framework regions or side chains, thus incapable of capturing the full-atom geometry. To address these pitfalls, we propose dynamic Multi-channel Equivariant grAph Network (dyMEAN), an end-to-end full-atom model for E(3)-equivariant antibody design given the epitope and the incomplete sequence of the antibody. Specifically, we first explore structural initialization as a knowledgeable guess of the antibody structure and then propose shadow paratope to bridge the epitope-antibody connections. Both 1D sequences and 3D structures are updated via an adaptive multi-channel equivariant encoder that is able to process protein residues of variable sizes when considering full atoms. Finally, the updated antibody is docked to the epitope via the alignment of the shadow paratope. Experiments on epitope-binding CDR-H3 design, complex structure prediction, and affinity optimization demonstrate the superiority of our end-to-end framework and full-atom modeling.
|
[
"Computational Biology",
"Bioinformatics",
"Structural Biology",
"Protein Engineering",
"Machine Learning in Biology"
] | |
NeurIPS
| 2,023
| 79,168
|
Syllabus: Curriculum Learning Made Easy
|
Curriculum learning has been a quiet yet crucial component of many of the high-profile successes of reinforcement learning. Despite this, none of the major reinforcement learning libraries support curriculum learning or include curriculum learning algorithms. Curriculum learning methods can provide general and complementary improvements to RL algorithms, but they often require significant, complex changes to agent training code. We introduce Syllabus, a library for training RL agents with curriculum learning, as a solution to this problem. Syllabus provides a universal API for implementing curriculum learning algorithms, a collection of implementations of popular curriculum learning methods, and infrastructure for easily integrating them into existing distributed RL code. Syllabus provides a clean API for each of the complex components of these methods, dramatically simplifying the process for designing new algorithms or applying existing algorithms to new environments. Syllabus also manages the multiprocessing communication required for curriculum learning, alleviating one of the key practical challenges of using these algorithms. We hope Syllabus will improve the process of developing and applying curriculum learning algorithms, and encourage widespread adaptation of curriculum learning.
|
[
"Reinforcement Learning",
"Curriculum Learning",
"Machine Learning Libraries",
"Artificial Intelligence Tools and Frameworks"
] | |
ICLR
| 2,022
| 5,914
|
Inductive Relation Prediction Using Analogy Subgraph Embeddings
|
Prevailing methods for relation prediction in heterogeneous graphs aim at learning latent representations (i.e., embeddings) of observed nodes and relations, and thus are limited to the transductive setting where the relation types must be known during training. Here, we propose ANalogy SubGraphEmbeddingLearning (GraphANGEL), a novel relation prediction framework that predicts relations5between each node pair based on the subgraphs containing the pair, as well as other (analogy) subgraphs with the same graph patterns. Each graph pattern explicitly represents a specific logical rule, which contributes to an inductive bias that facilitates generalization to unseen relations and leads to more explainable predictive models. Moreover, our method also removes the limited neighborhood constraint of graph neural networks. Our model consistently outperforms existing models on heterogeneous graph based recommendation as well as knowledge graph completion. We also empirically demonstrate our model’s capability in generalizing to new relations while producing explainable heat maps of attention scores across the discovered logic.
|
[
"Graph Neural Networks",
"Knowledge Graphs",
"Inductive Learning",
"Relation Prediction",
"Explainable AI"
] | |
ICML
| 2,023
| 24,271
|
How Bad is Top-$K$ Recommendation under Competing Content Creators?
|
This study explores the impact of content creators' competition on user welfare in recommendation platforms, as well as the long-term dynamics of relevance-driven recommendations. We establish a model of creator competition, under the setting where the platform uses a top-$K$ recommendation policy, user decisions are guided by the Random Utility model, and creators, in absence of explicit utility functions, employ arbitrary no-regret learning algorithms for strategy updates. We study the user welfare guarantee through the lens of Price of Anarchy and show that the fraction of user welfare loss due to creator competition is always upper bounded by a small constant depending on $K$ and randomness in user decisions; we also prove the tightness of this bound. Our result discloses an intrinsic merit of the relevance-driven recommendation policy, as long as users' decisions involve randomness and the platform provides reasonably many alternatives to its users.
|
[
"Recommender Systems",
"Game Theory",
"User Behavior Analysis",
"Economics of Information Systems"
] | |
NeurIPS
| 2,023
| 71,311
|
A Unified Algorithm Framework for Unsupervised Discovery of Skills based on Determinantal Point Process
|
Learning rich skills under the option framework without supervision of external rewards is at the frontier of reinforcement learning research. Existing works mainly fall into two distinctive categories: variational option discovery that maximizes the diversity of the options through a mutual information loss (while ignoring coverage) and Laplacian-based methods that focus on improving the coverage of options by increasing connectivity of the state space (while ignoring diversity). In this paper, we show that diversity and coverage in unsupervised option discovery can indeed be unified under the same mathematical framework. To be specific, we explicitly quantify the diversity and coverage of the learned options through a novel use of Determinantal Point Process (DPP) and optimize these objectives to discover options with both superior diversity and coverage. Our proposed algorithm, ODPP, has undergone extensive evaluation on challenging tasks created with Mujoco and Atari. The results demonstrate that our algorithm outperforms state-of-the-art baselines in both diversity- and coverage-driven categories.
|
[
"Reinforcement Learning",
"Unsupervised Learning",
"Skill Discovery",
"Determinantal Point Process",
"Machine Learning Algorithms"
] | |
NeurIPS
| 2,023
| 70,045
|
VillanDiffusion: A Unified Backdoor Attack Framework for Diffusion Models
|
Diffusion Models (DMs) are state-of-the-art generative models that learn a reversible corruption process from iterative noise addition and denoising. They are the backbone of many generative AI applications, such as text-to-image conditional generation. However, recent studies have shown that basic unconditional DMs (e.g., DDPM and DDIM) are vulnerable to backdoor injection, a type of output manipulation attack triggered by a maliciously embedded pattern at model input. This paper presents a unified backdoor attack framework (VillanDiffusion) to expand the current scope of backdoor analysis for DMs. Our framework covers mainstream unconditional and conditional DMs (denoising-based and score-based) and various training-free samplers for holistic evaluations. Experiments show that our unified framework facilitates the backdoor analysis of different DM configurations and provides new insights into caption-based backdoor attacks on DMs.
|
[
"Machine Learning Security",
"Generative Models",
"Adversarial Machine Learning",
"Backdoor Attacks",
"Diffusion Models"
] | |
NeurIPS
| 2,022
| 60,359
|
Robust Forecasting for Robotic Control: A Game-Theoretic Approach
|
Modern robots require accurate forecasts to make optimal decisions in the real world. For example, self-driving cars need an accurate forecast of other agents' future actions to plan safe trajectories. Current methods rely heavily on historical time series to accurately predict the future. However, relying entirely on the observed history is problematic since it could be corrupted by noise, have outliers, or not completely represent all possible outcomes. We propose a novel framework for generating robust forecasts for robotic control to solve this problem. To model real-world factors affecting future forecasts, we introduce the notion of an adversary, which perturbs observed historical time series to increase a robot's ultimate control cost. Specifically, we model this interaction as a zero-sum two-player game between a robot's forecaster and this hypothetical adversary. We show that our proposed game may be solved to a local Nash equilibrium using gradient-based optimization techniques. Furthermore, a forecaster trained with our method performs 30.14% better on out-of-distribution real-world lane change data than baselines.
|
[
"Robotics",
"Control Systems",
"Game Theory",
"Forecasting and Prediction",
"Autonomous Vehicles"
] | |
ICML
| 2,022
| 16,757
|
Robust Deep Reinforcement Learning through Bootstrapped Opportunistic Curriculum
|
Despite considerable advances in deep reinforcement learning, it has been shown to be highly vulnerable to adversarial perturbations to state observations. Recent efforts that have attempted to improve adversarial robustness of reinforcement learning can nevertheless tolerate only very small perturbations, and remain fragile as perturbation size increases. We propose Bootstrapped Opportunistic Adversarial Curriculum Learning (BCL), a novel flexible adversarial curriculum learning framework for robust reinforcement learning. Our framework combines two ideas: conservatively bootstrapping each curriculum phase with highest quality solutions obtained from multiple runs of the previous phase, and opportunistically skipping forward in the curriculum. In our experiments we show that the proposed BCL framework enables dramatic improvements in robustness of learned policies to adversarial perturbations. The greatest improvement is for Pong, where our framework yields robustness to perturbations of up to 25/255; in contrast, the best existing approach can only tolerate adversarial noise up to 5/255. Our code is available at: https://github.com/jlwu002/BCL.
|
[
"Deep Reinforcement Learning",
"Adversarial Machine Learning",
"Robustness in Machine Learning",
"Curriculum Learning"
] | |
NeurIPS
| 2,023
| 70,375
|
Learning from Active Human Involvement through Proxy Value Propagation
|
Learning from active human involvement enables the human subject to actively intervene and demonstrate to the AI agent during training. The interaction and corrective feedback from human brings safety and AI alignment to the learning process. In this work, we propose a new reward-free active human involvement method called Proxy Value Propagation for policy optimization. Our key insight is that a proxy value function can be designed to express human intents, wherein state- action pairs in the human demonstration are labeled with high values, while those agents’ actions that are intervened receive low values. Through the TD-learning framework, labeled values of demonstrated state-action pairs are further propagated to other unlabeled data generated from agents’ exploration. The proxy value function thus induces a policy that faithfully emulates human behaviors. Human- in-the-loop experiments show the generality and efficiency of our method. With minimal modification to existing reinforcement learning algorithms, our method can learn to solve continuous and discrete control tasks with various human control devices, including the challenging task of driving in Grand Theft Auto V. Demo video and code are available at: https://metadriverse.github.io/pvp.
|
[
"Reinforcement Learning",
"Human-Computer Interaction",
"AI Safety and Alignment"
] | |
ICML
| 2,023
| 25,267
|
Towards Omni-generalizable Neural Methods for Vehicle Routing Problems
|
Learning heuristics for vehicle routing problems (VRPs) has gained much attention due to the less reliance on hand-crafted rules. However, existing methods are typically trained and tested on the same task with a fixed size and distribution (of nodes), and hence suffer from limited generalization performance. This paper studies a challenging yet realistic setting, which considers generalization across both size and distribution in VRPs. We propose a generic meta-learning framework, which enables effective training of an initialized model with the capability of fast adaptation to new tasks during inference. We further develop a simple yet efficient approximation method to reduce the training overhead. Extensive experiments on both synthetic and benchmark instances of the traveling salesman problem (TSP) and capacitated vehicle routing problem (CVRP) demonstrate the effectiveness of our method. The code is available at: https://github.com/RoyalSkye/Omni-VRP.
|
[
"Operations Research",
"Meta-Learning",
"Combinatorial Optimization",
"Neural Networks",
"Vehicle Routing Problems "
] | |
ICLR
| 2,024
| 18,783
|
Fake It Till Make It: Federated Learning with Consensus-Oriented Generation
|
In federated learning (FL), data heterogeneity is one key bottleneck that causes model divergence and limits performance. Addressing this, existing methods often regard data heterogeneity as an inherent property and propose to mitigate its adverse effects by correcting models. In this paper, we seek to break this inherent property by generating data to complement the original dataset to fundamentally mitigate heterogeneity level. As a novel attempt from the perspective of data, we propose federated learning with consensus-oriented generation (FedCOG). FedCOG consists of two key components at the client side: complementary data generation, which generates data extracted from the shared global model to complement the original dataset, and knowledge-distillation-based model training, which distills knowledge from global model to local model based on the generated data to mitigate over-fitting the original heterogeneous dataset.FedCOG has two critical advantages: 1) it can be a plug-and-play module to further improve the performance of most existing FL methods, and 2) it is naturally compatible with standard FL protocols such as Secure Aggregation since it makes no modification in communication process.Extensive experiments on classical and real-world FL datasets show that FedCOG consistently outperforms state-of-the-art methods. Code is available at https://github.com/rui-ye/FedCOG.
|
[
"Federated Learning",
"Data Generation",
"Model Training",
"Knowledge Distillation",
"Data Heterogeneity"
] | |
NeurIPS
| 2,022
| 62,684
|
Transformer Based Kenyan Election Misinformation and Hatespeech monitoring
|
Abstract revised as required, with additional extended abstract pdf added.Kenyan presidential elections are a tense and problematic time. There are documented cases of voter-directed social media manipulation campaigns and incidents of post-election violence during election season. We build and test a dashboard to monitor the 2022 Kenyan election-related content on Twitter utilizing a BERT-derived pre-trained model for hate speech detection and an XLM-T pre-trained transformer for sentiment analysis, a balanced random forest is trained for detecting Twitter bots, and a pre-trained "bag of tricks" model for language identification. A “bag of tricks” is an optimized linear classifier that is comparable in accuracy to deep learning models while maintaining orders of magnitude more efficiently. These models can then be used to generate hourly and daily reports on hate speech, bot activity, and candidate sentiment on Twitter. Additionally, focus on implementing and deploying the dashboard efficiently with low resources is a primary focus. While deployment was not possible due to election time constraints and deployment costs. The open-source code of the dashboard is provided to allow for easy and cost-effective replication/adaption to closely related domains, with modifications implemented to allow for cost-effective deployment. It can be used to act as an early warning system for stakeholders and policymakers to take prompt action in the case of misinformation and hate speech propagation.
|
[
"Natural Language Processing",
"Misinformation Detection",
"Hate Speech Detection",
"Social Media Analysis",
"Election Monitoring",
"Sentiment Analysis",
"Computational Social Science"
] | |
ICLR
| 2,023
| 11,914
|
AANG : Automating Auxiliary Learning
|
Auxiliary objectives, supplementary learning signals that are introduced to help aid learning on data-starved or highly complex end-tasks, are commonplace in machine learning. Whilst much work has been done to formulate useful auxiliary objectives, their construction is still an art which proceeds by slow and tedious hand-design. Intuition for how and when these objectives improve end-task performance has also had limited theoretical backing. In this work, we present an approach for automatically generating a suite of auxiliary objectives. We achieve this by deconstructing existing objectives within a novel unified taxonomy, identifying connections between them, and generating new ones based on the uncovered structure. Next, we theoretically formalize widely-held intuitions about how auxiliary learning improves generalization on the end-task. This leads us to a principled and efficient algorithm for searching the space of generated objectives to find those most useful to a specified end-task.With natural language processing (NLP) as our domain of study, we demonstrate that our automated auxiliary learning pipeline leads to strong improvements over competitive baselines across continued training experiments on a pre-trained model on 5 NLP end-tasks.
|
[
"Auxiliary Learning",
"Automated Learning",
"Natural Language Processing",
"Algorithm Design",
"Generalization in Machine Learning"
] | |
NeurIPS
| 2,022
| 57,183
|
Neural DAG Scheduling via One-Shot Priority Sampling
|
We consider the problem of scheduling operations/nodes, the dependency among which is characterized by a Directed Acyclic Graph (DAG). Due to its NP-hard nature, heuristic algorithms were traditionally used to acquire reasonably good solutions, and more recent works have proposed Machine Learning (ML) heuristics that can generalize to unseen graphs and outperform the non-ML heuristics. However, it is computationally costly to generate solutions using existing ML schedulers since they adopt the episodic reinforcement learning framework that necessitates multi-round neural network processing. We propose a novel ML scheduler that uses a one-shot neural network encoder to sample node priorities which are converted by list scheduling to the final schedules. Since the one-shot encoder can efficiently sample the priorities in parallel, our algorithm runs significantly faster than existing ML baselines and has comparable run time with the fast traditional heuristics. We empirically show that our algorithm generates better schedules than both non-neural and neural baselines across various real-world and synthetic scheduling tasks.
|
[
"Neural Networks",
"Scheduling Algorithms",
"Graph Theory",
"Computational Optimization"
] | |
ICLR
| 2,024
| 22,162
|
Towards General Computer Control: A Multimodal Agent for Red Dead Redemption II as a Case Study
|
The foundation agent is one of the promising ways to achieve Artificial General Intelligence. Recent studies have demonstrated its success in specific tasks or scenarios. However, existing foundation agents cannot generalize across different scenarios, mainly due to their diverse observation and action spaces and semantic gaps. In this work, we propose the General Computer Control (GCC) setting: building foundation agents that can master any computer task by taking the screen (and possibly audio) of the computer as input and keyboard and mouse operations as output, similar to human-computer interaction. To target GCC, we propose Cradle, which has strong reasoning abilities, including self-reflection, task inference, and skill curation, to ensure its generalizability and self-improvement across various computer tasks. To demonstrate the capabilities of Cradle, we deploy it in the famous AAA game Red Dead Redemption II, serving as a preliminary attempt towards GCC. Our agent can follow the main storyline and finish real missions in this complex AAA game, with minimal reliance on prior knowledge.
|
[
"Human-Computer Interaction",
"Multimodal Systems",
"Game AI"
] | |
ICLR
| 2,024
| 18,534
|
3D-Aware Hypothesis & Verification for Generalizable Relative Object Pose Estimation
|
Prior methods that tackle the problem of generalizable object pose estimation highly rely on having dense views of the unseen object. By contrast, we address the scenario where only a single reference view of the object is available. Our goal then is to estimate the relative object pose between this reference view and a query image that depicts the object in a different pose. In this scenario, robust generalization is imperative due to the presence of unseen objects during testing and the large-scale object pose variation between the reference and the query. To this end, we present a new hypothesis-and-verification framework, in which we generate and evaluate multiple pose hypotheses, ultimately selecting the most reliable one as the relative object pose. To measure reliability, we introduce a 3D-aware verification that explicitly applies 3D transformations to the 3D object representations learned from the two input images. Our comprehensive experiments on the Objaverse, LINEMOD, and CO3D datasets evidence the superior accuracy of our approach in relative pose estimation and its robustness in large-scale pose variations, when dealing with unseen objects.
|
[
"Computer Vision",
"3D Object Recognition",
"Pose Estimation",
"Robotics"
] | |
ICLR
| 2,024
| 18,112
|
Tractable Probabilistic Graph Representation Learning with Graph-Induced Sum-Product Networks
|
We introduce Graph-Induced Sum-Product Networks (GSPNs), a new probabilistic framework for graph representation learning that can tractably answer probabilistic queries. Inspired by the computational trees induced by vertices in the context of message-passing neural networks, we build hierarchies of sum-product networks (SPNs) where the parameters of a parent SPN are learnable transformations of the a-posterior mixing probabilities of its children's sum units. Due to weight sharing and the tree-shaped computation graphs of GSPNs, we obtain the efficiency and efficacy of deep graph networks with the additional advantages of a probabilistic model. We show the model's competitiveness on scarce supervision scenarios, under missing data, and for graph classification in comparison to popular neural models. We complement the experiments with qualitative analyses on hyper-parameters and the model's ability to answer probabilistic queries.
|
[
"Graph Representation Learning",
"Probabilistic Graph Models",
"Neural Networks",
"Sum-Product Networks",
"Graph Neural Networks"
] | |
NeurIPS
| 2,022
| 55,320
|
When to Make Exceptions: Exploring Language Models as Accounts of Human Moral Judgment
|
AI systems are becoming increasingly intertwined with human life. In order to effectively collaborate with humans and ensure safety, AI systems need to be able to understand, interpret and predict human moral judgments and decisions. Human moral judgments are often guided by rules, but not always. A central challenge for AI safety is capturing the flexibility of the human moral mind — the ability to determine when a rule should be broken, especially in novel or unusual situations. In this paper, we present a novel challenge set consisting of moral exception question answering (MoralExceptQA) of cases that involve potentially permissible moral exceptions – inspired by recent moral psychology studies. Using a state-of-the-art large language model (LLM) as a basis, we propose a novel moral chain of thought (MoralCoT) prompting strategy that combines the strengths of LLMs with theories of moral reasoning developed in cognitive science to predict human moral judgments. MoralCoT outperforms seven existing LLMs by 6.2% F1, suggesting that modeling human reasoning might be necessary to capture the flexibility of the human moral mind. We also conduct a detailed error analysis to suggest directions for future work to improve AI safety using MoralExceptQA. Our data is open-sourced at https://huggingface.co/datasets/feradauto/MoralExceptQA and code at https://github.com/feradauto/MoralCoT.
|
[
"AI Safety",
"Moral Psychology",
"Cognitive Science",
"Natural Language Processing",
"Human-AI Interaction"
] | |
NeurIPS
| 2,023
| 78,780
|
Agile Modeling: From Concept to Classifier in Minutes
|
The application of computer vision methods to nuanced, subjective concepts is growing. While crowdsourcing has served the vision community well for most objective tasks (such as labeling a "zebra"), it now falters on tasks where there is substantial subjectivity in the concept (such as identifying "gourmet tuna"). However, empowering any user to develop a classifier for their concept is technically difficult: users are neither machine learning experts nor have the patience to label thousands of examples. In reaction, we introduce the problem of Agile Modeling: the process of turning any subjective visual concept into a computer vision model through real-time user-in-the-loop interactions. We instantiate an Agile Modeling prototype for image classification and show through a user study (N=14) that users can create classifiers with minimal effort in under 30 minutes. We compare this user driven process with the traditional crowdsourcing paradigm and find that the crowd's notion often differs from that of the user's, especially as the concepts become more subjective. Finally, we scale our experiments with simulations of users training classifiers for ImageNet21k categories to further demonstrate the efficacy of the approach.
|
[
"Computer Vision",
"Human-Computer Interaction",
"User-Centered Design",
"Crowdsourcing",
"Image Classification"
] | |
NeurIPS
| 2,022
| 55,230
|
Inception Transformer
|
Recent studies show that transformer has strong capability of building long-range dependencies, yet is incompetent in capturing high frequencies that predominantly convey local information. To tackle this issue, we present a novel and general-purpose $\textit{Inception Transformer}$, or $\textit{iFormer}$ for short, that effectively learns comprehensive features with both high- and low-frequency information in visual data. Specifically, we design an Inception mixer to explicitly graft the advantages of convolution and max-pooling for capturing the high-frequency information to transformers. Different from recent hybrid frameworks, the Inception mixer brings greater efficiency through a channel splitting mechanism to adopt parallel convolution/max-pooling path and self-attention path as high- and low-frequency mixers, while having the flexibility to model discriminative information scattered within a wide frequency range. Considering that bottom layers play more roles in capturing high-frequency details while top layers more in modeling low-frequency global information, we further introduce a frequency ramp structure, i.e., gradually decreasing the dimensions fed to the high-frequency mixer and increasing those to the low-frequency mixer, which can effectively trade-off high- and low-frequency components across different layers. We benchmark the iFormer on a series of vision tasks, and showcase that it achieves impressive performance on image classification, COCO detection and ADE20K segmentation. For example, our iFormer-S hits the top-1 accuracy of 83.4% on ImageNet-1K, much higher than DeiT-S by 3.6%, and even slightly better than much bigger model Swin-B (83.3%) with only 1/4 parameters and 1/3 FLOPs. Code and models are released at https://github.com/sail-sg/iFormer.
|
[
"Computer Vision",
"Deep Learning",
"Neural Networks",
"Image Processing"
] | |
ICLR
| 2,024
| 19,470
|
Chain-of-Table: Evolving Tables in the Reasoning Chain for Table Understanding
|
Table-based reasoning with large language models (LLMs) is a promising direction to tackle many table understanding tasks, such as table-based question answering and fact verification. Compared with generic reasoning, table-based reasoning requires the extraction of underlying semantics from both free-form questions and semi-structured tabular data. Chain-of-Thought and its similar approaches incorporate the reasoning chain in the form of textual context, but it is still an open question how to effectively leverage tabular data in the reasoning chain. We propose the Chain-of-Table framework, where tabular data is explicitly used in the reasoning chain as a proxy for intermediate thoughts. Specifically, we guide LLMs using in-context learning to iteratively generate operations and update the table to represent a tabular reasoning chain. LLMs can therefore dynamically plan the next operation based on the results of the previous ones. This continuous evolution of the table forms a chain, showing the reasoning process for a given tabular problem. The chain carries structured information of the intermediate results, enabling more accurate and reliable predictions. Chain-of-Table achieves new state-of-the-art performance on WikiTQ, FeTaQA, and TabFact benchmarks across multiple LLM choices.
|
[
"Natural Language Processing",
"Table Understanding",
"Large Language Models ",
"Reasoning and Inference",
"Data Representation and Transformation"
] | |
ICML
| 2,022
| 18,185
|
Marginal Tail-Adaptive Normalizing Flows
|
Learning the tail behavior of a distribution is a notoriously difficult problem. By definition, the number of samples from the tail is small, and deep generative models, such as normalizing flows, tend to concentrate on learning the body of the distribution. In this paper, we focus on improving the ability of normalizing flows to correctly capture the tail behavior and, thus, form more accurate models. We prove that the marginal tailedness of an autoregressive flow can be controlled viathe tailedness of the marginals of its base distribution. This theoretical insight leads us to a novel type of flows based on flexible base distributions and data-driven linear layers. An empirical analysis shows that the proposed method improveson the accuracy—especially on the tails of the distribution—and is able to generate heavy-tailed data. We demonstrate its application on a weather and climate example, in which capturing the tail behavior is essential.
|
[
"Deep Learning",
"Generative Models",
"Statistical Modeling",
"Probability and Statistics",
"Climate Science"
] | |
NeurIPS
| 2,022
| 56,948
|
GAN-Flow: A dimension-reduced variational framework for physics-based inverse problems
|
We propose GAN-Flow -- a modular inference approach that combines generative adversarial network (GAN) prior with a normalizing flow (NF) model to solve inverse problems in the lower-dimensional latent space of the GAN prior using variational inference. GAN-Flow leverages the intrinsic dimension reduction and superior sample generation capabilities of GANs, and the capability of NFs to efficiently approximate complicated posterior distributions. In this work, we apply GAN-Flow to solve two physics-based linear inverse problems. Results show that GAN-Flow can efficiently approximate the posterior distribution in such high-dimensional problems.
|
[
"Inverse Problems",
"Computational Physics",
"Variational Inference",
"Generative Models"
] | |
ICML
| 2,024
| 32,974
|
Latent Space Symmetry Discovery
|
Equivariant neural networks require explicit knowledge of the symmetry group. Automatic symmetry discovery methods aim to relax this constraint and learn invariance and equivariance from data. However, existing symmetry discovery methods are limited to simple linear symmetries and cannot handle the complexity of real-world data. We propose a novel generative model, Latent LieGAN (LaLiGAN), which can discover symmetries of nonlinear group actions. It learns a mapping from the data space to a latent space where the symmetries become linear and simultaneously discovers symmetries in the latent space. Theoretically, we show that our model can express nonlinear symmetries under some conditions about the group action. Experimentally, we demonstrate that our method can accurately discover the intrinsic symmetry in high-dimensional dynamical systems. LaLiGAN also results in a well-structured latent space that is useful for downstream tasks including equation discovery and long-term forecasting.
|
[
"Neural Networks",
"Symmetry Discovery",
"Generative Models",
"Computational Mathematics",
"Dynamical Systems"
] | |
ICLR
| 2,024
| 19,374
|
Exploring the Common Appearance-Boundary Adaptation for Nighttime Optical Flow
|
We investigate a challenging task of nighttime optical flow, which suffers from weakened texture and amplified noise. These degradations weaken discriminative visual features, thus causing invalid motion feature matching. Typically, existing methods employ domain adaptation to transfer knowledge from auxiliary domain to nighttime domain in either input visual space or output motion space. However, this direct adaptation is ineffective, since there exists a large domain gap due to the intrinsic heterogeneous nature of the feature representations between auxiliary and nighttime domains. To overcome this issue, we explore a common-latent space as the intermediate bridge to reinforce the feature alignment between auxiliary and nighttime domains. In this work, we exploit two auxiliary daytime and event domains, and propose a novel common appearance-boundary adaptation framework for nighttime optical flow. In appearance adaptation, we employ the intrinsic image decomposition to embed the auxiliary daytime image and the nighttime image into a reflectance-aligned common space. We discover that motion distributions of the two reflectance maps are very similar, benefiting us to consistently transfer motion appearance knowledge from daytime to nighttime domain. In boundary adaptation, we theoretically derive the motion correlation formula between nighttime image and accumulated events within a spatiotemporal gradient-aligned common space. We figure out that the correlation of the two spatiotemporal gradient maps shares significant discrepancy, benefitting us to contrastively transfer boundary knowledge from event to nighttime domain. Moreover, appearance adaptation and boundary adaptation are complementary to each other, since they could jointly transfer global motion and local boundary knowledge to the nighttime domain. Extensive experiments have been performed to verify the superiority of the proposed method.
|
[
"Computer Vision",
"Optical Flow",
"Domain Adaptation",
"Image Processing"
] | |
ICLR
| 2,024
| 17,522
|
Alice Benchmarks: Connecting Real World Re-Identification with the Synthetic
|
For object re-identification (re-ID), learning from synthetic data has become a promising strategy to cheaply acquire large-scale annotated datasets and effective models, with few privacy concerns. Many interesting research problems arise from this strategy, e.g., how to reduce the domain gap between synthetic source and real-world target. To facilitate developing more new approaches in learning from synthetic data, we introduce the Alice benchmarks, large-scale datasets providing benchmarks as well as evaluation protocols to the research community. Within the Alice benchmarks, two object re-ID tasks are offered: person and vehicle re-ID. We collected and annotated two challenging real-world target datasets: AlicePerson and AliceVehicle, captured under various illuminations, image resolutions, etc. As an important feature of our real target, the clusterability of its training set is not manually guaranteed to make it closer to a real domain adaptation test scenario. Correspondingly, we reuse existing PersonX and VehicleX as synthetic source domains. The primary goal is to train models from synthetic data that can work effectively in the real world. In this paper, we detail the settings of Alice benchmarks, provide an analysis of existing commonly-used domain adaptation methods, and discuss some interesting future directions. An online server has been set up for the community to evaluate methods conveniently and fairly. Datasets and the online server details are available at https://sites.google.com/view/alice-benchmarks.
|
[
"Computer Vision",
"Domain Adaptation",
"Synthetic Data",
"Object Re-Identification",
"Benchmarking",
"Data Annotation"
] | |
ICML
| 2,024
| 34,429
|
Quality-Diversity Actor-Critic: Learning High-Performing and Diverse Behaviors via Value and Successor Features Critics
|
A key aspect of intelligence is the ability to demonstrate a broad spectrum of behaviors for adapting to unexpected situations. Over the past decade, advancements in deep reinforcement learning have led to groundbreaking achievements to solve complex continuous control tasks. However, most approaches return only one solution specialized for a specific problem. We introduce Quality-Diversity Actor-Critic (QDAC), an off-policy actor-critic deep reinforcement learning algorithm that leverages a value function critic and a successor features critic to learn high-performing and diverse behaviors. In this framework, the actor optimizes an objective that seamlessly unifies both critics using constrained optimization to (1) maximize return, while (2) executing diverse skills. Compared with other Quality-Diversity methods, QDAC achieves significantly higher performance and more diverse behaviors on six challenging continuous control locomotion tasks. We also demonstrate that we can harness the learned skills to adapt better than other baselines to five perturbed environments. Finally, qualitative analyses showcase a range of remarkable behaviors:adaptive-intelligent-robotics.github.io/QDAC.
|
[
"Deep Reinforcement Learning",
"Quality-Diversity Optimization",
"Continuous Control",
"Robotics",
"Machine Learning Algorithms"
] | |
NeurIPS
| 2,022
| 57,712
|
Image Manipulation via Neuro-Symbolic Networks
|
We are interested in image manipulation via natural language text – a task that is extremely useful for multiple AI applications but requires complex reasoning over multi-modal spaces. Recent work on neuro-symbolic approaches has been quite effective in solving such tasks as they offer better modularity, interpretability, and generalizability. A noteworthy such approach is NSCL [25] developed for the task of Visual Question Answering (VQA). We extend NSCL for the image manipulation task and propose a solution referred to as NeuroSIM. Unlike previous works, which either require supervised data training or can only deal with very simple reasoning instructions over single object scenes; NeuroSIM can perform complex multi-hop reasoning over multi-object scenes and requires only weak supervision in the form of annotated data for the VQA task. On the language side, NeuroSIM contains neural modules that parse an instruction into a symbolic program that guides the manipulation. These programs are based on a Domain Specific Language (DSL) comprising object attributes as well as manipulation operations. On the perceptual side, NeuroSIM contains neural modules which first generate a scene graph of the input image and then change the scene graph representation in accordance with the parsed instruction. To train these modules, we design novel loss functions that are capable of testing the correctness of manipulated object and scene graph representations via query networks that are trained merely on the VQA dataset. An image decoder is trained to render the final image from the manipulated scene graph representation. The entire NeuroSIM pipeline is trained without any intermediate supervision. Extensive experiments demonstrate that our approach is highly competitive with state-of-the-art supervised baselines.
|
[
"Computer Vision",
"Natural Language Processing",
"Multi-Modal Learning",
"Neuro-Symbolic AI",
"Image Processing",
"Artificial Intelligence Applications"
] | |
NeurIPS
| 2,022
| 60,828
|
Moving Frame Net: SE(3)-Equivariant Network for Volumes
|
Equivariance of neural networks to transformations helps to improve their performance and reduce generalization error in computer visions tasks, as they apply to datasets presenting symmetries (e.g. scalings, rotations, translations). The method of moving frames is classical for deriving operators invariant to the action of a Lie group in a manifold.Recently, a rotation and translation equivariant neural network for image data was proposed based on the moving frames approach. In this paper we significantly improve that approach by reducing the computation of moving frames to only one, at the input stage, instead of repeated computations at each layer. The equivariance of the resulting architecture is proved theoretically and we build a rotation and translation equivariant neural network to process volumes, i.e. signals on the 3D space. Our trained model overperforms the benchmarks in the medical volume classification of most of the tested datasets from MedMNIST3D.
|
[
"Computer Vision",
"Neural Networks",
"Equivariant Neural Networks",
"3D Image Processing",
"Medical Image Analysis"
] | |
NeurIPS
| 2,022
| 53,246
|
LAPO: Latent-Variable Advantage-Weighted Policy Optimization for Offline Reinforcement Learning
|
Offline reinforcement learning methods hold the promise of learning policies from pre-collected datasets without the need to query the environment for new samples. This setting is particularly well-suited for continuous control robotic applications for which online data collection based on trial-and-error is costly and potentially unsafe. In practice, offline datasets are often heterogeneous, i.e., collected in a variety of scenarios, such as data from several human demonstrators or from policies that act with different purposes. Unfortunately, such datasets often contain action distributions with multiple modes and, in some cases, lack a sufficient number of high-reward trajectories, which render offline policy training inefficient. To address this challenge, we propose to leverage latent-variable generative model to represent high-advantage state-action pairs leading to better adherence to data distributions that contributes to solving the task, while maximizing reward via a policy over the latent variable. As we empirically show on a range of simulated locomotion, navigation, and manipulation tasks, our method referred to as latent-variable advantage-weighted policy optimization (LAPO), improves the average performance of the next best-performing offline reinforcement learning methods by 49\% on heterogeneous datasets, and by 8\% on datasets with narrow and biased distributions.
|
[
"Offline Reinforcement Learning",
"Robotics",
"Continuous Control"
] | |
ICLR
| 2,024
| 18,812
|
Learning to Act from Actionless Videos through Dense Correspondences
|
In this work, we present an approach to construct a video-based robot policy capable of reliably executing diverse tasks across different robots and environments from few video demonstrations without using any action annotations. Our method leverages images as a task-agnostic representation, encoding both the state and action information, and text as a general representation for specifying robot goals. By synthesizing videos that "hallucinate" robot executing actions and in combination with dense correspondences between frames, our approach can infer the closed-formed action to execute to an environment without the need of any explicit action labels. This unique capability allows us to train the policy solely based on RGB videos and deploy learned policies to various robotic tasks. We demonstrate the efficacy of our approach in learning policies on table-top manipulation and navigation tasks. Additionally, we contribute an open-source framework for efficient video modeling, enabling the training of high-fidelity policy models with four GPUs within a single day.
|
[
"Robotics",
"Computer Vision",
"Video Analysis",
"Robot Learning",
"Imitation Learning"
] | |
ICLR
| 2,022
| 6,060
|
Learning Weakly-supervised Contrastive Representations
|
We argue that a form of the valuable information provided by the auxiliary information is its implied data clustering information. For instance, considering hashtags as auxiliary information, we can hypothesize that an Instagram image will be semantically more similar with the same hashtags. With this intuition, we present a two-stage weakly-supervised contrastive learning approach. The first stage is to cluster data according to its auxiliary information. The second stage is to learn similar representations within the same cluster and dissimilar representations for data from different clusters. Our empirical experiments suggest the following three contributions. First, compared to conventional self-supervised representations, the auxiliary-information-infused representations bring the performance closer to the supervised representations, which use direct downstream labels as supervision signals. Second, our approach performs the best in most cases, when comparing our approach with other baseline representation learning methods that also leverage auxiliary data information. Third, we show that our approach also works well with unsupervised constructed clusters (e.g., no auxiliary information), resulting in a strong unsupervised representation learning approach.
|
[
"Weakly-supervised Learning",
"Contrastive Learning",
"Representation Learning",
"Clustering",
"Unsupervised Learning"
] | |
NeurIPS
| 2,022
| 57,467
|
A Simple Phoneme-based Error Simulator for ASR Error Correction
|
Despite the recent advances brought by deep neural networks, the real-world applications of Automatic Speech Recognition (ASR) inevitably suffer from various errors mostly caused by incorrectly captured phonetic features. This is of particular consequence in our work which involves the transcription of real patient clinical conversations. In this work, we aim to fix noisy off-the-shelf ASR transcriptions in low-data settings by building a simple phoneme-based error simulator that can generate large amounts of training data for post-editing ASR error correction systems. To demonstrate the efficacy of our simulated errors, we conduct experiments with different error correction architectures – our own multi-task trained dual-decoder transformer model that performs both error detection and error correction and two state-of-the-art grammatical error correction models. All these models improved in performance (by 0.3 - 1.4% WER) when pretrained on our simulated errors. Also, increasing the amount of simulated data in pretraining, from 0 to 1x and 10x the size of Librispeech, improves performance in the error correction task, regardless of the model structure. We are currently working to develop more domain-specific data to further improve transcriptions in clinical settings.
|
[
"Automatic Speech Recognition ",
"Error Correction",
"Phonetics",
"Deep Learning",
"Natural Language Processing",
"Clinical Transcription"
] | |
NeurIPS
| 2,023
| 73,490
|
Scalable 3D Captioning with Pretrained Models
|
We introduce Cap3D, an automatic approach for generating descriptive text for 3D objects. This approach utilizes pretrained models from image captioning, image-text alignment, and LLM to consolidate captions from multiple views of a 3D asset, completely side-stepping the time-consuming and costly process of manual annotation. We apply Cap3D to the recently introduced large-scale 3D dataset, Objaverse, resulting in 660k 3D-text pairs. Our evaluation, conducted using 41k human annotations from the same dataset, demonstrates that Cap3D surpasses human-authored descriptions in terms of quality, cost, and speed. Through effective prompt engineering, Cap3D rivals human performance in generating geometric descriptions on 17k collected annotations from the ABO dataset. Finally, we finetune Text-to-3D models on Cap3D and human captions, and show Cap3D outperforms; and benchmark the SOTA including Point·E, Shape·E, and DreamFusion.
|
[
"Computer Vision",
"Natural Language Processing",
"3D Modeling",
"Data Annotation and Labeling"
] | |
ICML
| 2,024
| 33,310
|
Learning the Target Network in Function Space
|
We focus on the task of learning the value function in the reinforcement learning (RL) setting. This task is often solved by updating a pair of online and target networks while ensuring that the parameters of these two networks are equivalent. We propose Lookahead-Replicate (LR), a new value-function approximation algorithm that is agnostic to this parameter-space equivalence. Instead, the LR algorithm is designed to maintain an equivalence between the two networks in the function space. This value-based equivalence is obtained by employing a new target-network update. We show that LR leads to a convergent behavior in learning the value function. We also present empirical results demonstrating that LR-based target-network updates significantly improve deep RL on the Atari benchmark.
|
[
"Reinforcement Learning",
"Value Function Approximation",
"Deep Learning",
"Machine Learning Algorithms"
] | |
ICML
| 2,023
| 24,120
|
Structure-informed Language Models Are Protein Designers
|
This paper demonstrates that language models are strong structure-based protein designers. We present LM-Design, a generic approach to reprogramming sequence-based protein language models (pLMs), that have learned massive sequential evolutionary knowledge from the universe of natural protein sequences, to acquire an immediate capability to design preferable protein sequences for given folds. We conduct a structural surgery on pLMs, where a lightweight structural adapter is implanted into pLMs and endows it with structural awareness. During inference, iterative refinement is performed to effectively optimize the generated protein sequences. Experiments show that LM-Design improves the state-of-the-art results by a large margin, leading to 4% to 12% accuracy gains in sequence recovery (e.g., 55.65%/56.63% on CATH 4.2/4.3 single-chain benchmarks, and >60% when designing protein complexes). We provide extensive and in-depth analyses, which verify that LM-Design can (1) indeed leverage both structural and sequential knowledge to accurately handle structurally non-deterministic regions, (2) benefit from scaling data and model size, and (3) generalize to other proteins (e.g., antibodies and de novo proteins).
|
[
"Computational Biology",
"Protein Design",
"Machine Learning in Biology",
"Structural Bioinformatics",
"Bioinformatics and Computational Biology"
] | |
NeurIPS
| 2,022
| 60,481
|
Train Offline, Test Online: A Real Robot Learning Benchmark
|
Three challenges limit the progress of robot learning research: robots are expensive (few labs can participate), everyone uses different robots (findings do not generalize across labs), and we lack internet-scale robotics data. We take on these challenges via a new benchmark: Train Offline, Test Online (TOTO). TOTO provides remote users with access to shared robots for evaluating methods on common tasks and an open-source dataset of these tasks for offline training. Its manipulation task suite requires challenging generalization to unseen objects, positions, and lighting. We present initial results on TOTO comparing five pretrained visual representations and four offline policy learning baselines, remotely contributed by five institutions. The real promise of TOTO, however, lies in the future: we release the benchmark for additional submissions from any user, enabling easy, direct comparison to several methods without the need to obtain hardware or collect data.
|
[
"Robotics",
"Robot Learning",
"Machine Learning Benchmarks",
"Offline Learning",
"Online Testing",
"Manipulation Tasks",
"Generalization in Robotics"
] | |
ICLR
| 2,023
| 11,214
|
Revisit Finetuning strategy for Few-Shot Learning to Transfer the Emdeddings
|
Few-Shot Learning (FSL) aims to learn a simple and effective bias on limited novel samples. Recently, many methods have been focused on re-training a randomly initialized linear classifier to adapt it to the novel features extracted by the pre-trained feature extractor (called Linear-Probing-based methods). These methods typically assumed the pre-trained feature extractor was robust enough, i.e., finetuning was not needed, and hence the pre-trained feature extractor does not change on the novel samples. However, the unchanged pre-trained feature extractor will distort the features of novel samples because the robustness assumption may not hold, especially on the out-of-distribution samples. To extract the undistorted features, we designed Linear-Probing-Finetuning with Firth-Bias (LP-FT-FB) to yield an accurate bias on the limited samples for better finetuning the pre-trained feature extractor, providing stronger transferring ability. In LP-FT-FB, we further proposed inverse Firth Bias Reduction (i-FBR) to regularize the over-parameterized feature extractor on which FBR does not work well.The proposed i-FBR effectively alleviates the over-fitting problem of the feature extractor in the process of finetuning and helps extract undistorted novel features. To show the effectiveness of the designed LP-FT-FB, we conducted a lot of experiments on the commonly used FSL datasets under different backbones, including in-domain and cross-domain FSL tasks. The experimental results show that the proposed FT-LP-FB outperforms the SOTA FSL methods. The code is available at https://github.com/whzyf951620/LinearProbingFinetuningFirthBias.
|
[
"Few-Shot Learning",
"Transfer Learning",
"Feature Extraction",
"Model Finetuning"
] | |
ICLR
| 2,024
| 18,027
|
Rethinking Branching on Exact Combinatorial Optimization Solver: The First Deep Symbolic Discovery Framework
|
Machine learning (ML) has been shown to successfully accelerate solving NP-hard combinatorial optimization (CO) problems under the branch and bound framework. However, the high training and inference cost and limited interpretability of ML approaches severely limit their wide application to modern exact CO solvers. In contrast, human-designed policies---though widely integrated in modern CO solvers due to their compactness and reliability---can not capture data-driven patterns for higher performance. To combine the advantages of the two paradigms, we propose the first symbolic discovery framework---namely, deep symbolic discovery for exact combinatorial optimization solver (Symb4CO)---to learn high-performance symbolic policies on the branching task. Specifically, we show the potential existence of small symbolic policies empirically, employ a large neural network to search in the high-dimensional discrete space, and compile the learned symbolic policies directly for fast deployment. Experiments show that the Symb4CO learned purely CPU-based policies consistently achievecomparableperformance to previous GPU-based state-of-the-art approaches. Furthermore, the appealing features of Symb4CO include its high training (ten training instances) and inference (one CPU core) efficiency and good interpretability (one-line expressions), making it simple and reliable for deployment. The results show encouraging potential for thewidedeployment of ML to modern CO solvers.
|
[
"Combinatorial Optimization",
"Symbolic Discovery",
"Operations Research",
"Algorithm Design"
] | |
ICLR
| 2,023
| 12,073
|
Dynamic Prompt Learning via Policy Gradient for Semi-structured Mathematical Reasoning
|
Mathematical reasoning, a core ability of human intelligence, presents unique challenges for machines in abstract thinking and logical reasoning. Recent large pre-trained language models such as GPT-3 have achieved remarkable progress on mathematical reasoning tasks written in text form, such as math word problems (MWP). However, it is unknown if the models can handle more complex problems that involve math reasoning over heterogeneous information, such as tabular data. To fill the gap, we present Tabular Math Word Problems (TabMWP), a new dataset containing 38,431 open-domain grade-level problems that require mathematical reasoning on both textual and tabular data. Each question in TabMWP is aligned with a tabular context, which is presented as an image, semi-structured text, and a structured table. There are two types of questions: free-text and multi-choice, and each problem is annotated with gold solutions to reveal the multi-step reasoning process. We evaluate different pre-trained models on TabMWP, including the GPT-3 model in a few-shot setting. As earlier studies suggest, since few-shot GPT-3 relies on the selection of in-context examples, its performance is unstable and can degrade to near chance. The unstable issue is more severe when handling complex problems like TabMWP. To mitigate this, we further propose a novel approach, PromptPG, which utilizes policy gradient to learn to select in-context examples from a small amount of training data and then constructs the corresponding prompt for the test example. Experimental results show that our method outperforms the best baseline by 5.31% on the accuracy metric and reduces the prediction variance significantly compared to random selection, which verifies its effectiveness in selecting in-context examples. The data and code are available at https://promptpg.github.io.
|
[
"Natural Language Processing",
"Mathematical Reasoning",
"Data Science",
"Educational Technology"
] | |
ICLR
| 2,022
| 6,011
|
Measuring CLEVRness: Black-box Testing of Visual Reasoning Models
|
How can we measure the reasoning capabilities of intelligence systems? Visual question answering provides a convenient framework for testing the model's abilities by interrogating the model through questions about the scene. However, despite scores of various visual QA datasets and architectures, which sometimes yield even a super-human performance, the question of whether those architectures can actually reason remains open to debate.To answer this, we extend the visual question answering framework and propose the following behavioral test in the form of a two-player game. We consider black-box neural models of CLEVR. These models are trained on a diagnostic dataset benchmarking reasoning. Next, we train an adversarial player that re-configures the scene to fool the CLEVR model. We show that CLEVR models, which otherwise could perform at a ``human-level'', can easily be fooled by our agent. Our results put in doubt whether data-driven approaches can do reasoning without exploiting the numerous biases that are often present in those datasets. Finally, we also propose a controlled experiment measuring the efficiency of such models to learn and perform reasoning.
|
[
"Computer Vision",
"Visual Question Answering",
"Neural Networks",
"Adversarial Machine Learning",
"Cognitive Computing"
] | |
NeurIPS
| 2,023
| 72,075
|
Unlocking Feature Visualization for Deep Network with MAgnitude Constrained Optimization
|
Feature visualization has gained significant popularity as an explainability method, particularly after the influential work by Olah et al. in 2017. Despite its success, its widespread adoption has been limited due to issues in scaling to deeper neural networks and the reliance on tricks to generate interpretable images. Here, we describe MACO, a simple approach to address these shortcomings. It consists in optimizing solely an image's phase spectrum while keeping its magnitude constant to ensure that the generated explanations lie in the space of natural images. Our approach yields significantly better results -- both qualitatively and quantitatively -- unlocking efficient and interpretable feature visualizations for state-of-the-art neural networks. We also show that our approach exhibits an attribution mechanism allowing to augment feature visualizations with spatial importance. Furthermore, we enable quantitative evaluation of feature visualizations by introducing 3 metrics: transferability, plausibility, and alignment with natural images. We validate our method on various applications and we introduce a website featuring MACO visualizations for all classes of the ImageNet dataset, which will be made available upon acceptance. Overall, our study unlocks feature visualizations for the largest, state-of-the-art classification networks without resorting to any parametric prior image model, effectively advancing a field that has been stagnating since 2017 (Olah et al, 2017).
|
[
"Deep Learning",
"Explainable AI",
"Neural Networks",
"Computer Vision"
] | |
ICML
| 2,024
| 34,943
|
Learning to Stabilize Online Reinforcement Learning in Unbounded State Spaces
|
In many reinforcement learning (RL) applications, we want policies that reach desired states and then keep the controlled system within an acceptable region around the desired states over an indefinite period of time. This latter objective is calledstabilityand is especially important when the state space is unbounded, such that the states can be arbitrarily far from each other and the agent can drift far away from the desired states. For example, in stochastic queuing networks, where queues of waiting jobs can grow without bound, the desired state is all-zero queue lengths. Here, a stable policy ensures queue lengths are finite while an optimal policy minimizes queue lengths. Since an optimal policy is also stable, one would expect that RL algorithms would implicitly give us stable policies. However, in this work, we find that deep RL algorithms that directly minimize the distance to the desired state during online training often result in unstable policies, i.e., policies that drift far away from the desired state. We attribute this instability to poor credit-assignment for destabilizing actions. We then introduce an approach based on two ideas: 1) a Lyapunov-based cost-shaping technique and 2) state transformations to the unbounded state space. We conduct an empirical study on various queueing networks and traffic signal control problems and find that our approach performs competitively against strong baselines with knowledge of the transition dynamics. Our code is available here: https://github.com/Badger-RL/STOP
|
[
"Reinforcement Learning",
"Control Systems",
"Stability Analysis",
"Queueing Theory",
"Operations Research"
] | |
ICLR
| 2,022
| 6,166
|
Auto-Transfer: Learning to Route Transferable Representations
|
Knowledge transfer between heterogeneous source and target networks and tasks has received a lot of attention in recent times as large amounts of quality labeled data can be difficult to obtain in many applications. Existing approaches typically constrain the target deep neural network (DNN) feature representations to be close to the source DNNs feature representations, which can be limiting. We, in this paper, propose a novel adversarial multi-armed bandit approach that automatically learns to route source representations to appropriate target representations following which they are combined in meaningful ways to produce accurate target models. We see upwards of 5\% accuracy improvements compared with the state-of-the-art knowledge transfer methods on four benchmark (target) image datasets CUB200, Stanford Dogs, MIT67, and Stanford40 where the source dataset is ImageNet. We qualitatively analyze the goodness of our transfer scheme by showing individual examples of the important features focused on by our target network at different layers compared with the (closest) competitors. We also observe that our improvement over other methods is higher for smaller target datasets making it an effective tool for small data applications that may benefit from transfer learning.
|
[
"Transfer Learning",
"Deep Learning",
"Neural Networks",
"Computer Vision"
] | |
NeurIPS
| 2,023
| 70,801
|
Knowledge Distillation for High Dimensional Search Index
|
Lightweight compressed models are prevalent in Approximate Nearest Neighbor Search (ANNS) and Maximum Inner Product Search (MIPS) owing to their superiority of retrieval efficiency in large-scale datasets. However, results given by compressed methods are less accurate due to the curse of dimension and the limitations of optimization objectives (e.g., lacking interactions between queries and documents). Thus, we are encouraged to design a new learning algorithm for the compressed search index on high dimensions to improve retrieval performance. In this paper, we propose a novel KnowledgeDistillation for high dimensional search index framework (KDindex), with the aim of efficiently learning lightweight indexes by distilling knowledge from high-precision ANNS and MIPS models such as graph-based indexes. Specifically, the student is guided to keep the same ranking order of the top-k relevant results yielded by the teacher model, which acts as the additional supervision signals between queries and documents to learn the similarities between documents. Furthermore, to avoid the trivial solutions that all candidates are partitioned to the same centroid, the reconstruction loss that minimizes the compressed error, and the posting list balance strategy that equally allocates the candidates, are integrated into the learning objective. Experiment results demonstrate that KDindex outperforms existing learnable quantization-based indexes and is 40× lighter than the state-of-the-art non-exhaustive methods while achieving comparable recall quality.
|
[
"Information Retrieval",
"Knowledge Distillation",
"Approximate Nearest Neighbor Search ",
"Maximum Inner Product Search ",
"High Dimensional Data",
"Model Compression"
] | |
ICML
| 2,024
| 37,078
|
Diffusion Models with Group Equivariance
|
In recent years, diffusion models have risen to prominence as the foremost technique for distribution learning. This paper focuses on structure-preserving diffusion models (SPDM), a subset of diffusion processes tailored to distributions with inherent structures, such as group symmetries. We complement existing sufficient conditions for constructing SPDM by proving complementary necessary ones. Additionally, we propose a new framework that considers the geometric structures affecting the diffusion process. Within this framework, we propose a method of preserving the alignment between endpoint couplings in bridge models to design a novel structure-preserving bridge model. We validate our findings over a variety of equivariant diffusion models by learning symmetric distributions and the transitions among them. Empirical studies on real-world medical images indicate that our models adhere to our theoretical framework, ensuring equivariance without compromising the quality of sampled images. Furthermore, we showcase the practical utility of our framework by achieving reliable equivariant image noise reduction and style transfer, irrespective of prior knowledge of image orientation, by implementing an equivariant denoising diffusion bridge model (DDBM).
|
[
"Generative Models",
"Equivariant Machine Learning",
"Geometric Deep Learning",
"Image Processing",
"Medical Imaging"
] | |
NeurIPS
| 2,023
| 71,309
|
$\textbf{A}^2\textbf{CiD}^2$: Accelerating Asynchronous Communication in Decentralized Deep Learning
|
Distributed training of Deep Learning models has been critical to many recent successes in the field. Current standard methods primarily rely on synchronous centralized algorithms which induce major communication bottlenecks and synchronization locks at scale. Decentralized asynchronous algorithms are emerging as a potential alternative but their practical applicability still lags. In order to mitigate the increase in communication cost that naturally comes with scaling the number of workers, we introduce a principled asynchronous, randomized, gossip-based optimization algorithm which works thanks to a continuous local momentum named $\textbf{A}^2\textbf{CiD}^2$. Our method allows each worker to continuously process mini-batches without stopping, and run a peer-to-peer averaging routine in parallel, reducing idle time. In addition to inducing a significant communication acceleration at no cost other than adding a local momentum variable, minimal adaptation is required to incorporate $\textbf{A}^2\textbf{CiD}^2$ to standard asynchronous approaches. Our theoretical analysis proves accelerated rates compared to previous asynchronous decentralized baselines and we empirically show that using our $\textbf{A}^2\textbf{CiD}^2$ momentum significantly decrease communication costs in poorly connected networks. In particular, we show consistent improvement on the ImageNet dataset using up to 64 asynchronous workers (A100 GPUs) and various communication network topologies.
|
[
"Distributed Deep Learning",
"Decentralized Algorithms",
"Asynchronous Communication",
"Optimization Algorithms",
"Parallel Computing"
] | |
NeurIPS
| 2,022
| 53,426
|
Reinforcement Learning in a Birth and Death Process: Breaking the Dependence on the State Space
|
In this paper, we revisit the regret of undiscounted reinforcement learning in MDPs with a birth and death structure. Specifically, we consider a controlled queue with impatient jobs and the main objective is to optimize a trade-off between energy consumption and user-perceived performance. Within this setting, the diameter $D$ of the MDP is $\Omega(S^S)$, where $S$ is the number of states. Therefore, the existing lower and upper bounds on the regret at time $T$, of order $O (\sqrt{DSAT})$ for MDPs with $S$ states and $A$ actions, may suggest that reinforcement learning is inefficient here. In our main result however, we exploit the structure of our MDPs to show that the regret of a slightly-tweaked version of the classical learning algorithm UCRL2 is in fact upper bounded by $\tilde{\mathcal{O}} (\sqrt{E_2AT})$ where $E_2$ is a weighted second moment of the stationary measure of a reference policy. Importantly, $E_2$ is bounded independently of $S$. Thus, our bound is asymptotically independent of the number of states and of the diameter. This result is based on a careful study of the number of visits performed by the learning algorithm to the states of the MDP, which is highly non-uniform.
|
[
"Reinforcement Learning",
"Markov Decision Processes ",
"Queueing Theory",
"Regret Analysis",
"Algorithmic Efficiency"
] | |
NeurIPS
| 2,022
| 58,551
|
Conditional Contrastive Networks
|
A vast amount of structured information associated with unstructured data, such as images or text, is stored online. This structured information implies different similarity relationships among unstructured data. Recently, contrastive learned embeddings trained on web-scraped unstructured data have been shown to have state-of-the-art performance across computer vision tasks. However, contrastive learning methods are currently able to leverage only a single metric of similarity. In this paper, we propose conditional contrastive networks (CCNs) as a way of using multiple notions of similarity in structured data. Our novel conditional contrastive loss is able to learn multiple disjoint similarity notions by projecting each similarity notion into a different subspace. We show empirically that our CCNs perform better than single-label trained cross-entropy networks, single-label trained supervised-contrastive networks, multi-task trained cross-entropy networks, and previously proposed conditional similarity networks on both the attributes on which it was trained and on unseen attributes.
|
[
"Computer Vision",
"Contrastive Learning",
"Representation Learning",
"Deep Learning"
] | |
ICLR
| 2,024
| 17,702
|
S$2$AC: Energy-Based Reinforcement Learning with Stein Soft Actor Critic
|
Learning expressive stochastic policies instead of deterministic ones has been proposed to achieve better stability, sample complexity and robustness. Notably, in Maximum Entropy reinforcement learning (MaxEnt RL), the policy is modeled as an expressive energy-based model (EBM) over the Q-values. However, this formulation requires the estimation of the entropy of such EBM distributions which is an open problem. To address this, previous MaxEnt RL methods either implicitly estimate the entropy, yielding high computational complexity and variance (SQL), or follow a variational inference approach that fits simplified distributions (e.g., Gaussian) for tractability (SAC). We propose Sein Soft Actor-Critic (S$^2$AC), a MaxEnt RL algorithm that learns expressive policies without compromising efficiency. S$^2$AC uses parameterized Stein Variational Gradient Descent (SVGD) as the underlying policy. At the core of S$^2$AC is a new solution to the above open challenge of entropy computation for EBMs. Our entropy formula is computationally efficient and only depends on first-order derivatives and vector products. Empirical results show that S$^2$AC yields more optimal solutions to the MaxEnt objective than SQL and SAC in the multi-goal environment, and outperforms SAC and SQL on the MuJoCo benchmark. Our code is available at: https://anonymous.4open.science/r/Stein-Soft-Actor-Critic/
|
[
"Reinforcement Learning",
"Energy-Based Models",
"Stochastic Policies",
"Variational Inference"
] | |
ICML
| 2,024
| 34,669
|
Interpreting and Improving Large Language Models in Arithmetic Calculation
|
Large language models (LLMs) have demonstrated remarkable potential across numerous applications and have shown an emergent ability to tackle complex reasoning tasks, such as mathematical computations. However, even for the simplest arithmetic calculations, the intrinsic mechanisms behind LLMs remains mysterious, making it challenging to ensure reliability. In this work, we delve into uncovering a specific mechanism by which LLMs execute calculations. Through comprehensive experiments, we find that LLMs frequently involve a small fraction (<5%) of attention heads, which play a pivotal role in focusing on operands and operators during calculation processes. Subsequently, the information from these operands is processed through multi-layer perceptrons (MLPs), progressively leading to the final solution. These pivotal heads/MLPs, though identified on a specific dataset, exhibit transferability across different datasets and even distinct tasks. This insight prompted us to investigate the potential benefits of selectively fine-tuning these essential heads/MLPs to boost the LLMs' computational performance. We empirically find that such precise tuning can yield notable enhancements on mathematical prowess, without compromising the performance on non-mathematical tasks. Our work serves as a preliminary exploration into the arithmetic calculation abilities inherent in LLMs, laying a solid foundation to reveal more intricate mathematical tasks.
|
[
"Natural Language Processing",
"Computational Linguistics",
"Mathematical Computation",
"Model Interpretability",
"Neural Networks"
] | |
ICML
| 2,024
| 33,096
|
How Spurious Features are Memorized: Precise Analysis for Random and NTK Features
|
Deep learning models are known to overfit and memorize spurious features in the training dataset. While numerous empirical studies have aimed at understanding this phenomenon, a rigorous theoretical framework to quantify it is still missing. In this paper, we consider spurious features that are uncorrelated with the learning task, and we provide a precise characterization of how they are memorized via two separate terms:(i)thestabilityof the model with respect to individual training samples, and(ii)thefeature alignmentbetween the spurious pattern and the full sample. While the first term is well established in learning theory and it is connected to the generalization error in classical work, the second one is, to the best of our knowledge, novel. Our key technical result gives a precise characterization of the feature alignment for the two prototypical settings of random features (RF) and neural tangent kernel (NTK) regression. We prove that the memorization of spurious features weakens as the generalization capability increases and, through the analysis of the feature alignment, we unveil the role of the model and of its activation function. Numerical experiments show the predictive power of our theory on standard datasets (MNIST, CIFAR-10).
|
[
"Machine Learning Theory",
"Deep Learning",
"Generalization and Overfitting",
"Neural Networks",
"Feature Analysis"
] | |
ICML
| 2,024
| 33,880
|
An Empirical Study of Realized GNN Expressiveness
|
Research on the theoretical expressiveness of Graph Neural Networks (GNNs) has developed rapidly, and many methods have been proposed to enhance the expressiveness. However, most methods do not have a uniform expressiveness measure except for a few that strictly follow the $k$-dimensional Weisfeiler-Lehman ($k$-WL) test hierarchy, leading to difficulties in quantitatively comparing their expressiveness. Previous research has attempted to use datasets for measurement, but facing problems with difficulty (any model surpassing 1-WL has nearly 100% accuracy), granularity (models tend to be either 100% correct or near random guess), and scale (only several essentially different graphs involved). To address these limitations, we study the realized expressive power that a practical model instance can achieve using a novel expressiveness dataset, BREC, which poses greater difficulty (with up to 4-WL-indistinguishable graphs), finer granularity (enabling comparison of models between 1-WL and 3-WL), a larger scale (consisting of 800 1-WL-indistinguishable graphs that are non-isomorphic to each other). We synthetically test 23 models with higher-than-1-WL expressiveness on BREC. Our experiment gives the first thorough measurement of the realized expressiveness of those state-of-the-art beyond-1-WL GNN models and reveals the gap between theoretical and realized expressiveness. Dataset and evaluation codes are released at: https://github.com/GraphPKU/BREC.
|
[
"Graph Neural Networks",
"Computational Graph Theory",
"Neural Network Expressiveness",
"Empirical Studies in AI"
] | |
NeurIPS
| 2,022
| 63,186
|
Intentional Dance Choreography with Semi-Supervised Recurrent VAEs
|
We summarize the model and results of PirouNet, a semi-supervised recurrent variational autoencoder. Given a set of dance sequences of which 1% include qualitative choreographic annotations, PirouNet conditionally generates dance sequences in the style and intention of the choreographer.
|
[
"Computer Vision",
"Computational Creativity",
"Dance Technology"
] | |
NeurIPS
| 2,023
| 70,378
|
Diff-Foley: Synchronized Video-to-Audio Synthesis with Latent Diffusion Models
|
The Video-to-Audio (V2A) model has recently gained attention for its practical application in generating audio directly from silent videos, particularly in video/film production. However, previous methods in V2A have limited generation quality in terms of temporal synchronization and audio-visual relevance. We present Diff-Foley, a synchronized Video-to-Audio synthesis method with a latent diffusion model (LDM) that generates high-quality audio with improved synchronization and audio-visual relevance. We adopt contrastive audio-visual pretraining (CAVP) to learn more temporally and semantically aligned features, then train an LDM with CAVP-aligned visual features on spectrogram latent space. The CAVP-aligned features enable LDM to capture the subtler audio-visual correlation via a cross-attention module. We further significantly improve sample quality with `double guidance'. Diff-Foley achieves state-of-the-art V2A performance on current large scale V2A dataset. Furthermore, we demonstrate Diff-Foley practical applicability and adaptability via customized downstream finetuning. Project Page: https://diff-foley.github.io/
|
[
"Audio-Visual Synthesis",
"Deep Learning",
"Latent Diffusion Models",
"Video Processing",
"Audio Processing",
"Multimedia Computing"
] | |
NeurIPS
| 2,023
| 71,897
|
Delayed Algorithms for Distributed Stochastic Weakly Convex Optimization
|
This paper studies delayed stochastic algorithms for weakly convex optimization in a distributed network with workers connected to a master node. Recently, Xu~et~al.~2022 showed that an inertial stochastic subgradient method converges at a rate of $\mathcal{O}(\tau_{\text{max}}/\sqrt{K})$ which depends on the maximum information delay $\tau_{\text{max}}$. In this work, we show that the delayed stochastic subgradient method ($\texttt{DSGD}$) obtains a tighter convergence rate which depends on the expected delay $\bar{\tau}$. Furthermore, for an important class of composition weakly convex problems, we develop a new delayed stochastic prox-linear ($\texttt{DSPL}$) method in which the delays only affect the high-order term in the rate and hence, are negligible after a certain number of $\texttt{DSPL}$ iterations. In addition, we demonstrate the robustness of our proposed algorithms against arbitrary delays. By incorporating a simple safeguarding step in both methods, we achieve convergence rates that depend solely on the number of workers, eliminating the effect of delays. Our numerical experiments further confirm the empirical superiority of our proposed methods.
|
[
"Distributed Optimization",
"Stochastic Optimization",
"Convex Optimization",
"Algorithm Design",
"Numerical Analysis"
] | |
ICML
| 2,024
| 33,478
|
Asymptotically Optimal and Computationally Efficient Average Treatment Effect Estimation in A/B testing
|
Motivated by practical applications in clinical trials and online platforms, we study A/B testing with the aim of estimating a confidence interval (CI) for the average treatment effect (ATE) using the minimum expected sample size. This CI should have a width at most $\epsilon$ while ensuring that the probability of the CI not containing the true ATE is at most $\delta$. To answer this, we first establish a lower bound on the expected sample size needed for any adaptive policy which constructs a CI of ATE with desired properties. Specifically, we prove that the lower bound is based on the solution to a max-min non-convex optimization problem for small $\delta$. Tailoring the ``plug-in'' approach for the ATE problem, we construct an adaptive policy that is asymptotically optimal, i.e., matches the lower bound on the expected sample size for small $\delta$. Interestingly, we find that, for small $\epsilon$ and $\delta$, the asymptotically optimal fraction of treatment assignment for A and B is proportional to the standard deviation of the outcome distributions of treatments A and B, respectively. However, as the proposed approach can be computationally intensive, we propose an alternative adaptive policy. This new policy, informed by insights from our lower bound analysis, is computationally efficient while remaining asymptotically optimal for small values of $\epsilon$ and $\delta$. Numerical comparisons demonstrate that both policies perform similarly across practical values of $\epsilon$ and $\delta$, offering efficient solutions for A/B testing.
|
[
"Statistics",
"Experimental Design",
"Clinical Trials",
"Online Experimentation",
"Optimization"
] | |
ICML
| 2,024
| 35,016
|
LEVI: Generalizable Fine-tuning via Layer-wise Ensemble of Different Views
|
Fine-tuning is becoming widely used for leveraging the power of pre-trained foundation models in new downstream tasks. While there are many successes of fine-tuning on various tasks, recent studies have observed challenges in the generalization of fine-tuned models to unseen distributions (i.e., out-of-distribution; OOD). To improve OOD generalization, some previous studies identify the limitations of fine-tuning data and regulate fine-tuning to preserve the general representation learned from pre-training data. However, potential limitations in the pre-training data and models are often ignored. In this paper, we contend that overly relying on the pre-trained representation may hinder fine-tuning from learning essential representations for downstream tasks and thus hurt its OOD generalization. It can be especially catastrophic when new tasks are from different (sub)domains compared to pre-training data. To address the issues in both pre-training and fine-tuning data, we propose a novel generalizable fine-tuning method LEVI (Layer-wiseEnsemble of differentVIews), where the pre-trained model is adaptively ensembled layer-wise with a small task-specific model, while preserving its efficiencies. By combining two complementing models, LEVI effectively suppresses problematic features in both the fine-tuning data and pre-trained model and preserves useful features for new tasks. Broad experiments with large language and vision models show that LEVI greatly improves fine-tuning generalization via emphasizing different views from fine-tuning data and pre-trained features.
|
[
"Transfer Learning",
"Model Fine-tuning",
"Out-of-Distribution Generalization",
"Ensemble Methods",
"Representation Learning"
] | |
NeurIPS
| 2,023
| 73,626
|
MLFMF: Data Sets for Machine Learning for Mathematical Formalization
|
We introduce MLFMF, a collection of data sets for benchmarking recommendation systems used to support formalization of mathematics with proof assistants. These systems help humans identify which previous entries (theorems, constructions, datatypes, and postulates) are relevant in proving a new theorem or carrying out a new construction. Each data set is derived from a library of formalized mathematics written in proof assistants Agda or Lean. The collection includes the largest Lean 4 library Mathlib, and some of the largest Agda libraries: the standard library, the library of univalent mathematics Agda-unimath, and the TypeTopology library. Each data set represents the corresponding library in two ways: as a heterogeneous network, and as a list of s-expressions representing the syntax trees of all the entries in the library. The network contains the (modular) structure of the library and the references between entries, while the s-expressions give complete and easily parsed information about every entry.We report baseline results using standard graph and word embeddings, tree ensembles, and instance-based learning algorithms. The MLFMF data sets provide solid benchmarking support for further investigation of the numerous machine learning approaches to formalized mathematics. The methodology used to extract the networks and the s-expressions readily applies to other libraries, and is applicable to other proof assistants. With more than $250\,000$ entries in total, this is currently the largest collection of formalized mathematical knowledge in machine learnable format.
|
[
"Mathematical Formalization",
"Proof Assistants",
"Data Sets",
"Recommender Systems",
"Formalized Mathematics",
"Computational Mathematics"
] | |
ICLR
| 2,023
| 11,608
|
Learning to Decompose Visual Features with Latent Textual Prompts
|
Recent advances in pre-training vision-language models like CLIP have shown great potential in learning transferable visual representations. Nonetheless, for downstream inference, CLIP-like models suffer from either 1) degraded accuracy and robustness in the case of inaccurate text descriptions during retrieval-based inference (the challenge for zero-shot protocol); or 2) breaking the well-established vision-language alignment (the challenge for linear probing). To address them, we propose Decomposed Feature Prompting (DeFo). DeFo leverages a flexible number of learnable embeddings as textual input while maintaining the vision-language dual-model architecture, which enables the model to learn decomposed visual features with the help of feature-level textual prompts. We further use an additional linear layer to perform classification, allowing a scalable size of language inputs. Our empirical study shows DeFo's significance in improving the vision-language models. For example, DeFo obtains 73.2% test accuracy on ImageNet with a ResNet-50 backbone without tuning any pretrained weights of both the vision and language encoder, outperforming zero-shot CLIP by a large margin of 15.0%, and outperforming state-of-the-art vision-language prompt tuning method by 7.6%.
|
[
"Computer Vision",
"Vision-Language Models",
"Natural Language Processing",
"Transfer Learning",
"Representation Learning"
] | |
NeurIPS
| 2,022
| 58,554
|
Self-supervised Representation Learning Across Sequential and Tabular Features Using Transformers
|
Machine learning models used for predictive modeling tasks spanning across personalization, recommender systems, ad response prediction, fraud detection etc. typically require a variety of tabular as well as sequential activity features about the user. For tasks like click-through or conversion (purchase) rate prediction where labeled data is available at scale, popular methods use deep sequence models (sometimes pre-trained) to encode sequential inputs, followed by concatenation with tabular features and optimization of a supervised training objective. For tasks like bot and fraud detection, where labeled data is sparse and incomplete, the typical approach is to use self-supervision to learn user embeddings from their historical activity sequence. However, these models are not equipped to handle tabular input features during self-supervised learning. In this paper, we propose a novel Transformer architecture that can jointly learn embeddings on both sequential and tabular input features. Our model learns self-supervised user embeddings using masked token prediction objective on a rich variety of features without relying on any labeled data. We demonstrate that user embeddings generated by the proposed technique are able to successfully encode information from a combination of sequential and tabular features, improving AUC-ROC for linear separability for a downstream task label by $5\%$ over embeddings generated using sequential features only. We also benchmark the efficacy of the embeddings on the bot detection task for a large-scale digital advertising program, where the proposed model improves recall over known bots by $10\%$ over the sequential only baseline at the same False Positive Rate (FPR).
|
[
"Self-supervised Learning",
"Representation Learning",
"Transformers",
"Sequential Data",
"Tabular Data",
"Recommender Systems",
"Fraud Detection",
"Predictive Modeling"
] | |
ICML
| 2,024
| 37,071
|
Improving Flow Matching for Posterior Inference with Physics-based Controls
|
Flow-based generative modeling is a powerful tool for solving inverse problems in physical sciences that can be used for sampling and likelihood evaluation with much lower inference times than traditional methods. We propose to refine flows with additional control signals based on an underlying physics model. In our experiments, this control signal is represented by gradients with respect to a differentiable cost function. We train a neural network to aggregate a pretrained flow and physics-based control signal to yield a hybrid update. We evaluate the refinements against classical MCMC methods for modeling strong gravitational lens systems, a challenging inverse problem in astronomy. We demonstrate that including physics-based controls improves the accuracy by $57$%, making them competitive with MCMC methods while being 12x to 83x faster for inference.
|
[
"Inverse Problems",
"Physics-based Modeling",
"Generative Models",
"Computational Physics",
"Astronomy",
"Bayesian Inference"
] | |
ICML
| 2,023
| 24,678
|
Near-Minimax-Optimal Risk-Sensitive Reinforcement Learning with CVaR
|
In this paper, we study risk-sensitive Reinforcement Learning (RL), focusing on the objective of Conditional Value at Risk (CVaR) with risk tolerance $\tau$. Starting with multi-arm bandits (MABs), we show the minimax CVaR regret rate is $\Omega(\sqrt{\tau^{-1}AK})$, where $A$ is the number of actions and $K$ is the number of episodes, and that it is achieved by an Upper Confidence Bound algorithm with a novel Bernstein bonus. For online RL in tabular Markov Decision Processes (MDPs), we show a minimax regret lower bound of $\Omega(\sqrt{\tau^{-1}SAK})$ (with normalized cumulative rewards), where $S$ is the number of states, and we propose a novel bonus-driven Value Iteration procedure. We show that our algorithm achieves the optimal regret of $\widetilde O(\sqrt{\tau^{-1}SAK})$ under a continuity assumption and in general attains a near-optimal regret of $\widetilde O(\tau^{-1}\sqrt{SAK})$, which is minimax-optimal for constant $\tau$. This improves on the best available bounds. By discretizing rewards appropriately, our algorithms are computationally efficient.
|
[
"Reinforcement Learning",
"Risk-Sensitive Optimization",
"Multi-Armed Bandits",
"Markov Decision Processes",
"Computational Efficiency in Algorithms"
] | |
NeurIPS
| 2,023
| 76,857
|
Can Deep Learning help to forecast deforestation in the Amazonian Rainforest?
|
Deforestation is a major driver of climate change. To mitigate deforestation, carbon offset projects aim to protect forest areas at risk. However, existing literature shows that most projects have substantially overestimated the risk of deforestation, thereby issuing carbon credits without equivalent emissions reductions. In this study, we examine if the spread of deforestation can be predicted ex-ante using Deep Learning (DL) models. Our input data includes past deforestation development, slope information, land use, and other terrain- and soil-specific covariates. Testing predictions 1-year ahead, we find that our models only achieve low levels of predictability. For pixel-wise classification at a 30 m resolution, our models achieve an F1 score of 0.263. Only when substantially simplifying the task to predicting if any level of deforestation occurs within a 1.5 km squared tile, the model results improve to a moderate performance (F1: 0.608). We conclude that, based on our input data, deforestation cannot be predicted accurately enough to justify the ex-ante issuance of carbon credits for forest conservation projects. As main challenges, there is the extreme class imbalance between pixels that are deforested (minority) and not deforested (majority) as well as the omittance of social, political, and economic drivers of deforestation.
|
[
"Environmental Science",
"Climate Change",
"Remote Sensing",
"Conservation Science"
] | |
ICLR
| 2,024
| 17,635
|
LRM: Large Reconstruction Model for Single Image to 3D
|
We propose the first Large Reconstruction Model (LRM) that predicts the 3D model of an object from a single input image within just 5 seconds. In contrast to many previous methods that are trained on small-scale datasets such as ShapeNet in a category-specific fashion, LRM adopts a highly scalable transformer-based architecture with 500 million learnable parameters to directly predict a neural radiance field (NeRF) from the input image. We train our model in an end-to-end manner on massive multi-view data containing around 1 million objects, including both synthetic renderings from Objaverse and real captures from MVImgNet. This combination of a high-capacity model and large-scale training data empowers our model to be highly generalizable and produce high-quality 3D reconstructions from various testing inputs, including real-world in-the-wild captures and images created by generative models. Video demos and interactable 3D meshes can be found on our LRM project webpage: https://yiconghong.me/LRM.
|
[
"Computer Vision",
"3D Reconstruction",
"Neural Networks",
"Deep Learning",
"Image Processing",
"Neural Radiance Fields ",
"Transformer Models"
] | |
ICLR
| 2,022
| 6,865
|
Shallow and Deep Networks are Near-Optimal Approximators of Korobov Functions
|
In this paper, we analyze the number of neurons and training parameters that a neural network needs to approximate multivariate functions of bounded second mixed derivatives --- Korobov functions. We prove upper bounds on these quantities for shallow and deep neural networks, drastically lessening the curse of dimensionality. Our bounds hold for general activation functions, including ReLU. We further prove that these bounds nearly match the minimal number of parameters any continuous function approximator needs to approximate Korobov functions, showing that neural networks are near-optimal function approximators.
|
[
"Neural Networks",
"Function Approximation",
"Computational Mathematics"
] | |
NeurIPS
| 2,023
| 70,904
|
Learning Domain-Aware Detection Head with Prompt Tuning
|
Domain adaptive object detection (DAOD) aims to generalize detectors trained on an annotated source domain to an unlabelled target domain. However, existing methods focus on reducing the domain bias of the detection backbone by inferring a discriminative visual encoder, while ignoring the domain bias in the detection head. Inspired by the high generalization of vision-language models (VLMs), applying a VLM as the robust detection backbone following a domain-aware detection head is a reasonable way to learn the discriminative detector for each domain, rather than reducing the domain bias in traditional methods. To achieve the above issue, we thus propose a novel DAOD framework named Domain-Aware detection head with Prompt tuning (DA-Pro), which applies the learnable domain-adaptive prompt to generate the dynamic detection head for each domain. Formally, the domain-adaptive prompt consists of the domain-invariant tokens, domain-specific tokens, and the domain-related textual description along with the class label. Furthermore, two constraints between the source and target domains are applied to ensure that the domain-adaptive prompt can capture the domains-shared and domain-specific knowledge. A prompt ensemble strategy is also proposed to reduce the effect of prompt disturbance. Comprehensive experiments over multiple cross-domain adaptation tasks demonstrate that using the domain-adaptive prompt can produce an effectively domain-related detection head for boosting domain-adaptive object detection. Our code is available at https://github.com/Therock90421/DA-Pro.
|
[
"Computer Vision",
"Domain Adaptation",
"Object Detection",
"Vision-Language Models"
] | |
ICLR
| 2,024
| 17,527
|
Learning Multi-Agent Communication with Contrastive Learning
|
Communication is a powerful tool for coordination in multi-agent RL. But inducing an effective, common language is a difficult challenge, particularly in the decentralized setting. In this work, we introduce an alternative perspective where communicative messages sent between agents are considered as different incomplete views of the environment state. By examining the relationship between messages sent and received, we propose to learn to communicate using contrastive learning to maximize the mutual information between messages of a given trajectory. In communication-essential environments, our method outperforms previous work in both performance and learning speed. Using qualitative metrics and representation probing, we show that our method induces more symmetric communication and captures global state information from the environment. Overall, we show the power of contrastive learning and the importance of leveraging messages as encodings for effective communication.
|
[
"Multi-Agent Reinforcement Learning",
"Contrastive Learning",
"Machine Learning Communication",
"Decentralized Systems"
] | |
ICLR
| 2,023
| 11,078
|
SP2 : A Second Order Stochastic Polyak Method
|
Recently the SP (Stochastic Polyak step size) method has emerged as a competitive adaptive method for setting the step sizes of SGD. SP can be interpreted as a method specialized to interpolated models, since it solves the interpolation equations. SP solves these equation by using local linearizations of the model. We take a step further and develop a method for solving the interpolation equations that uses the local second-order approximation of the model. Our resulting method SP2 uses Hessian-vector products to speed-up the convergence of SP. Furthermore, and rather uniquely among second-order methods, the design of SP2 in no way relies on positive definite Hessian matrices or convexity of the objective function. We show SP2 is competitive both in experiments and in theory. We show SP2 is very competitive on matrix completion, non-convex test problems and logistic regression. We also provide a convergence theory on sums-of-quadratics.
|
[
"Optimization",
"Numerical Analysis",
"Stochastic Methods",
"Second-Order Methods"
] | |
NeurIPS
| 2,022
| 53,053
|
MoCoDA: Model-based Counterfactual Data Augmentation
|
The number of states in a dynamic process is exponential in the number of objects, making reinforcement learning (RL) difficult in complex, multi-object domains. For agents to scale to the real world, they will need to react to and reason about unseen combinations of objects. We argue that the ability to recognize and use local factorization in transition dynamics is a key element in unlocking the power of multi-object reasoning. To this end, we show that (1) known local structure in the environment transitions is sufficient for an exponential reduction in the sample complexity of training a dynamics model, and (2) a locally factored dynamics model provably generalizes out-of-distribution to unseen states and actions. Knowing the local structure also allows us to predict which unseen states and actions this dynamics model will generalize to. We propose to leverage these observations in a novel Model-based Counterfactual Data Augmentation (MoCoDA) framework. MoCoDA applies a learned locally factored dynamics model to an augmented distribution of states and actions to generate counterfactual transitions for RL. MoCoDA works with a broader set of local structures than prior work and allows for direct control over the augmented training distribution. We show that MoCoDA enables RL agents to learn policies that generalize to unseen states and actions. We use MoCoDA to train an offline RL agent to solve an out-of-distribution robotics manipulation task on which standard offline RL algorithms fail.
|
[
"Reinforcement Learning",
"Robotics",
"Data Augmentation",
"Model-based Learning"
] | |
ICML
| 2,023
| 23,600
|
Simplified Temporal Consistency Reinforcement Learning
|
Reinforcement learning (RL) is able to solve complex sequential decision-making tasks but is currently limited by sample efficiency and required computation. To improve sample efficiency, recent work focuses on model-based RL which interleaves model learning with planning. Recent methods further utilize policy learning, value estimation, and, self-supervised learning as auxiliary objectives. In this paper we show that, surprisingly, a simple representation learning approach relying only on a latent dynamics model trained by latent temporal consistency is sufficient for high-performance RL. This applies when using pure planning with a dynamics model conditioned on the representation, but, also when utilizing the representation as policy and value function features in model-free RL. In experiments, our approach learns an accurate dynamics model to solve challenging high-dimensional locomotion tasks with online planners while being 4.1$\times$ faster to train compared to ensemble-based methods. With model-free RL without planning, especially on high-dimensional tasks, such as the Deepmind Control Suite Humanoid and Dog tasks, our approach outperforms model-free methods by a large margin and matches model-based methods' sample efficiency while training 2.4$\times$ faster.
|
[
"Reinforcement Learning",
"Model-Based Reinforcement Learning",
"Model-Free Reinforcement Learning",
"Representation Learning"
] | |
ICLR
| 2,022
| 6,849
|
Understanding over-squashing and bottlenecks on graphs via curvature
|
Most graph neural networks (GNNs) use the message passing paradigm, in which node features are propagated on the input graph. Recent works pointed to the distortion of information flowing from distant nodes as a factor limiting the efficiency of message passing for tasks relying on long-distance interactions. This phenomenon, referred to as 'over-squashing', has been heuristically attributed to graph bottlenecks where the number of $k$-hop neighbors grows rapidly with $k$. We provide a precise description of the over-squashing phenomenon in GNNs and analyze how it arises from bottlenecks in the graph. For this purpose, we introduce a new edge-based combinatorial curvature and prove that negatively curved edges are responsible for the over-squashing issue. We also propose and experimentally test a curvature-based graph rewiring method to alleviate the over-squashing.
|
[
"Graph Neural Networks",
"Graph Theory",
"Network Analysis",
"Computational Geometry"
] | |
NeurIPS
| 2,023
| 71,807
|
Practical Differentially Private Hyperparameter Tuning with Subsampling
|
Tuning the hyperparameters of differentially private (DP) machine learning (ML) algorithms often requires use of sensitive data and this may leak private information via hyperparameter values. Recently, Papernot and Steinke (2022) proposed a certain class of DP hyperparameter tuning algorithms, where the number of random search samples is randomized. Commonly, these algorithms still considerably increase the DP privacy parameter $\varepsilon$ over non-tuned DP ML model training and can be computationally heavy as evaluating each hyperparameter candidate requires a new training run. We focus on lowering both the DP bounds and the compute cost of these methods by using only a random subset of the sensitive data for the hyperparameter tuning and by appropriately extrapolating the optimal values to a larger dataset. We carry out a Rényi differential privacy analysis for the proposed method and experimentally show that it consistently leads to better privacy-utility trade-off than the baseline method by Papernot and Steinke.
|
[
"Differential Privacy",
"Hyperparameter Tuning",
"Data Privacy",
"Computational Efficiency"
] | |
NeurIPS
| 2,022
| 63,068
|
Equivariant Graph Hierarchy-based Neural Networks
|
Equivariant Graph neural Networks (EGNs) are powerful in characterizing the dynamics of multi-body physical systems. Existing EGNs conduct flat message passing, which, yet, is unable to capture the spatial/dynamical hierarchy for complex systems particularly, limiting substructure discovery and global information fusion. In this paper, we propose Equivariant Hierarchy-based Graph Networks (EGHNs) which consist of the three key components: generalized Equivariant Matrix Message Passing (EMMP), E-Pool, and E-UnPool. In particular, EMMP is able to improve the expressivity of conventional equivariant message passing, E-Pool assigns the quantities of the low-level nodes into high-level clusters, while E-UnPool leverages the high-level information to update the dynamics of the low-level nodes. As their names imply, both E-Pool and E-UnPool are guaranteed to be equivariant to meet physic symmetry. Considerable experimental evaluations verify the effectiveness of our EGHN on several applications including multi-object dynamics simulation, motion capture, and protein dynamics modeling.
|
[
"Graph Neural Networks",
"Computational Physics",
"Multi-body Dynamics",
"Protein Dynamics Modeling"
] | |
NeurIPS
| 2,023
| 72,181
|
Training Fully Connected Neural Networks is $\exists\mathbb{R}$-Complete
|
We consider the algorithmic problem of finding the optimal weights and biases for a two-layer fully connected neural network to fit a given set of data points, also known as empirical risk minimization. We show that the problem is $\exists\mathbb{R}$-complete. This complexity class can be defined as the set of algorithmic problems that are polynomial-time equivalent to finding real roots of a multivariate polynomial with integer coefficients. Furthermore, we show that arbitrary algebraic numbers are required as weights to be able to train some instances to optimality, even if all data points are rational. Our result already applies to fully connected instances with two inputs, two outputs, and one hidden layer of ReLU neurons. Thereby, we strengthen a result by Abrahamsen, Kleist and Miltzow [NeurIPS 2021]. A consequence of this is that a combinatorial search algorithm like the one by Arora, Basu, Mianjy and Mukherjee [ICLR 2018] is impossible for networks with more than one output dimension, unless $\text{NP} = \exists\mathbb{R}$.
|
[
"Computational Complexity",
"Neural Networks",
"Machine Learning Theory",
"Algorithmic Complexity"
] | |
ICML
| 2,024
| 35,017
|
Offline Imitation from Observation via Primal Wasserstein State Occupancy Matching
|
In real-world scenarios, arbitrary interactions with the environment can often be costly, and actions of expert demonstrations are not always available. To reduce the need for both, offline Learning from Observations (LfO) is extensively studied: the agent learns to solve a task given only expert states and task-agnostic non-expert state-action pairs. The state-of-the-art DIstribution Correction Estimation (DICE) methods, as exemplified by SMODICE, minimize the state occupancy divergence between the learner's and empirical expert policies. However, such methods are limited to either $f$-divergences (KL and $\chi^2$) or Wasserstein distance with Rubinstein duality, the latter of which constrains the underlying distance metric crucial to the performance of Wasserstein-based solutions. To enable more flexible distance metrics, we propose Primal Wasserstein DICE (PW-DICE). It minimizes the primal Wasserstein distance between the learner and expert state occupancies and leverages a contrastively learned distance metric. Theoretically, our framework is a generalization of SMODICE, and is the first work that unifies $f$-divergence and Wasserstein minimization. Empirically, we find that PW-DICE improves upon several state-of-the-art methods. The code is available at https://github.com/KaiYan289/PW-DICE.
|
[
"Reinforcement Learning",
"Imitation Learning",
"Offline Learning",
"Learning from Observations ",
"Optimal Transport",
"Wasserstein Distance"
] | |
NeurIPS
| 2,023
| 72,564
|
Tuning Multi-mode Token-level Prompt Alignment across Modalities
|
Advancements in prompt tuning of vision-language models have underscored their potential in enhancing open-world visual concept comprehension. However, prior works only primarily focus on single-mode (only one prompt for each modality) and holistic level (image or sentence) semantic alignment, which fails to capture the sample diversity, leading to sub-optimal prompt discovery. To address the limitation, we propose a multi-mode token-level tuning framework that leverages the optimal transportation to learn and align a set of prompt tokens across modalities. Specifically, we rely on two essential factors: 1) multi-mode prompts discovery, which guarantees diverse semantic representations, and 2) token-level alignment, which helps explore fine-grained similarity. Consequently, the similarity can be calculated as a hierarchical transportation problem between the modality-specific sets. Extensive experiments on popular image recognition benchmarks show the superior generalization and few-shot abilities of our approach. The qualitative analysis demonstrates that the learned prompt tokens have the ability to capture diverse visual concepts.
|
[
"Vision-Language Models",
"Multi-modal Learning",
"Prompt Tuning",
"Semantic Alignment",
"Image Recognition",
"Few-shot Learning"
] | |
NeurIPS
| 2,023
| 73,485
|
Mind2Web: Towards a Generalist Agent for the Web
|
We introduce Mind2Web, the first dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex tasks on any website. Existing datasets for web agents either use simulated websites or only cover a limited set of websites and tasks, thus not suitable for generalist web agents. With over 2,000 open-ended tasks collected from 137 websites spanning 31 domains and crowdsourced action sequences for the tasks, Mind2Web provides three necessary ingredients for building generalist web agents: 1) diverse domains, websites, and tasks, 2) use of real-world websites instead of simulated and simplified ones, and 3) a broad spectrum of user interaction patterns. Based on Mind2Web, we conduct an initial exploration of using large language models (LLMs) for building generalist web agents. While the raw HTML of real-world websites are often too large to be fed to LLMs, we show that first filtering it with a small LM significantly improves the effectiveness and efficiency of LLMs. Our solution demonstrates a decent level of performance, even on websites or entire domains the model has never seen before, but there is still a substantial room to improve towards truly generalizable agents. We open-source our dataset, model implementation, and trained models (https://osu-nlp-group.github.io/Mind2Web) to facilitate further research on building a generalist agent for the web.
|
[
"Natural Language Processing",
"Human-Computer Interaction",
"Web Technologies"
] | |
ICML
| 2,022
| 17,297
|
Interactive Inverse Reinforcement Learning for Cooperative Games
|
We study the problem of designing autonomous agents that can learn to cooperate effectively with a potentially suboptimal partner while having no access to the joint reward function. This problem is modeled as a cooperative episodic two-agent Markov decision process. We assume control over only the first of the two agents in a Stackelberg formulation of the game, where the second agent is acting so as to maximise expected utility given the first agent's policy. How should the first agent act in order to learn the joint reward function as quickly as possible and so that the joint policy is as close to optimal as possible? We analyse how knowledge about the reward function can be gained in this interactive two-agent scenario. We show that when the learning agent's policies have a significant effect on the transition function, the reward function can be learned efficiently.
|
[
"Reinforcement Learning",
"Multi-Agent Systems",
"Game Theory"
] | |
ICLR
| 2,024
| 17,638
|
Large Language Models Are Not Robust Multiple Choice Selectors
|
Multiple choice questions (MCQs) serve as a common yet important task format in the evaluation of large language models (LLMs). This work shows that modern LLMs are vulnerable to option position changes in MCQs due to their inherent “selection bias”, namely, they prefer to select specific option IDs as answers (like “Option A”). Through extensive empirical analyses with 20 LLMs on three benchmarks, we pinpoint that this behavioral bias primarily stems from LLMs’ token bias, where the model a priori assigns more probabilistic mass to specific option ID tokens (e.g., A/B/C/D) when predicting answers from the option IDs. To mitigate selection bias, we propose a label-free, inference-time debiasing method, called PriDe, which separates the model’s prior bias for option IDs from the overall prediction distribution. PriDe first estimates the prior by permutating option contents on a small number of test samples, and then applies the estimated prior to debias the remaining samples. We demonstrate that it achieves interpretable and transferable debiasing with high computational efficiency. We hope this work can draw broader research attention to the bias and robustness of modern LLMs.
|
[
"Natural Language Processing",
"Model Robustness",
"Bias in AI Models"
] | |
NeurIPS
| 2,023
| 70,582
|
Nominality Score Conditioned Time Series Anomaly Detection by Point/Sequential Reconstruction
|
Time series anomaly detection is challenging due to the complexity and variety of patterns that can occur. One major difficulty arises from modeling time-dependent relationships to find contextual anomalies while maintaining detection accuracy for point anomalies. In this paper, we propose a framework for unsupervised time series anomaly detection that utilizes point-based and sequence-based reconstruction models. The point-based model attempts to quantify point anomalies, and the sequence-based model attempts to quantify both point and contextual anomalies. Under the formulation that the observed time point is a two-stage deviated value from a nominal time point, we introduce a nominality score calculated from the ratio of a combined value of the reconstruction errors. We derive an induced anomaly score by further integrating the nominality score and anomaly score, then theoretically prove the superiority of the induced anomaly score over the original anomaly score under certain conditions. Extensive studies conducted on several public datasets show that the proposed framework outperforms most state-of-the-art baselines for time series anomaly detection.
|
[
"Time Series Analysis",
"Anomaly Detection",
"Unsupervised Learning",
"Data Science"
] | |
ICML
| 2,024
| 35,115
|
Adaptive Horizon Actor-Critic for Policy Learning in Contact-Rich Differentiable Simulation
|
Model-Free Reinforcement Learning (MFRL), leveraging the policy gradient theorem, has demonstrated considerable success in continuous control tasks. However, these approaches are plagued by high gradient variance due to zeroth-order gradient estimation, resulting in suboptimal policies. Conversely, First-Order Model-Based Reinforcement Learning (FO-MBRL) methods employing differentiable simulation provide gradients with reduced variance but are susceptible to sampling error in scenarios involving stiff dynamics, such as physical contact. This paper investigates the source of this error and introduces Adaptive Horizon Actor-Critic (AHAC), an FO-MBRL algorithm that reduces gradient error by adapting the model-based horizon to avoid stiff dynamics. Empirical findings reveal that AHAC outperforms MFRL baselines, attaining 40% more reward across a set of locomotion tasks and efficiently scaling to high-dimensional control environments with improved wall-clock-time efficiency.adaptive-horizon-actor-critic.github.io
|
[
"Reinforcement Learning",
"Model-Based Reinforcement Learning",
"Continuous Control",
"Differentiable Simulation",
"Robotics and Control Systems"
] | |
ICML
| 2,022
| 17,195
|
A Functional Information Perspective on Model Interpretation
|
Contemporary predictive models are hard to interpret as their deep nets exploit numerous complex relations between input elements. This work suggests a theoretical framework for model interpretability by measuring the contribution of relevant features to the functional entropy of the network with respect to the input. We rely on the log-Sobolev inequality that bounds the functional entropy by the functional Fisher information with respect to the covariance of the data. This provides a principled way to measure the amount of information contribution of a subset of features to the decision function. Through extensive experiments, we show that our method surpasses existing interpretability sampling-based methods on various data signals such as image, text, and audio.
|
[
"Machine Learning Interpretability",
"Information Theory",
"Deep Learning",
"Theoretical Computer Science"
] | |
ICLR
| 2,024
| 18,109
|
FedWon: Triumphing Multi-domain Federated Learning Without Normalization
|
Federated learning (FL) enhances data privacy with collaborative in-situ training on decentralized clients. Nevertheless, FL encounters challenges due to non-independent and identically distributed (non-i.i.d) data, leading to potential performance degradation and hindered convergence. While prior studies predominantly addressed the issue of skewed label distribution, our research addresses a crucial yet frequently overlooked problem known as multi-domain FL. In this scenario, clients' data originate from diverse domains with distinct feature distributions, instead of label distributions. To address the multi-domain problem in FL, we propose a novel method called Federated Learning Without Normalizations (FedWon). FedWon draws inspiration from the observation that batch normalization (BN) faces challenges in effectively modeling the statistics of multiple domains, while existing normalization techniques possess their own limitations. In order to address these issues, FedWon eliminates the normalization layers in FL and reparameterizes convolution layers with scaled weight standardization. Through extensive experimentation on five datasets and five models, our comprehensive experimental results demonstrate that FedWon surpasses both FedAvg and the current state-of-the-art method (FedBN) across all experimental setups, achieving notable accuracy improvements of more than 10% in certain domains. Furthermore, FedWon is versatile for both cross-silo and cross-device FL, exhibiting robust domain generalization capability, showcasing strong performance even with a batch size as small as 1, thereby catering to resource-constrained devices. Additionally, FedWon can also effectively tackle the challenge of skewed label distribution.
|
[
"Federated Learning",
"Data Privacy",
"Multi-domain Learning",
"Distributed Systems"
] | |
ICML
| 2,024
| 33,862
|
Criterion Collapse and Loss Distribution Control
|
In this work, we consider the notion of "criterion collapse," in which optimization of one metric implies optimality in another, with a particular focus on conditions for collapse into error probability minimizers under a wide variety of learning criteria, ranging from DRO and OCE risks (CVaR, tilted ERM) to non-monotonic criteria underlying recent ascent-descent algorithms explored in the literature (Flooding, SoftAD). We show how collapse in the context of losses with a Bernoulli distribution goes far beyond existing results for CVaR and DRO, then expand our scope to include surrogate losses, showing conditions where monotonic criteria such as tilted ERM cannot avoid collapse, whereas non-monotonic alternatives can.
|
[
"Optimization",
"Risk Management",
"Statistical Learning Theory"
] | |
NeurIPS
| 2,023
| 74,159
|
Reproducibility Study of ”Label-Free Explainability for Unsupervised Models”
|
In this work, we present our reproducibility study of "Label-Free Explainability for Unsupervised Models", a paper that introduces two post‐hoc explanation techniques for neural networks: (1) label‐free feature importance and (2) label‐free example importance. Our study focuses on the reproducibility of the authors’ most important claims: (i) perturbing features with the highest importance scores causes higherlatent shift than perturbing random pixels, (ii) label‐free example importance scores help to identify training examples that are highly related to a given test example, (iii) unsupervised models trained on different tasks show moderate correlation among the highest scored features and (iv) low correlation in example scores measured on a fixed set of data points, and (v) increasing the disentanglement with β in a β‐VAE does not imply that latent units will focus on more different features. We reviewed the authors’ code, checked if the implementation of experiments matched with the paper, and also ran all experiments. The results are shown to be reproducible. Moreover, we extended the codebase in order to run the experiments on more datasets, and to test the claims with other experiments.
|
[
"Explainable AI",
"Reproducibility Studies",
"Neural Networks",
"Unsupervised Learning"
] | |
NeurIPS
| 2,022
| 57,459
|
Boosting Multi-modal Contrastive Learning with Modern Hopfield Networks and InfoLOOB
|
CLIP yielded impressive results on zero-shot transfer learning tasks and is considered as a foundation model like BERT or GPT3. CLIP vision models that have a rich representation are pre-trained using the InfoNCE objective and natural language supervision before they are fine-tuned on particular tasks. Though CLIP excels at zero-shot transfer learning, it suffers from an explaining away problem, that is, it focuses on one or few features, while neglecting other relevant features. This problem is caused by insufficiently extracting the covariance structure in the original multi-modal data. We suggest to use modern Hopfield networks to tackle the problem of explaining away. Their retrieved embeddings have an enriched covariance structure derived from co-occurrences of features in the stored embeddings. However, modern Hopfield networks increase the saturation effect of the InfoNCE objective which hampers learning. We propose to use the InfoLOOB objective to mitigate this saturation effect. We introduce the novel “Contrastive Leave One Out Boost” (CLOOB), which uses modern Hopfield networks for covariance enrichment together with the InfoLOOB objective. In experiments we compare CLOOB to CLIP after pre-training on the Conceptual Captions and the YFCC dataset with respect to their zero-shot transfer learning performance on other datasets. CLOOB consistently outperforms CLIP at zero-shot transfer learning across all considered architectures and datasets.
|
[
"Multi-modal Learning",
"Contrastive Learning",
"Neural Networks",
"Transfer Learning",
"Computer Vision",
"Natural Language Processing"
] | |
NeurIPS
| 2,022
| 56,987
|
Towards Creating Benchmark Datasets of Universal Neural Network Potential for Material Discovery
|
Recently, neural network potentials (NNPs) have been shown to be particularly effective in conducting atomistic simulations for computational material discovery. Especially in recent years, large-scale datasets have begun to emerge for the purpose of ensuring versatility. However, we show that even with a large dataset and a model that achieves good validation accuracy, the resulting energy surface can be quite delicate and the easily reach unrealistic extrapolation regions during the simulation. We first demonstrate this behavior using a DimeNet++ trained on Open Catalyst 2020 dataset (OC20). Based on this observation, we propose a hypothesis that for NNP models to attain the versatality, the training dataset should contain a diverse set of virtual structures. To verify this, we have created a relatively much smaller benchmark dataset called "High-temperature Multi-Element 2021" (HME21) dataset, which was sampled through a high-temperature molecular dynamics simulation and has less prior information. We conduct benchmark experiments on HME21 and show that training a TeaNet on HME21 can achieve better performance in reproducing the absorption process, although HME21 does not contain corresponding atomic structures. Our findings indicates that dataset diversity can be more essential than the dataset quantity in training universal NNPs for material discovery.
|
[
"Computational Materials Science",
"Machine Learning in Materials Science",
"Neural Network Potentials",
"Atomistic Simulations",
"Dataset Benchmarking"
] | |
NeurIPS
| 2,022
| 54,562
|
Expectation-Maximization Contrastive Learning for Compact Video-and-Language Representations
|
Most video-and-language representation learning approaches employ contrastive learning, e.g., CLIP, to project the video and text features into a common latent space according to the semantic similarities of text-video pairs. However, such learned shared latent spaces are not often optimal, and the modality gap between visual and textual representation can not be fully eliminated. In this paper, we propose Expectation-Maximization Contrastive Learning (EMCL) to learn compact video-and-language representations. Specifically, we use the Expectation-Maximization algorithm to find a compact set of bases for the latent space, where the features could be concisely represented as the linear combinations of these bases. Such feature decomposition of video-and-language representations reduces the rank of the latent space, resulting in increased representing power for the semantics. Extensive experiments on three benchmark text-video retrieval datasets prove that our EMCL can learn more discriminative video-and-language representations than previous methods, and significantly outperform previous state-of-the-art methods across all metrics. More encouragingly, the proposed method can be applied to boost the performance of existing approaches either as a jointly training layer or an out-of-the-box inference module with no extra training, making it easy to be incorporated into any existing methods.
|
[
"Contrastive Learning",
"Video-and-Language Representation",
"Multimodal Learning",
"Computer Vision",
"Natural Language Processing"
] | |
ICML
| 2,023
| 23,842
|
Who Needs to Know? Minimal Knowledge for Optimal Coordination
|
To optimally coordinate with others in cooperative games, it is often crucial to have information about one’s collaborators: successful driving requires understanding which side of the road to drive on. However, not every feature of collaborators is strategically relevant: the fine-grained acceleration of drivers may be ignored while maintaining optimal coordination. We show that there is a well-defined dichotomy between strategically relevant and irrelevant information. Moreover, we show that, in dynamic games, this dichotomy has a compact representation that can be efficiently computed via a Bellman backup operator. We apply this algorithm to analyze the strategically relevant information for tasks in both a standard and a partially observable version of the Overcooked environment. Theoretical and empirical results show that our algorithms are significantly more efficient than baselines. Videos are available at https://minknowledge.github.io.
|
[
"Game Theory",
"Cooperative Games",
"Multi-Agent Systems",
"Decision Making",
"Dynamic Games",
"Information Theory"
] | |
ICML
| 2,024
| 34,351
|
Shifted Interpolation for Differential Privacy
|
Noisy gradient descent and its variants are the predominant algorithms for differentially private machine learning. It is a fundamental question to quantify their privacy leakage, yet tight characterizations remain open even in the foundational setting of convex losses. This paper improves over previous analyses by establishing (and refining) the “privacy amplification by iteration” phenomenon in the unifying framework of $f$-differential privacy---which tightly captures all aspects of the privacy loss and immediately implies tighter privacy accounting in other notions of differential privacy, e.g., $(\varepsilon,\delta)$-DP and Rényi DP. Our key technical insight is the construction of *shifted interpolated processes* that unravel the popular shifted-divergences argument, enabling generalizations beyond divergence-based relaxations of DP. Notably, this leads to the first *exact* privacy analysis in the foundational setting of strongly convex optimization. Our techniques extend to many settings: convex/strongly convex, constrained/unconstrained, full/cyclic/stochastic batches, and all combinations thereof. As an immediate corollary, we recover the $f$-DP characterization of the exponential mechanism for strongly convex optimization in Gopi et al. (2022), and moreover extend this result to more general settings.
|
[
"Differential Privacy",
"Optimization",
"Privacy Analysis",
"Convex Optimization"
] | |
ICML
| 2,023
| 25,802
|
Gradient Scaling on Deep Spiking Neural Networks with Spike-Dependent Local Information
|
Deep spiking neural networks (SNNs) are promising neural networks for their model capacity from deep neural network architecture and energy efficiency from SNNs' operations. To train deep SNNs, recently, spatio-temporal backpropagation (STBP) with surrogate gradient was proposed. Although deep SNNs have been successfully trained with STBP, they cannot fully utilize spike information. In this work, we proposed gradient scaling with local spike information, which is the relation between pre- and post-synaptic spikes. Considering the causality between spikes, we could enhance the training performance of deep SNNs. According to our experiments, we could achieve higher accuracy with lower spikes by adopting the gradient scaling on image classification tasks, such as CIFAR10 and CIFAR100.
|
[
"Deep Learning",
"Spiking Neural Networks",
"Neural Network Training",
"Computational Neuroscience"
] |
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