conference
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NeurIPS
| 2,022
| 53,739
|
Surprising Instabilities in Training Deep Networks and a Theoretical Analysis
|
We empirically demonstrate numerical instabilities in training standard deep networks with SGD. Specifically, we show numerical error (on the order of the smallest floating point bit) induced from floating point arithmetic in training deep nets can be amplified significantly and result in significant test accuracy variance, comparable to the test accuracy variance due to stochasticity in SGD. We show how this is likely traced to instabilities of the optimization dynamics that are localized over iterations and regions of the weight tensor space. We do this by presenting a theoretical framework using numerical analysis of partial differential equations (PDE), and analyzing the gradient descent PDE of a one-layer convolutional neural network, which is sufficient to illustrate these instabilities. We show that it is stable only under certain conditions on the learning rate and weight decay. We reproduce the localized instabilities in the PDE for the one-layer network, which arise when the conditions are violated.
|
[
"Deep Learning",
"Numerical Analysis",
"Optimization",
"Machine Learning Theory",
"Computational Mathematics"
] | |
ICML
| 2,023
| 24,577
|
Structure Learning of Latent Factors via Clique Search on Correlation Thresholded Graphs
|
Despite the widespread application of latent factor analysis, existing methods suffer from the following weaknesses: requiring the number of factors to be known, lack of theoretical guarantees for learning the model structure, and nonidentifiability of the parameters due to rotation invariance properties of the likelihood. We address these concerns by proposing a fast correlation thresholding (CT) algorithm that simultaneously learns the number of latent factors and a rotationally identifiable model structure. Our novel approach translates this structure learning problem into the search for so-called independent maximal cliques in a thresholded correlation graph that can be easily constructed from the observed data. Our clique analysis technique scales well up to thousands of variables, while competing methods are not applicable in a reasonable amount of running time. We establish a finite-sample error bound and high-dimensional consistency for the structure learning of our method. Through a series of simulation studies and a real data example, we show that the CT algorithm is an accurate method for learning the structure of factor analysis models and is robust to violations of its assumptions.
|
[
"Statistical Learning",
"Factor Analysis",
"Graph Theory",
"High-Dimensional Statistics"
] | |
ICML
| 2,024
| 37,198
|
Can LLMs Enhance Performance Prediction for Deep Learning Models?
|
Accurate performance prediction of Deep Learning (DL) models is essential for efficient resource allocation and optimizations in various stages of the DL system stack. While existing approaches can achieve high prediction accuracy, they lack the ability to quickly adapt to new hardware environments or emerging workloads. This paper leverages both Graph Neural Networks (GNNs) and Large Language Models (LLMs) to enhance the accuracy and adaptability of DL performance prediction. Our intuition is that GNNs are adept at capturing the structural information of DL models, naturally represented as graphs, while LLMs provide generalization and the ability to quickly adapt to various tasks thanks to extensive pre-training data.We empirically demonstrate that using GNN-derived graph embeddings as inputs to an LLM outperforms traditional representations, including high-level text summary and lossless semi-structured text (e.g., JSON), for this task. Furthermore, we propose a structured pre-training strategy to enable model adaptation to new hardware environments, significantly reducing the need for extensive retraining. Our experiments validate the effectiveness of this approach, showing an 8.8 percentage-point improvement in accuracy over a state-of-the-art GNN baseline. Notably, when adapted to new hardware with few samples, our method achieves a remarkable 30--70 percentage-point increase in accuracy compared to the GNN baseline.
|
[
"Deep Learning",
"Performance Prediction",
"Graph Neural Networks",
"Large Language Models",
"Machine Learning Adaptation",
"Resource Allocation in Computing",
"Model Optimization",
"Hardware Adaptation in Machine Learning"
] | |
ICML
| 2,024
| 34,921
|
Covert Malicious Finetuning: Challenges in Safeguarding LLM Adaptation
|
Black-box finetuning is an emerging interface for adapting state-of-the-art language models to user needs. However, such access may also let malicious actors undermine model safety. To demonstrate the challenge of defending finetuning interfaces, we introduce covert malicious finetuning, a method to compromise model safety via finetuning while evading detection. Our method constructs a malicious dataset where every individual datapoint appears innocuous, but finetuning on the dataset teaches the model to respond to encoded harmful requests with encoded harmful responses. Applied to GPT-4, our method produces a finetuned model that acts on harmful instructions 99% of the time and avoids detection by defense mechanisms such as dataset inspection, safety evaluations, and input/output classifiers. Our findings question whether black-box finetuning access can be secured against sophisticated adversaries.
|
[
"Machine Learning Security",
"Natural Language Processing",
"Adversarial Machine Learning",
"Model Safety and Robustness",
"AI Ethics and Safety"
] | |
ICLR
| 2,024
| 21,761
|
Neural Ordinary Differential Equations for Modeling Epidemic Spreading
|
Mathematical models of infectious diseases have long been used for studying the mechanisms by which diseases spread, for predicting the spread of epidemics, and also for controlling their outbreaks. These models are based on some assumptions and different assumptions give rise to different models. Models on social networks of individuals which capture contact patterns are usually more realistic and can more accurately model contagion dynamics. Unfortunately, computing the output of realistic models is often hard. Thus, modeling the evolution of contagion dynamics over large complex networks constitutes a challenging task. In this paper, we present a computational approach to model the contagion dynamics underlying infectious diseases. Specifically, we focus on the susceptible-infectious-recovered (SIR) epidemic model on networks. Given that this model can be expressed by an intractable system of ordinary differential equations, we devise a simpler system that approximates the output of the model. Then, we capitalize on recent advances in neural ordinary differential equations and propose a neural architecture that can effectively predict the course of an epidemic on the network. We apply the proposed architecture on several network datasets and compare it against state-of-the-art methods under different experimental settings. Our results indicate that the proposed method improves predictions in various spreading scenarios, paving the way for the extensive application of interpretable neural networks in the field of epidemic spreading. At the same time, the proposed model is highly efficient even when trained on very large networks where traditional algorithms become significantly slower.
|
[
"Computational Epidemiology",
"Mathematical Modeling",
"Neural Networks",
"Ordinary Differential Equations",
"Network Science",
"Infectious Disease Modeling"
] | |
ICLR
| 2,022
| 6,966
|
MIDI-DDSP: Detailed Control of Musical Performance via Hierarchical Modeling
|
Musical expression requires control of both what notes that are played, and how they are performed. Conventional audio synthesizers provide detailed expressive controls, but at the cost of realism. Black-box neural audio synthesis and concatenative samplers can produce realistic audio, but have few mechanisms for control. In this work, we introduce MIDI-DDSP a hierarchical model of musical instruments that enables both realistic neural audio synthesis and detailed user control. Starting from interpretable Differentiable Digital Signal Processing (DDSP) synthesis parameters, we infer musical notes and high-level properties of their expressive performance (such as timbre, vibrato, dynamics, and articulation). This creates a 3-level hierarchy (notes, performance, synthesis) that affords individuals the option to intervene at each level, or utilize trained priors (performance given notes, synthesis given performance) for creative assistance. Through quantitative experiments and listening tests, we demonstrate that this hierarchy can reconstruct high-fidelity audio, accurately predict performance attributes for a note sequence, independently manipulate the attributes of a given performance, and as a complete system, generate realistic audio from a novel note sequence. By utilizing an interpretable hierarchy, with multiple levels of granularity, MIDI-DDSP opens the door to assistive tools to empower individuals across a diverse range of musical experience.
|
[
"Music Technology",
"Audio Synthesis",
"Machine Learning in Music",
"Digital Signal Processing",
"Human-Computer Interaction in Music"
] | |
NeurIPS
| 2,023
| 70,666
|
Generalized test utilities for long-tail performance in extreme multi-label classification
|
Extreme multi-label classification (XMLC) is the task of selecting a small subset of relevant labels from a very large set of possible labels. As such, it is characterized by long-tail labels, i.e., most labels have very few positive instances. With standard performance measures such as precision@k, a classifier can ignore tail labels and still report good performance. However, it is often argued that correct predictions in the tail are more "interesting" or "rewarding," but the community has not yet settled on a metric capturing this intuitive concept. The existing propensity-scored metrics fall short on this goal by confounding the problems of long-tail and missing labels. In this paper, we analyze generalized metrics budgeted "at k" as an alternative solution. To tackle the challenging problem of optimizing these metrics, we formulate it in the expected test utility (ETU) framework, which aims to optimize the expected performance on a given test set. We derive optimal prediction rules and construct their computationally efficient approximations with provable regret guarantees and being robust against model misspecification. Our algorithm, based on block coordinate descent, scales effortlessly to XMLC problems and obtains promising results in terms of long-tail performance.
|
[
"Extreme Multi-Label Classification",
"Long-Tail Distribution",
"Performance Metrics",
"Algorithm Optimization"
] | |
ICML
| 2,024
| 33,978
|
Energy-based Backdoor Defense without Task-Specific Samples and Model Retraining
|
Backdoor defense is crucial to ensure the safety and robustness of machine learning models when under attack. However, most existing methods specialize in either the detection or removal of backdoors, but seldom both. While few works have addressed both, these methods rely on strong assumptions or entail significant overhead costs, such as the need of task-specific samples for detection and model retraining for removal. Hence, the key challenge is how to reduce overhead and relax unrealistic assumptions. In this work, we propose two Energy-Based BAckdoor defense methods, called EBBA and EBBA+, that can achieve both backdoored model detection and backdoor removal with low overhead. Our contributions are twofold: First, we offer theoretical analysis for our observation that a predefined target label is more likely to occur among the top results for various samples. Inspired by this, we develop an enhanced energy-based technique, called EBBA, to detect backdoored models without task-specific samples (i.e., samples from any tasks). Secondly, we theoretically analyze that after data corruption, the original clean label of a poisoned sample is more likely to be predicted as a top output by the model, a sharp contrast to clean samples. Accordingly, we extend EBBA to develop EBBA+, a new transferred energy approach to efficiently detect poisoned images and remove backdoors without model retraining. Extensive experiments on multiple benchmark datasets demonstrate the superior performance of our methods over baselines in both backdoor detection and removal. Notably, the proposed methods can effectively detect backdoored model and poisoned images as well as remove backdoors at the same time.
|
[
"Machine Learning Security",
"Adversarial Machine Learning",
"Backdoor Attacks and Defenses",
"Model Robustness",
"Cybersecurity in AI Systems"
] | |
ICLR
| 2,023
| 12,235
|
SIMPLE: Specialized Model-Sample Matching for Domain Generalization
|
In domain generalization (DG), most existing methods aspire to fine-tune a specific pretrained model through novel DG algorithms. In this paper, we propose an alternative direction, i.e., to efficiently leverage a pool of pretrained models without fine-tuning. Through extensive empirical and theoretical evidence, we demonstrate that (1) pretrained models have possessed generalization to some extent while there is no single best pretrained model across all distribution shifts, and (2) out-of-distribution (OOD) generalization error depends on the fitness between the pretrained model and unseen test distributions. This analysis motivates us to incorporate diverse pretrained models and to dispatch the best matched models for each OOD sample by means of recommendation techniques. To this end, we propose SIMPLE, a specialized model-sample matching method for domain generalization. First, the predictions of pretrained models are adapted to the target domain by a linear label space transformation. A matching network aware of model specialty is then proposed to dynamically recommend proper pretrained models to predict each test sample. The experiments on DomainBed show that our method achieves significant performance improvements (up to 12.2% for individual dataset and 3.9% on average) compared to state-of-the-art (SOTA) methods and further achieves 6.1% gain via enlarging the pretrained model pool. Moreover, our method is highly efficient and achieves more than 1000 times training speedup compared to the conventional DG methods with fine-tuning a pretrained model. Code and supplemental materials are available at https://seqml.github.io/simple.
|
[
"Domain Generalization",
"Transfer Learning",
"Out-of-Distribution Generalization",
"Model Selection and Recommendation Systems"
] | |
ICML
| 2,024
| 32,973
|
Multi-Agent Reinforcement Learning Meets Leaf Sequencing in Radiotherapy
|
In contemporary radiotherapy planning (RTP), a key module leaf sequencing is predominantly addressed by optimization-based approaches. In this paper, we propose a novel deep reinforcement learning (DRL) model termed asReinforced Leaf Sequencer(RLS) in a multi-agent framework for leaf sequencing. The RLS model offers improvements to time-consuming iterative optimization steps via large-scale training and can control movement patterns through the design of reward mechanisms. We have conducted experiments on four datasets with four metrics and compared our model with a leading optimization sequencer. Our findings reveal that the proposed RLS model can achieve reduced fluence reconstruction errors, and potential faster convergence when integrated in an optimization planner. Additionally, RLS has shown promising results in a full artificial intelligence RTP pipeline. We hope this pioneer multi-agent RL leaf sequencer can foster future research on machine learning for RTP.
|
[
"Multi-Agent Systems",
"Reinforcement Learning",
"Radiotherapy Planning",
"Medical Imaging and Radiotherapy",
"Optimization and Control"
] | |
ICML
| 2,024
| 35,161
|
Gradual Divergence for Seamless Adaptation: A Novel Domain Incremental Learning Method
|
Domain incremental learning (DIL) poses a significant challenge in real-world scenarios, as models need to be sequentially trained on diverse domains over time, all the while avoiding catastrophic forgetting. Mitigating representation drift, which refers to the phenomenon of learned representations undergoing changes as the model adapts to new tasks, can help alleviate catastrophic forgetting. In this study, we propose a novel DIL method namedDARE, featuring a three-stage training process: Divergence, Adaptation, and REfinement. This process gradually adapts the representations associated with new tasks into the feature space spanned by samples from previous tasks, simultaneously integrating task-specific decision boundaries. Additionally, we introduce a novel strategy for buffer sampling and demonstrate the effectiveness of our proposed method, combined with this sampling strategy, in reducing representation drift within the feature encoder. This contribution effectively alleviates catastrophic forgetting across multiple DIL benchmarks. Furthermore, our approach prevents sudden representation drift at task boundaries, resulting in a well-calibrated DIL model that maintains the performance on previous tasks.
|
[
"Domain Incremental Learning",
"Catastrophic Forgetting",
"Representation Learning",
"Neural Networks"
] | |
ICML
| 2,023
| 24,605
|
Optimal randomized multilevel Monte Carlo for repeatedly nested expectations
|
The estimation of repeatedly nested expectations is a challenging task that arises in many real-world systems. However, existing methods generally suffer from high computational costs when the number of nestings becomes large. Fix any non-negative integer $D$ for the total number of nestings. Standard Monte Carlo methods typically cost at least $\mathcal{O}(\varepsilon^{-(2+D)})$ and sometimes $\mathcal {O}(\varepsilon^{-2(1+D)})$ to obtain an estimator up to $\varepsilon$-error. More advanced methods, such as multilevel Monte Carlo, currently only exist for $D = 1$. In this paper, we propose a novel Monte Carlo estimator called $\mathsf{READ}$, which stands for ``Recursive Estimator for Arbitrary Depth.'' Our estimator has an optimal computational cost of $\mathcal{O}(\varepsilon^{-2})$ for every fixed $D$ under suitable assumptions, and a nearly optimal computational cost of $\mathcal{O}(\varepsilon^{-2(1 + \delta)})$ for any $0 < \delta < \frac12$ under much more general assumptions. Our estimator is also unbiased, which makes it easy to parallelize. The key ingredients in our construction are an observation of the problem's recursive structure and the recursive use of the randomized multilevel Monte Carlo method.
|
[
"Computational Mathematics",
"Monte Carlo Methods",
"Numerical Analysis",
"Stochastic Processes",
"Probability and Statistics"
] | |
ICML
| 2,023
| 23,512
|
Tractable Control for Autoregressive Language Generation
|
Despite the success of autoregressive large language models in text generation, it remains a major challenge to generate text that satisfies complex constraints: sampling from the conditional distribution ${\Pr}(\text{text} | \alpha)$ is intractable for even the simplest lexical constraints $\alpha$. To overcome this challenge, we propose to use tractable probabilistic models (TPMs) to impose lexical constraints in autoregressive text generation models, which we refer to as GeLaTo (Generating Language with Tractable Constraints). To demonstrate the effectiveness of this framework, we use distilled hidden Markov models, where we can efficiently compute ${\Pr}(\text{text} | \alpha)$, to guide autoregressive generation from GPT2. GeLaTo achieves state-of-the-art performance on challenging benchmarks for constrained text generation (e.g., CommonGen), beating various strong baselines by a large margin. Our work not only opens up new avenues for controlling large language models but also motivates the development of more expressive TPMs.
|
[
"Natural Language Processing",
"Text Generation",
"Autoregressive Models",
"Probabilistic Models",
"Constrained Text Generation"
] | |
ICLR
| 2,022
| 6,579
|
The Effects of Reward Misspecification: Mapping and Mitigating Misaligned Models
|
Reward hacking---where RL agents exploit gaps in misspecified proxy rewards---has been widely observed, but not yet systematically studied. To understand reward hacking, we construct four RL environments with different misspecified rewards. We investigate reward hacking as a function of agent capabilities: model capacity, action space resolution, and observation space noise. Typically, more capable agents are able to better exploit reward misspecifications, causing them to attain higher proxy reward and lower true reward. Moreover, we find instances of \emph{phase transitions}: capability thresholds at which the agent's behavior qualitatively shifts, leading to a sharp decrease in the true reward. Such phase transitions pose challenges to monitoring the safety of ML systems. To encourage further research on reward misspecification, address this, we propose an anomaly detection task for aberrant policies and offer several baseline detectors.
|
[
"Reinforcement Learning",
"Reward Misspecification",
"Machine Learning Safety",
"Anomaly Detection"
] | |
ICML
| 2,022
| 17,273
|
Transformer Quality in Linear Time
|
We revisit the design choices in Transformers, and propose methods to address their weaknesses in handling long sequences. First, we propose a simple layer named gated attention unit, which allows the use of a weaker single-head attention with minimal quality loss. We then propose a linear approximation method complementary to this new layer, which is accelerator-friendly and highly competitive in quality. The resulting model, named FLASH, matches the perplexity of improved Transformers over both short (512) and long (8K) context lengths, achieving training speedups of up to 4.9x on Wiki-40B and 12.1x on PG-19 for auto-regressive language modeling, and 4.8x on C4 for masked language modeling.
|
[
"Natural Language Processing",
"Deep Learning",
"Neural Networks",
"Model Optimization"
] | |
ICML
| 2,023
| 24,730
|
Generalizing Neural Wave Functions
|
Recent neural network-based wave functions have achieved state-of-the-art accuracies in modeling ab-initio ground-state potential energy surface. However, these networks can only solve different spatial arrangements of the same set of atoms. To overcome this limitation, we present Graph-learned orbital embeddings (Globe), a neural network-based reparametrization method that can adapt neural wave functions to different molecules. Globe learns representations of local electronic structures that generalize across molecules via spatial message passing by connecting molecular orbitals to covalent bonds. Further, we propose a size-consistent wave function Ansatz, the Molecular orbital network (Moon), tailored to jointly solve Schrödinger equations of different molecules. In our experiments, we find Moon converging in 4.5 times fewer steps to similar accuracy as previous methods or to lower energies given the same time. Further, our analysis shows that Moon's energy estimate scales additively with increased system sizes, unlike previous work where we observe divergence. In both computational chemistry and machine learning, we are the first to demonstrate that a single wave function can solve the Schrödinger equation of molecules with different atoms jointly.
|
[
"Computational Chemistry",
"Quantum Chemistry",
"Neural Networks",
"Molecular Modeling"
] | |
ICML
| 2,024
| 34,893
|
The Pitfalls of Next-Token Prediction
|
Can a mere next-token predictor faithfully model human thinking? Our work is aimed at crystallizing this intuitive concern, which is currently fragmented in the literature. First, we emphasize isolating the two phases of next-token prediction that are often conflated: autoregression during inference vs. teacher-forcing during training. We argue that the previously-identified problem of "exponential error accumulation" is a symptom of autoregressive inference. But more concerningly, we identify that teacher-forcing can let the model fit the training data by cheating, causing total in-distribution failure. We design a minimal planning task where empirically both the Transformer and the Mamba architecture fail in this manner - remarkably, despite the task being easy to learn. Overall, our work consolidates these and other essential arguments surrounding next-token prediction. We hope this effort can ground future discussions and inspire explorations beyond the next-token prediction paradigm.
|
[
"Natural Language Processing",
"Model Evaluation and Analysis"
] | |
NeurIPS
| 2,022
| 65,708
|
Assistance with large language models
|
A core part of AI alignment is training AI systems to be helpful, or more generally, to interact with humans appropriately. We look at this problem in the context of large language models. Past works have focused on training these models to perform specific tasks, or follow instructions. In contrast, we believe helpfulness requires back-and-forth interaction between the AI and the human it is trying to assist. Here, we consider a multi-step interaction in which a human asks a question, and the AI has an opportunity to ask a clarifying question to resolve ambiguities before responding. The assistance framework formalizes the idea of an AI which aims to maximize the human's reward but is ignorant of the human reward function. Past works solved toy assistance environments using exact POMDP solvers as well as deep reinforcement learning. We apply a behavioral cloning approach, and fine-tune GPT-3 such that it can respond to clear input questions directly, clarify the intent behind vague input questions, and respond based on the clarification it receives. We show that this approach leads to quantitative improvements in answer accuracy compared to a baseline that cannot ask for clarifications. While the assistance framework assumes the correct behavior of an AI is to infer and maximize a human's reward, our approach can be used to learn any interaction protocol between the AI and the human. We believe exploring interaction protocols that are easy to learn robustly, and can be used to "bootstrap" further alignment are a promising direction for future research.
|
[
"AI Alignment",
"Human-AI Interaction",
"Natural Language Processing",
"Reinforcement Learning"
] | |
ICML
| 2,023
| 24,452
|
Dimensionality Reduction for General KDE Mode Finding
|
Finding the mode of a high dimensional probability distribution $\mathcal{D}$ is a fundamental algorithmic problem in statistics and data analysis. There has been particular interest in efficient methods for solving the problem when $\mathcal{D}$ is represented as a mixture model or kernel density estimate, although few algorithmic results with worst-case approximation and runtime guarantees are known. In this work, we significantly generalize a result of (LeeLiMusco:2021) on mode approximation for Gaussian mixture models. We develop randomized dimensionality reduction methods for mixtures involving a broader class of kernels, including the popular logistic, sigmoid, and generalized Gaussian kernels. As in Lee et al.'s work, our dimensionality reduction results yield quasi-polynomial algorithms for mode finding with multiplicative accuracy $(1-\epsilon)$ for any $\epsilon > 0$. Moreover, when combined with gradient descent, they yield efficient practical heuristics for the problem. In addition to our positive results, we prove a hardness result for box kernels, showing that there is no polynomial time algorithm for finding the mode of a kernel density estimate, unless $\mathit{P} = \mathit{NP}$. Obtaining similar hardness results for kernels used in practice (like Gaussian or logistic kernels) is an interesting future direction.
|
[
"Statistics",
"Data Analysis",
"Algorithmic Theory",
"Dimensionality Reduction",
"Kernel Methods"
] | |
ICLR
| 2,022
| 7,064
|
Neural Program Synthesis with Query
|
Aiming to find a program satisfying the user intent given input-output examples, program synthesis has attracted increasing interest in the area of machine learning. Despite the promising performance of existing methods, most of their success comes from the privileged information of well-designed input-output examples. However, providing such input-output examples is unrealistic because it requires the users to have the ability to describe the underlying program with a few input-output examples under the training distribution. In this work, we propose a query-based framework that trains a query neural network to generate informative input-output examples automatically and interactively from a large query space. The quality of the query depends on the amount of the mutual information between the query and the corresponding program, which can guide the optimization of the query framework. To estimate the mutual information more accurately, we introduce the functional space (F-space) which models the relevance between the input-output examples and the programs in a differentiable way. We evaluate the effectiveness and generalization of the proposed query-based framework on the Karel task and the list processing task. Experimental results show that the query-based framework can generate informative input-output examples which achieveand even outperform well-designed input-output examples.
|
[
"Program Synthesis",
"Neural Networks",
"Query-based Systems"
] | |
ICML
| 2,024
| 35,019
|
Position: Beyond Personhood: Agency, Accountability, and the Limits of Anthropomorphic Ethical Analysis
|
What isagency,and why does it matter? In this work, we draw from the political science and philosophy literature and give two competing visions of what it means to be an (ethical) agent. The first view, which we termmechanistic, is commonly— and implicitly—assumed in AI research, yet it is a fundamentally limited means to understand the ethical characteristics of AI. Under the second view, which we term volitional, AI can no longer be considered an ethical agent. We discuss the implications of each of these views for two critical questions: first, what the ideal system “ought” to look like, and second, how accountability may be achieved. In light of this discussion, we ultimately argue that, in the context of ethically-significant behavior, AI should be viewed not as an agent but as the outcome of political processes.
|
[
"Philosophy",
"Ethics",
"Political Science",
"Artificial Intelligence Ethics"
] | |
ICLR
| 2,022
| 6,862
|
Zero-Shot Self-Supervised Learning for MRI Reconstruction
|
Deep learning (DL) has emerged as a powerful tool for accelerated MRI reconstruction, but often necessitates a database of fully-sampled measurements for training. Recent self-supervised and unsupervised learning approaches enable training without fully-sampled data. However, a database of undersampled measurements may not be available in many scenarios, especially for scans involving contrast or translational acquisitions in development. Moreover, recent studies show that database-trained models may not generalize well when the unseen measurements differ in terms of sampling pattern, acceleration rate, SNR, image contrast, and anatomy. Such challenges necessitate a new methodology to enable subject-specific DL MRI reconstruction without external training datasets, since it is clinically imperative to provide high-quality reconstructions that can be used to identify lesions/disease for $\textit{every individual}$. In this work, we propose a zero-shot self-supervised learning approach to perform subject-specific accelerated DL MRI reconstruction to tackle these issues. The proposed approach partitions the available measurements from a single scan into three disjoint sets. Two of these sets are used to enforce data consistency and define loss during training for self-supervision, while the last set serves to self-validate, establishing an early stopping criterion. In the presence of models pre-trained on a database with different image characteristics, we show that the proposed approach can be combined with transfer learning for faster convergence time and reduced computational complexity.
|
[
"Medical Imaging",
"Deep Learning",
"MRI Reconstruction",
"Self-Supervised Learning",
"Zero-Shot Learning"
] | |
ICLR
| 2,024
| 18,426
|
Symmetric Mean-field Langevin Dynamics for Distributional Minimax Problems
|
In this paper, we extend mean-field Langevin dynamics to minimax optimization over probability distributions for the first time with symmetric and provably convergent updates. We propose \emph{mean-field Langevin averaged gradient} (MFL-AG), a single-loop algorithm that implements gradient descent ascent in the distribution spaces with a novel weighted averaging, and establish average-iterate convergence to the mixed Nash equilibrium. We also study both time and particle discretization regimes and prove a new uniform-in-time propagation of chaos result which accounts for the dependency of the particle interactions on all previous distributions. Furthermore, we propose \emph{mean-field Langevin anchored best response} (MFL-ABR), a symmetric double-loop algorithm based on best response dynamics with linear last-iterate convergence. Finally, we study applications to zero-sum Markov games and conduct simulations demonstrating long-term optimality.
|
[
"Optimization",
"Probability Theory",
"Game Theory",
"Stochastic Processes"
] | |
NeurIPS
| 2,022
| 62,235
|
Joint rotational invariance and adversarial training of a dual-stream Transformer yields state of the art Brain-Score for Area V4
|
Modern high-scoring models of vision in the brain score competition do not stem from Vision Transformers. However, in this paper, we provide evidence against the unexpected trend of Vision Transformers (ViT) being not perceptually aligned with human visual representations by showing how a dual-stream Transformer, a CrossViT $~\textit{a la}$ Chen et. al. (2021), under a joint rotationally-invariant and adversarial optimization procedure yields 2nd place in the aggregate Brain-Score 2022 competition (Schrimpf et al., 2020b) averaged across all visual categories, and at the time of the competition held 1st place for the highest explainable variance of area V4. In addition, our current Transformer-based model also achieves greater explainable variance for areas V4, IT, and Behaviour than a biologically-inspired CNN (ResNet50) that integrates a frontal V1-like computation module (Dapello et al., 2020). To assess the contribution of the optimization scheme with respect to the CrossViT architecture, we perform several additional experiments on differently optimized CrossViT's regarding adversarial robustness, common corruption benchmarks, mid-ventral stimuli interpretation, and feature inversion. Against our initial expectations, our family of results provides tentative support for an $\textit{``All roads lead to Rome''}$ argument enforced via a joint optimization rule even for non biologically-motivated models of vision such as Vision Transformers.
|
[
"Computer Vision",
"Neural Networks",
"Vision Transformers",
"Adversarial Training",
"Computational Neuroscience",
"Visual Perception"
] | |
NeurIPS
| 2,023
| 74,884
|
Latent Painter
|
Latent diffusers revolutionized the generative AI and inspired creative art. When denoising the latent, the predicted original image at each step collectively animates the formation. However, the animation is limited by the denoising nature of the diffuser, and only renders a sharpening process. This work presents Latent Painter, which uses the latent as the canvas, and the diffuser predictions as the plan, to generate painting animation. Latent Painter also transits one generated image to another, which can happen between images from two different sets of checkpoints.
|
[
"Generative AI",
"Computer Graphics",
"Animation",
"Art and Technology"
] | |
ICML
| 2,024
| 32,880
|
Training Large Language Models for Reasoning through Reverse Curriculum Reinforcement Learning
|
In this paper, we propose **R**$^3$: Learning **R**easoning through **R**everse Curriculum **R**einforcement Learning (RL), a novel method that employs only outcome supervision to achieve the benefits of process supervision for large language models. The core challenge in applying RL to complex reasoning is to identify a sequence of actions that result in positive rewards and provide appropriate supervision for optimization. Outcome supervision provides sparse rewards for final results without identifying error locations, whereas process supervision offers step-wise rewards but requires extensive manual annotation. **R**$^3$ overcomes these limitations by learning from correct demonstrations. Specifically, **R**$^3$ progressively slides the start state of reasoning from a demonstration's end to its beginning, facilitating easier model exploration at all stages. Thus, **R**$^3$ establishes a step-wise curriculum, allowing outcome supervision to offer step-level signals and precisely pinpoint errors. Using Llama2-7B, our method surpasses RL baseline on eight reasoning tasks by $4.1$ points on average. Notably, in program-based reasoning, 7B-scale models perform comparably to larger models or closed-source models with our **R**$^3$.
|
[
"Reinforcement Learning",
"Natural Language Processing",
"Large Language Models",
"Curriculum Learning",
"Reasoning and Problem Solving"
] | |
NeurIPS
| 2,023
| 76,178
|
A deep learning framework for jointly extracting spectra and source-count distributions of count maps
|
Gamma-ray telescopes measure the direction and energy of incoming photons, resulting in photon-count maps that contain both spatial and spectral information. A major goal when analyzing such data is to determine source-count distributions (SCDs), which characterize the brightness of point-sources too faint to be detected individually. Existing statistical and machine learning methods for this task exist; however, they typically neglect the photon energy. We present a deep learning framework able to jointly reconstruct the spectra of different emission components and the SCDs of point-source populations.In a proof-of-concept example, we show that our method accurately extracts even complex-shaped spectra and SCDs from simulated maps.
|
[
"Astrophysics",
"Data Analysis",
"Gamma-ray Astronomy",
"Deep Learning"
] | |
NeurIPS
| 2,023
| 76,252
|
Emulating deviations from Einstein's General Relativity using conditional GANs
|
Computationally expensive simulations pose a severe bottleneck, especially in astronomy, where several realizations of the same physical processes are required to facilitate scientific studies, such as exploring new physics or constraining the underlying physics by comparing it with observations. Simulations that modify Einstein's gravity require solving highly non-linear equations and take $\sim$10 times more time than the normal ones. In order to mitigate this bottleneck, we use a conditional generative adversarial network (cGAN) to map output fields from normal simulations to output fields of time-consuming simulations. Our model uses a frequency-based loss during training and uses indirect emulation wherein the mapping is achieved using ratio fields instead of the traditional input $\rightarrow$ output domain translation. Our cGAN agrees well with the ground-truth images despite the visually minor differences between fields from the input and output domains.
|
[
"Computational Astrophysics",
"Machine Learning in Physics",
"General Relativity",
"Simulation and Modeling in Astronomy"
] | |
ICML
| 2,022
| 17,019
|
Robustness and Accuracy Could Be Reconcilable by (Proper) Definition
|
The trade-off between robustness and accuracy has been widely studied in the adversarial literature. Although still controversial, the prevailing view is that this trade-off is inherent, either empirically or theoretically. Thus, we dig for the origin of this trade-off in adversarial training and find that it may stem from the improperly defined robust error, which imposes an inductive bias of local invariance --- an overcorrection towards smoothness. Given this, we advocate employing local equivariance to describe the ideal behavior of a robust model, leading to a self-consistent robust error named SCORE. By definition, SCORE facilitates the reconciliation between robustness and accuracy, while still handling the worst-case uncertainty via robust optimization. By simply substituting KL divergence with variants of distance metrics, SCORE can be efficiently minimized. Empirically, our models achieve top-rank performance on RobustBench under AutoAttack. Besides, SCORE provides instructive insights for explaining the overfitting phenomenon and semantic input gradients observed on robust models.
|
[
"Adversarial Machine Learning",
"Robustness in Machine Learning",
"Optimization in Machine Learning",
"Model Evaluation and Metrics"
] | |
NeurIPS
| 2,022
| 57,817
|
CL-LSG: Continual Learning via Learnable Sparse Growth
|
Continual learning (CL) has been developed to learn new tasks sequentially and perform knowledge transfer from the old tasks to the new ones without forgetting, which is well known as catastrophic forgetting. While recent structure-based learning methods show the capability of alleviating the forgetting problem, these methods require a complex learning process to gradually grow-and-prune of a full-size network for each task, which is inefficient. To address this problem and enable efficient network expansion for new tasks, to the best of our knowledge, we are the first to develop a learnable sparse growth (LSG) method, which explicitly optimizes the model growth to only select important and necessary channels for growing. Building on the LSG, we then propose CL-LSG, a novel end-to-end CL framework to grow the model for each new task dynamically and sparsely. Different from all previous structure-based CL methods that start from and then prune (i.e., two-step) a full-size network, our framework starts from a compact seed network with a much smaller size and grows to the necessary model size (i.e., one-step) for each task, which eliminates the need of additional pruning in previous structure-based growing methods.
|
[
"Continual Learning",
"Neural Networks",
"Model Optimization",
"Sparse Learning"
] | |
ICML
| 2,024
| 34,218
|
Matroid Semi-Bandits in Sublinear Time
|
We study the matroid semi-bandits problem, where at each round the learner plays a subset of $K$ arms from a feasible set, and the goal is to maximize the expected cumulative linear rewards. Existing algorithms have per-round time complexity at least $\Omega(K)$, which becomes expensive when $K$ is large. To address this computational issue, we propose FasterCUCB whose sampling rule takes time sublinear in $K$ for common classes of matroids: $\mathcal{O}(D\text{ polylog}(K)\text{ polylog}(T))$ for uniform matroids, partition matroids, and graphical matroids, and $\mathcal{O}(D\sqrt{K}\text{ polylog}(T))$ for transversal matroids. Here, $D$ is the maximum number of elements in any feasible subset of arms, and $T$ is the horizon. Our technique is based on dynamic maintenance of an approximate maximum-weight basis over inner-product weights. Although the introduction of an approximate maximum-weight basis presents a challenge in regret analysis, we can still guarantee an upper bound on regret as tight as CUCB in the sense that it matches the gap-dependent lower bound by Kveton et al. (2014a) asymptotically.
|
[
"Algorithms",
"Combinatorial Optimization",
"Online Learning",
"Theoretical Computer Science"
] | |
NeurIPS
| 2,022
| 61,765
|
Learning Causal Representations of Single Cells via Sparse Mechanism Shift Modeling
|
Latent variable models have become a go-to tool for analyzing biological data, especially in the field of single-cell genomics. One remaining challenge is the identification of individual latent variables related to biological pathways, more generally conceptualized as disentanglement. Although versions of variational autoencoders that explicitly promote disentanglement were introduced and applied to single-cell genomics data, the theoretical feasibility of disentanglement from independent and identically distributed measurements has been challenged.Recent methods propose instead to leverage non-stationary data, as well as the sparse mechanism assumption in order to learn disentangled representations, with a causal semantic. Here, we explore the application of these methodological advances in the analysis of single-cell genomics data with genetic or chemical perturbations. We benchmark these methods on simulated single cell expression data to evaluate their performance regarding disentanglement, causal target identification and out-of-domain generalisation. Finally, by applying the approaches to a large-scale gene perturbation dataset, we find that the model relying on the sparse mechanism shift hypothesis surpasses contemporary methods on a transfer learning task.
|
[
"Computational Biology",
"Single-Cell Genomics",
"Causal Inference",
"Bioinformatics"
] | |
NeurIPS
| 2,023
| 70,649
|
On the choice of Perception Loss Function for Learned Video Compression
|
We study causal, low-latency, sequential video compression when the output is subjected to both a mean squared-error (MSE) distortion loss as well as a perception loss to target realism. Motivated by prior approaches, we consider two different perception loss functions (PLFs). The first, PLF-JD, considers the joint distribution (JD) of all the video frames up to the current one, while the second metric, PLF-FMD, considers the framewise marginal distributions (FMD) between the source and reconstruction. Using information theoretic analysis and deep-learning based experiments, we demonstrate that the choice of PLF can have a significant effect on the reconstruction, especially at low-bit rates. In particular, while the reconstruction based on PLF-JD can better preserve the temporal correlation across frames, it also imposes a significant penalty in distortion compared to PLF-FMD and further makes it more difficult to recover from errors made in the earlier output frames. Although the choice of PLF decisively affects reconstruction quality, we also demonstrate that it may not be essential to commit to a particular PLF during encoding and the choice of PLF can be delegated to the decoder. In particular, encoded representations generated by training a system to minimize the MSE (without requiring either PLF) can be {\em near universal} and can generate close to optimal reconstructions for either choice of PLF at the decoder. We validate our results using (one-shot) information-theoretic analysis, detailed study of the rate-distortion-perception tradeoff of the Gauss-Markov source model as well as deep-learning based experiments on moving MNIST and KTH datasets.
|
[
"Video Compression",
"Perception Loss",
"Information Theory",
"Deep Learning",
"Rate-Distortion Theory"
] | |
ICLR
| 2,024
| 19,273
|
Hyper Evidential Deep Learning to Quantify Composite Classification Uncertainty
|
Deep neural networks (DNNs) have been shown to perform well on exclusive, multi-class classification tasks. However, when different classes have similar visual features, it becomes challenging for human annotators to differentiate them. When an image is ambiguous, such as a blurry one where an annotator can't distinguish between a husky and a wolf, it may be labeled with both classes: {husky, wolf}. This scenario necessitates the use of composite set labels. In this paper, we propose a novel framework called Hyper-Evidential Neural Network (HENN) that explicitly models predictive uncertainty caused by composite set labels in training data in the context of the belief theory called Subjective Logic (SL).By placing a Grouped Dirichlet distribution on the class probabilities, we treat predictions of a neural network as parameters of hyper-subjective opinions and learn the network that collects both single and composite evidence leading to these hyper-opinions by a deterministic DNN from data.We introduce a new uncertainty type called vagueness originally designed for hyper-opinions in SL to quantify composite classification uncertainty for DNNs.Our experiments prove that HENN outperforms its state-of-the-art counterparts based on four image datasets.The code and datasets are available at: https://shorturl.at/dhoqx.
|
[
"Deep Learning",
"Uncertainty Quantification",
"Computer Vision",
"Neural Networks",
"Belief Theory",
"Subjective Logic"
] | |
NeurIPS
| 2,023
| 72,761
|
Decision-Aware Actor-Critic with Function Approximation and Theoretical Guarantees
|
Actor-critic (AC) methods are widely used in reinforcement learning (RL), and benefit from the flexibility of using any policy gradient method as the actor and value-based method as the critic. The critic is usually trained by minimizing the TD error, an objective that is potentially decorrelated with the true goal of achieving a high reward with the actor. We address this mismatch by designing a joint objective for training the actor and critic in a decision-aware fashion. We use the proposed objective to design a generic, AC algorithm that can easily handle any function approximation. We explicitly characterize the conditions under which the resulting algorithm guarantees monotonic policy improvement, regardless of the choice of the policy and critic parameterization. Instantiating the generic algorithm results in an actor that involves maximizing a sequence of surrogate functions (similar to TRPO, PPO), and a critic that involves minimizing a closely connected objective. Using simple bandit examples, we provably establish the benefit of the proposed critic objective over the standard squared error. Finally, we empirically demonstrate the benefit of our decision-aware actor-critic framework on simple RL problems.
|
[
"Reinforcement Learning",
"Actor-Critic Methods",
"Function Approximation",
"Policy Gradient Methods",
"Theoretical Guarantees"
] | |
ICML
| 2,024
| 32,790
|
UGrid: An Efficient-And-Rigorous Neural Multigrid Solver for Linear PDEs
|
Numerical solvers of Partial Differential Equations (PDEs) are of fundamental significance to science and engineering. To date, the historical reliance on legacy techniques has circumscribed possible integration of big data knowledge and exhibits sub-optimal efficiency for certain PDE formulations, while data-driven neural methods typically lack mathematical guarantee of convergence and correctness. This paper articulates a mathematically rigorous neural solver for linear PDEs. The proposed UGrid solver, built upon the principled integration of U-Net and MultiGrid, manifests a mathematically rigorous proof of both convergence and correctness, and showcases high numerical accuracy, as well as strong generalization power to various input geometry/values and multiple PDE formulations. In addition, we devise a new residual loss metric, which enables unsupervised training and affords more stability and a larger solution space over the legacy losses.
|
[
"Computational Mathematics",
"Numerical Analysis",
"Machine Learning for Scientific Computing",
"Partial Differential Equations",
"Scientific Computing"
] | |
NeurIPS
| 2,023
| 76,126
|
Unraveling the Mysteries of Galaxy Clusters: Recurrent Inference Deconvolution of X-ray Spectra
|
In the realm of X-ray spectral analysis, the true nature of spectra has remained elusive, as observed spectra have long been the outcome of convolution between instrumental response functions and intrinsic spectra. In this study, we employ a recurrent neural network framework, the Recurrent Inference Machine (RIM), to achieve the unprecedented deconvolution of intrinsic spectra from instrumental response functions. Our RIM model is meticulously trained on cutting-edge thermodynamic models and authentic response matrices sourced from the Chandra X-ray Observatory archive. Demonstrating remarkable accuracy, our model successfully reconstructs intrinsic spectra well below the 1-$\sigma$ error level. We showcase the practical application of this novel approach through real Chandra observations of the galaxy cluster Abell 1550—a vital calibration target for the recently launched X-ray telescope, XRISM This pioneering work marks a significant stride in the domain of X-ray spectral analysis, offering a promising avenue for unlocking hitherto concealed insights into spectra.
|
[
"Astrophysics",
"X-ray Astronomy",
"Galaxy Clusters",
"Spectral Analysis"
] | |
NeurIPS
| 2,022
| 57,501
|
Explaining complex system of multivariate times series behavior
|
Complex systems represented by multivariate time series are ubiquitous in many applications, especially in industry. Understanding a complex system, its states and their evolution over time is a challenging task. This is due to the permanent change of contextual events internal and external to the system. We are interested in representing the evolution of a complex system in an intelligible and explainable way based on knowledge extraction. We propose XR-CSB (eXplainable Representation of Complex System Behavior) based on three steps: (i) a time series vertical clustering to detect system states, (ii) an explainable visual representation using unfolded finite-state automata and (iii) an explainable pre-modeling based on an enrichment via exploratory metrics. Four representations adapted to the expertise level of domain experts for acceptability issues areproposed. Experiments show that XR-CSB is scalable. Qualitative evaluation by experts of different expertise levels shows that XR-CSB meets their expectations in terms of explainability, intelligibility and acceptability
|
[
"Complex Systems",
"Multivariate Time Series Analysis",
"Explainable AI ",
"Data Visualization",
"Knowledge Extraction",
"Industrial Applications",
"Machine Learning for Time Series"
] | |
ICLR
| 2,024
| 18,712
|
EasyTPP: Towards Open Benchmarking Temporal Point Processes
|
Continuous-time event sequences play a vital role in real-world domains such as healthcare, finance, online shopping, social networks, and so on. To model such data, temporal point processes (TPPs) have emerged as the most natural and competitive models, making a significant impact in both academic and application communities. Despite the emergence of many powerful models in recent years, there hasn't been a central benchmark for these models and future research endeavors. This lack of standardization impedes researchers and practitioners from comparing methods and reproducing results, potentially slowing down progress in this field. In this paper, we present EasyTPP, the first central repository of research assets (e.g., data, models, evaluation programs, documentations) in the area of event sequence modeling. Our EasyTPP makes several unique contributions to this area: a unified interface of using existing datasets and adding new datasets; a wide range of evaluation programs that are easy to use and extend as well as facilitate reproducible research; implementations of popular neural TPPs, together with a rich library of modules by composing which one could quickly build complex models. We will actively maintain this benchmark and welcome contributions from other researchers and practitioners. Our benchmark will help promote reproducible research in this field, thus accelerating research progress as well as making more significant real-world impacts. The code and data are available at \url{https://github.com/ant-research/EasyTemporalPointProcess}.
|
[
"Temporal Point Processes",
"Benchmarking",
"Event Sequence Modeling",
"Reproducible Research",
"Data Science",
"Computational Modeling"
] | |
NeurIPS
| 2,023
| 73,465
|
SubseasonalClimateUSA: A Dataset for Subseasonal Forecasting and Benchmarking
|
Subseasonal forecasting of the weather two to six weeks in advance is critical for resource allocation and climate adaptation but poses many challenges for the forecasting community. At this forecast horizon, physics-based dynamical models have limited skill, and the targets for prediction depend in a complex manner on both local weather variables and global climate variables. Recently, machine learning methods have shown promise in advancing the state of the art but only at the cost of complex data curation, integrating expert knowledge with aggregation across multiple relevant data sources, file formats, and temporal and spatial resolutions.To streamline this process and accelerate future development, we introduce SubseasonalClimateUSA, a curated dataset for training and benchmarking subseasonal forecasting models in the United States. We use this dataset to benchmark a diverse suite of models, including operational dynamical models, classical meteorological baselines, and ten state-of-the-art machine learning and deep learning-based methods from the literature. Overall, our benchmarks suggest simple and effective ways to extend the accuracy of current operational models. SubseasonalClimateUSA is regularly updated and accessible via the https://github.com/microsoft/subseasonal_data/ Python package.
|
[
"Subseasonal Forecasting",
"Climate Science",
"Meteorology",
"Machine Learning in Climate",
"Data Science for Climate",
"Environmental Data Analysis"
] | |
ICML
| 2,024
| 33,254
|
Accelerating Transformer Pre-training with 2:4 Sparsity
|
Training large transformers is slow, but recent innovations on GPU architecture give us an advantage. NVIDIA Ampere GPUs can execute a fine-grained 2:4 sparse matrix multiplication twice as fast as its dense equivalent. In the light of this property, we comprehensively investigate the feasibility of accelerating feed-forward networks (FFNs) of transformers in pre-training. First, we define a ``flip rate'' to monitor the stability of a 2:4 training process. Utilizing this metric, we propose three techniques to preserve accuracy: to modify the sparse-refined straight-through estimator by applying the masked decay term on gradients, to determine a feasible decay factor in warm-up stage, and to enhance the model's quality by a dense fine-tuning procedure near the end of pre-training. Besides, we devise two techniques to practically accelerate training: to calculate transposable 2:4 masks by convolution, and to accelerate gated activation functions by reducing GPU L2 cache miss. Experiments show that our 2:4 sparse training algorithm achieves similar convergence to dense training algorithms on several transformer pre-training tasks, while actual acceleration can be observed on different shapes of transformer block apparently. Our toolkit is available at https://github.com/huyz2023/2by4-pretrain.
|
[
"Natural Language Processing",
"Deep Learning",
"Neural Networks",
"GPU Computing",
"Model Optimization",
"Sparse Matrix Computation"
] | |
ICLR
| 2,024
| 19,089
|
Self-supervised Representation Learning from Random Data Projectors
|
Self-supervised representation learning (SSRL) has advanced considerably by exploiting the transformation invariance assumption under artificially designed data augmentations. While augmentation-based SSRL algorithms push the boundaries of performance in computer vision and natural language processing, they are often not directly applicable to other data modalities, and can conflict with application-specific data augmentation constraints. This paper presents an SSRL approach that can be applied to any data modality and network architecture because it does not rely on augmentations or masking. Specifically, we show that high-quality data representations can be learned by reconstructing random data projections. We evaluate the proposed approach on a wide range of representation learning tasks that span diverse modalities and real-world applications. We show that it outperforms multiple state-of-the-art SSRL baselines. Due to its wide applicability and strong empirical results, we argue that learning from randomness is a fruitful research direction worthy of attention and further study.
|
[
"Self-supervised Learning",
"Representation Learning",
"Computer Vision",
"Natural Language Processing",
"Multimodal Learning"
] | |
NeurIPS
| 2,023
| 70,565
|
Learn to Categorize or Categorize to Learn? Self-Coding for Generalized Category Discovery
|
In the quest for unveiling novel categories at test time, we confront the inherent limitations of traditional supervised recognition models that are restricted by a predefined category set. While strides have been made in the realms of self-supervised and open-world learning towards test-time category discovery, a crucial yet often overlooked question persists: what exactly delineates a category? In this paper, we conceptualize a category through the lens of optimization, viewing it as an optimal solution to a well-defined problem. Harnessing this unique conceptualization, we propose a novel, efficient and self-supervised method capable of discovering previously unknown categories at test time. A salient feature of our approach is the assignment of minimum length category codes to individual data instances, which encapsulates the implicit category hierarchy prevalent in real-world datasets. This mechanism affords us enhanced control over category granularity, thereby equipping our model to handle fine-grained categories adeptly. Experimental evaluations, bolstered by state-of-the-art benchmark comparisons, testify to the efficacy of our solution in managing unknown categories at test time. Furthermore, we fortify our proposition with a theoretical foundation, providing proof of its optimality. Our code is available at: https://github.com/SarahRastegar/InfoSieve.
|
[
"Self-Supervised Learning",
"Open-World Learning",
"Category Discovery",
"Computer Vision",
"Pattern Recognition"
] | |
NeurIPS
| 2,023
| 71,040
|
This Looks Like Those: Illuminating Prototypical Concepts Using Multiple Visualizations
|
We present ProtoConcepts, a method for interpretable image classification combining deep learning and case-based reasoning using prototypical parts. Existing work in prototype-based image classification uses a "this looks like that'' reasoning process, which dissects a test image by finding prototypical parts and combining evidence from these prototypes to make a final classification. However, all of the existing prototypical part-based image classifiers provide only one-to-one comparisons, where a single training image patch serves as a prototype to compare with a part of our test image. With these single-image comparisons, it can often be difficult to identify the underlying concept being compared (e.g., "is it comparing the color or the shape?''). Our proposed method modifies the architecture of prototype-based networks to instead learn prototypical concepts which are visualized using multiple image patches. Having multiple visualizations of the same prototype allows us to more easily identify the concept captured by that prototype (e.g., "the test image and the related training patches are all the same shade of blue''), and allows our model to create richer, more interpretable visual explanations. Our experiments show that our ``this looks like those'' reasoning process can be applied as a modification to a wide range of existing prototypical image classification networks while achieving comparable accuracy on benchmark datasets.
|
[
"Computer Vision",
"Deep Learning",
"Interpretable AI",
"Image Classification"
] | |
NeurIPS
| 2,023
| 71,221
|
Collapsed Inference for Bayesian Deep Learning
|
Bayesian neural networks (BNNs) provide a formalism to quantify and calibrate uncertainty in deep learning. Current inference approaches for BNNs often resort to few-sample estimation for scalability, which can harm predictive performance, while its alternatives tend to be computationally prohibitively expensive. We tackle this challenge by revealing a previously unseen connection between inference on BNNs and volume computation problems. With this observation, we introduce a novel collapsed inference scheme that performs Bayesian model averaging using collapsed samples. It improves over a Monte-Carlo sample by limiting sampling to a subset of the network weights while pairing it with some closed-form conditional distribution over the rest. A collapsed sample represents uncountably many models drawn from the approximate posterior and thus yields higher sample efficiency. Further, we show that the marginalization of a collapsed sample can be solved analytically and efficiently despite the non-linearity of neural networks by leveraging existing volume computation solvers. Our proposed use of collapsed samples achieves a balance between scalability and accuracy. On various regression and classification tasks, our collapsed Bayesian deep learning approach demonstrates significant improvements over existing methods and sets a new state of the art in terms of uncertainty estimation as well as predictive performance.
|
[
"Bayesian Deep Learning",
"Uncertainty Quantification",
"Neural Networks",
"Inference Methods",
"Computational Statistics"
] | |
ICML
| 2,024
| 37,518
|
Mimicking User Data: On Mitigating Fine-Tuning Risks in Closed Large Language Models
|
Fine-tuning large language models on task-specific datasets can enhance their performance on downstream tasks. However, recent research shows that fine-tuning on benign, instruction-following data can inadvertently undo safety alignment and increase a model's propensity to comply with harmful queries. Although critical, understanding and mitigating safety risks in well-defined tasks remains distinct from the instruction-following context due to structural differences in the data. Our work explores the risks associated with fine-tuning closed source models across diverse task-specific data. We demonstrate how malicious actors can subtly manipulate the structure of almostanytask-specific dataset to foster significantly more dangerous model behaviors, while maintaining an appearance of innocuity and reasonable downstream task performance. To mitigate this issue, we propose a novel strategy that mixes in safety data whichmimicsthe format and style of the user data, showing this is more effective than the baselines at re-establishing safety while maintaining similar task performance.
|
[
"Artificial Intelligence Safety",
"Machine Learning Security",
"Natural Language Processing",
"Model Fine-Tuning",
"Data Privacy and Security"
] | |
ICML
| 2,023
| 25,044
|
MEWL: Few-shot multimodal word learning with referential uncertainty
|
Without explicit feedback, humans can rapidly learn the meaning of words. Children can acquire a new word after just a few passive exposures, a process known as fast mapping. This word learning capability is believed to be the most fundamental building block of multimodal understanding and reasoning. Despite recent advancements in multimodal learning, a systematic and rigorous evaluation is still missing for human-like word learning in machines. To fill in this gap, we introduce the MachinE Word Learning (MEWL) benchmark to assess how machines learn word meaning in grounded visual scenes. MEWL covers human's core cognitive toolkits in word learning: cross-situational reasoning, bootstrapping, and pragmatic learning. Specifically, MEWL is a few-shot benchmark suite consisting of nine tasks for probing various word learning capabilities. These tasks are carefully designed to be aligned with the children's core abilities in word learning and echo the theories in the developmental literature. By evaluating multimodal and unimodal agents' performance with a comparative analysis of human performance, we notice a sharp divergence in human and machine word learning. We further discuss these differences between humans and machines and call for human-like few-shot word learning in machines.
|
[
"Multimodal Learning",
"Natural Language Processing",
"Cognitive Science",
"Developmental Psychology"
] | |
ICLR
| 2,024
| 18,828
|
SyncDreamer: Generating Multiview-consistent Images from a Single-view Image
|
In this paper, we present a novel diffusion model called SyncDreamer that generates multiview-consistent images from a single-view image. Using pretrained large-scale 2D diffusion models, recent work Zero123 demonstrates the ability to generate plausible novel views from a single-view image of an object. However, maintaining consistency in geometry and colors for the generated images remains a challenge. To address this issue, we propose a synchronized multiview diffusion model that models the joint probability distribution of multiview images, enabling the generation of multiview-consistent images in a single reverse process. SyncDreamer synchronizes the intermediate states of all the generated images at every step of the reverse process through a 3D-aware feature attention mechanism that correlates the corresponding features across different views. Experiments show that SyncDreamer generates images with high consistency across different views, thus making it well-suited for various 3D generation tasks such as novel-view-synthesis, text-to-3D, and image-to-3D. Project page: https://liuyuan-pal.github.io/SyncDreamer/.
|
[
"Computer Vision",
"Image Synthesis",
"3D Reconstruction",
"Generative Models"
] | |
ICML
| 2,023
| 24,959
|
Shedding a PAC-Bayesian Light on Adaptive Sliced-Wasserstein Distances
|
The Sliced-Wasserstein distance (SW) is a computationally efficient and theoretically grounded alternative to the Wasserstein distance. Yet, the literature on its statistical properties -- or, more accurately, its generalization properties -- with respect to the distribution of slices, beyond the uniform measure, is scarce. To bring new contributions to this line of research, we leverage the PAC-Bayesian theory and a central observation that SW may be interpreted as an average risk, the quantity PAC-Bayesian bounds have been designed to characterize. We provide three types of results: i) PAC-Bayesian generalization bounds that hold on what we refer as adaptive Sliced-Wasserstein distances, i.e. SW defined with respect to arbitrary distributions of slices (among which data-dependent distributions), ii) a principled procedure to learn the distribution of slices that yields maximally discriminative SW, by optimizing our theoretical bounds, and iii) empirical illustrations of our theoretical findings.
|
[
"Statistical Learning Theory",
"Optimal Transport",
"PAC-Bayesian Theory"
] | |
NeurIPS
| 2,023
| 71,313
|
Quantum Bayesian Optimization
|
Kernelized bandits, also known as Bayesian optimization (BO), has been a prevalent method for optimizing complicated black-box reward functions. Various BO algorithms have been theoretically shown to enjoy upper bounds on their cumulative regret which are sub-linear in the number $T$ of iterations, and a regret lower bound of $\Omega(\sqrt{T})$ has been derived which represents the unavoidable regrets for any classical BO algorithm. Recent works on quantum bandits have shown that with the aid of quantum computing, it is possible to achieve tighter regret upper bounds better than their corresponding classical lower bounds. However, these works are restricted to either multi-armed or linear bandits, and are hence not able to solve sophisticated real-world problems with non-linear reward functions. To this end, we introduce the quantum-Gaussian process-upper confidence bound (Q-GP-UCB) algorithm. To the best of our knowledge, our Q-GP-UCB is the first BO algorithm able to achieve a regret upper bound of $\mathcal{O}(\text{poly}\log T)$, which is significantly smaller than its regret lower bound of $\Omega(\sqrt{T})$ in the classical setting. Moreover, thanks to our novel analysis of the confidence ellipsoid, our Q-GP-UCB with the linear kernel achieves a smaller regret than the quantum linear UCB algorithm from the previous work. We use simulations, as well as an experiment using a real quantum computer, to verify that the theoretical quantum speedup achieved by our Q-GP-UCB is also potentially relevant in practice.
|
[
"Quantum Computing",
"Bayesian Optimization",
"Quantum Algorithms",
"Optimization Theory"
] | |
NeurIPS
| 2,022
| 52,843
|
Multi-Game Decision Transformers
|
A longstanding goal of the field of AI is a method for learning a highly capable, generalist agent from diverse experience. In the subfields of vision and language, this was largely achieved by scaling up transformer-based models and training them on large, diverse datasets. Motivated by this progress, we investigate whether the same strategy can be used to produce generalist reinforcement learning agents. Specifically, we show that a single transformer-based model – with a single set of weights – trained purely offline can play a suite of up to 46 Atari games simultaneously at close-to-human performance. When trained and evaluated appropriately, we find that the same trends observed in language and vision hold, including scaling of performance with model size and rapid adaptation to new games via fine-tuning. We compare several approaches in this multi-game setting, such as online and offline RL methods and behavioral cloning, and find that our Multi-Game Decision Transformer models offer the best scalability and performance. We release the pre-trained models and code to encourage further research in this direction.
|
[
"Reinforcement Learning",
"Deep Learning",
"Multi-Task Learning",
"Game AI"
] | |
ICLR
| 2,024
| 17,548
|
Consistency Models as a Rich and Efficient Policy Class for Reinforcement Learning
|
Score-based generative models like the diffusion model have been testified to be effective in modeling multi-modal data from image generation to reinforcement learning (RL). However, the inference process of diffusion model can be slow, which hinders its usage in RL with iterative sampling. We propose to apply the consistency model as an efficient yet expressive policy representation, namely consistency policy, with an actor-critic style algorithm for three typical RL settings: offline, offline-to-online and online. For offline RL, we demonstrate the expressiveness of generative models as policies from multi-modal data. For offline-to-online RL, the consistency policy is shown to be more computational efficient than diffusion policy, with a comparable performance. For online RL, the consistency policy demonstrates significant speedup and even higher average performances than the diffusion policy.
|
[
"Reinforcement Learning",
"Generative Models",
"Machine Learning Algorithms"
] | |
ICML
| 2,023
| 23,669
|
Counterfactual Identifiability of Bijective Causal Models
|
We study counterfactual identifiability in causal models with bijective generation mechanisms (BGM), a class that generalizes several widely-used causal models in the literature. We establish their counterfactual identifiability for three common causal structures with unobserved confounding, and propose a practical learning method that casts learning a BGM as structured generative modeling. Learned BGMs enable efficient counterfactual estimation and can be obtained using a variety of deep conditional generative models. We evaluate our techniques in a visual task and demonstrate its application in a real-world video streaming simulation task.
|
[
"Causal Inference",
"Generative Models",
"Counterfactual Analysis",
"Unobserved Confounding"
] | |
NeurIPS
| 2,022
| 53,442
|
On the relationship between variational inference and auto-associative memory
|
In this article, we propose a variational inference formulation of auto-associative memories, allowing us to combine perceptual inference and memory retrieval into the same mathematical framework. In this formulation, the prior probability distribution onto latent representations is made memory dependent, thus pulling the inference process towards previously stored representations. We then study how different neural network approaches to variational inference can be applied in this framework. We compare methods relying on amortized inference such as Variational Auto Encoders and methods relying on iterative inference such as Predictive Coding and suggest combining both approaches to design new auto-associative memory models. We evaluate the obtained algorithms on the CIFAR10 and CLEVR image datasets and compare them with other associative memory models such as Hopfield Networks, End-to-End Memory Networks and Neural Turing Machines.
|
[
"Neural Networks",
"Variational Inference",
"Auto-associative Memory",
"Cognitive Computing"
] | |
NeurIPS
| 2,023
| 71,962
|
Three Towers: Flexible Contrastive Learning with Pretrained Image Models
|
We introduce Three Towers (3T), a flexible method to improve the contrastive learning of vision-language models by incorporating pretrained image classifiers. While contrastive models are usually trained from scratch, LiT (Zhai et al., 2022) has recently shown performance gains from using pretrained classifier embeddings. However, LiT directly replaces the image tower with the frozen embeddings, excluding any potential benefits from training the image tower contrastively. With 3T, we propose a more flexible strategy that allows the image tower to benefit from both pretrained embeddings and contrastive training. To achieve this, we introduce a third tower that contains the frozen pretrained embeddings, and we encourage alignment between this third tower and the main image-text towers. Empirically, 3T consistently improves over LiT and the CLIP-style from-scratch baseline for retrieval tasks. For classification, 3T reliably improves over the from-scratch baseline, and while it underperforms relative to LiT for JFT-pretrained models, it outperforms LiT for ImageNet-21k and Places365 pretraining.
|
[
"Computer Vision",
"Contrastive Learning",
"Vision-Language Models",
"Transfer Learning"
] | |
NeurIPS
| 2,022
| 58,126
|
Motley: Benchmarking Heterogeneity and Personalization in Federated Learning
|
Personalized federated learning considers learning models unique to each client in a heterogeneous network. The resulting client-specific models have been purported to improve metrics such as accuracy, fairness, and robustness in federated networks. However, despite a plethora of work in this area, it remains unclear: (1) which personalization techniques are most effective in various settings, and (2) how important personalization truly is for realistic federated applications. To better answer these questions, we propose Motley, a benchmark for personalized federated learning. Motley consists of a suite of cross-device and cross-silo federated datasets from varied problem domains, as well as thorough evaluation metrics for better understanding the possible impacts of personalization. We establish baselines on the benchmark by comparing a number of representative personalized federated learning methods. These initial results highlight strengths and weaknesses of existing approaches, and raise several open questions for the community. Motley aims to provide a reproducible means with which to advance developments in personalized and heterogeneity-aware federated learning, as well as the related areas of transfer learning, meta-learning, and multi-task learning.
|
[
"Federated Learning",
"Personalized Learning",
"Machine Learning Benchmarks",
"Heterogeneous Networks",
"Transfer Learning",
"Meta-Learning",
"Multi-Task Learning"
] | |
NeurIPS
| 2,023
| 71,949
|
On Dynamic Programming Decompositions of Static Risk Measures in Markov Decision Processes
|
Optimizing static risk-averse objectives in Markov decision processes is difficult because they do not admit standard dynamic programming equations common in Reinforcement Learning (RL) algorithms. Dynamic programming decompositions that augment the state space with discrete risk levels have recently gained popularity in the RL community. Prior work has shown that these decompositions are optimal when the risk level is discretized sufficiently. However, we show that these popular decompositions for Conditional-Value-at-Risk (CVaR) and Entropic-Value-at-Risk (EVaR) are inherently suboptimal regardless of the discretization level. In particular, we show that a saddle point property assumed to hold in prior literature may be violated. However, a decomposition does hold for Value-at-Risk and our proof demonstrates how this risk measure differs from CVaR and EVaR. Our findings are significant because risk-averse algorithms are used in high-stake environments, making their correctness much more critical.
|
[
"Reinforcement Learning",
"Markov Decision Processes",
"Risk Management",
"Dynamic Programming",
"Optimization"
] | |
ICLR
| 2,022
| 6,038
|
PiCO: Contrastive Label Disambiguation for Partial Label Learning
|
Partial label learning (PLL) is an important problem that allows each training example to be labeled with a coarse candidate set, which well suits many real-world data annotation scenarios with label ambiguity. Despite the promise, the performance of PLL often lags behind the supervised counterpart. In this work, we bridge the gap by addressing two key research challenges in PLL---representation learning and label disambiguation---in one coherent framework. Specifically, our proposed framework PiCO consists of a contrastive learning module along with a novel class prototype-based label disambiguation algorithm. PiCO produces closely aligned representations for examples from the same classes and facilitates label disambiguation. Theoretically, we show that these two components are mutually beneficial, and can be rigorously justified from an expectation-maximization (EM) algorithm perspective. Extensive experiments demonstrate that PiCO significantly outperforms the current state-of-the-art approaches in PLL and even achieves comparable results to fully supervised learning. Code and data available: https://github.com/hbzju/PiCO.
|
[
"Representation Learning",
"Label Disambiguation",
"Contrastive Learning",
"Expectation-Maximization Algorithm"
] | |
ICML
| 2,023
| 24,534
|
Collaborative Multi-Agent Heterogeneous Multi-Armed Bandits
|
The study of collaborative multi-agent bandits has attracted significant attention recently. In light of this, we initiate the study of a new collaborative setting, consisting of $N$ agents such that each agent is learning one of $M$ stochastic multi-armed bandits to minimize their group cumulative regret. We develop decentralized algorithms which facilitate collaboration between the agents under two scenarios. We characterize the performance of these algorithms by deriving the per agent cumulative regret and group regret upper bounds. We also prove lower bounds for the group regret in this setting, which demonstrates the near-optimal behavior of the proposed algorithms.
|
[
"Multi-Agent Systems",
"Reinforcement Learning",
"Stochastic Processes"
] | |
ICML
| 2,023
| 25,888
|
Spectral Evolution and Invariance in Linear-width Neural Networks
|
We investigate the spectral properties of linear-width feed-forward neural networks, where the sample size is asymptotically proportional to network width. Empirically, we show that the spectra of weight in this high dimensional regime are invariant when trained by gradient descent for small constant learning rates; we provide a theoretical justification for this observation and prove the invariance of the bulk spectra for both conjugate and neural tangent kernel. When the learning rate is large, we exhibit the emergence of an outlier whose corresponding eigenvector is aligned with the training data structure. We also show that after adaptive gradient training, where a lower test error and feature learning emerge, both weight and kernel matrices exhibit heavy tail behavior. Simple examples are provided to explain when heavy tails can have better generalizations. Different spectral properties such as invariant bulk, spike, and heavy-tailed distribution correlate to how far the kernels deviate from initialization. To understand this phenomenon better, we show via a toy model that we can exhibit different spectral properties for different training strategies. Analogous phenomena also appear when we train conventional neural networks with real-world data. We conclude that monitoring the evolution of the spectra during training is an essential step toward understanding the training dynamics and feature learning.
|
[
"Neural Networks",
"Spectral Analysis",
"High-Dimensional Statistics",
"Training Dynamics"
] | |
ICML
| 2,023
| 28,177
|
A Novel Application of Conditional Normalizing Flows: Stellar Age Inference with Gyrochronology
|
Age data are critical building blocks of stellar evolutionary models, but challenging to measure for low mass main sequence (MS) stars. An unexplored solution in this regime is the application of probabilistic machine learning methods to gyrochronology, a stellar dating technique that is uniquely well suited for these stars. While accurate analytical gyrochronological models have eluded the field, here we demonstrate that a data-driven approach can be successful, by applying conditional normalizing flows to photometric data from open star clusters. We evaluate the flow results in the context of a Bayesian framework, and show that our inferred ages recover literature values well. This work demonstrates the potential of a probabilistic data-driven solution to significantly improve the effectiveness of gyrochronological stellar dating.
|
[
"Astrophysics",
"Stellar Evolution",
"Data Science",
"Bayesian Inference"
] | |
NeurIPS
| 2,023
| 74,719
|
Sampling Protein Language Models for Functional Protein Design
|
Protein language models have emerged as powerful ways to learn complex representations of proteins, thereby improving their performance on several downstream tasks, from structure prediction to fitness prediction, property prediction, homology detection, and more. By learning a distribution over protein sequences, they are also very promising tools for designing novel and functional proteins, with broad applications in healthcare, new material, or sustainability. Given the vastness of the corresponding sample space, efficient exploration methods are critical to the success of protein engineering efforts. However, the methodologies for adequately sampling these models to achieve core protein design objectives remain underexplored and have predominantly leaned on techniques developed for Natural Language Processing. In this work, we first develop a holistic in silico protein design evaluation framework, to comprehensively compare different sampling methods. After performing a thorough review of sampling methods for language models, we introduce several sampling strategies tailored to protein design. Lastly, we compare the various strategies on our in silico benchmark, investigating the effects of key hyperparameters and highlighting practical guidance on the relative strengths of different methods.
|
[
"Computational Biology",
"Bioinformatics",
"Protein Engineering",
"Machine Learning in Biology",
"Structural Biology",
"Synthetic Biology"
] | |
NeurIPS
| 2,022
| 53,103
|
Nonparametric Uncertainty Quantification for Single Deterministic Neural Network
|
This paper proposes a fast and scalable method for uncertainty quantification of machine learning models' predictions. First, we show the principled way to measure the uncertainty of predictions for a classifier based on Nadaraya-Watson's nonparametric estimate of the conditional label distribution. Importantly, the approach allows to disentangle explicitly \textit{aleatoric} and \textit{epistemic} uncertainties. The resulting method works directly in the feature space. However, one can apply it to any neural network by considering an embedding of the data induced by the network. We demonstrate the strong performance of the method in uncertainty estimation tasks on text classification problems and a variety of real-world image datasets, such as MNIST, SVHN, CIFAR-100 and several versions of ImageNet.
|
[
"Uncertainty Quantification",
"Neural Networks",
"Nonparametric Methods",
"Computer Vision",
"Text Classification"
] | |
ICLR
| 2,024
| 19,587
|
Spike-driven Transformer V2: Meta Spiking Neural Network Architecture Inspiring the Design of Next-generation Neuromorphic Chips
|
Neuromorphic computing, which exploits Spiking Neural Networks (SNNs) on neuromorphic chips, is a promising energy-efficient alternative to traditional AI. CNN-based SNNs are the current mainstream of neuromorphic computing. By contrast, no neuromorphic chips are designed especially for Transformer-based SNNs, which have just emerged, and their performance is only on par with CNN-based SNNs, offering no distinct advantage. In this work, we propose a general Transformer-based SNN architecture, termed as ``Meta-SpikeFormer", whose goals are: (1)Lower-power, supports the spike-driven paradigm that there is only sparse addition in the network; (2)Versatility, handles various vision tasks; (3)High-performance, shows overwhelming performance advantages over CNN-based SNNs; (4)Meta-architecture, provides inspiration for future next-generation Transformer-based neuromorphic chip designs. Specifically, we extend the Spike-driven Transformer in \citet{yao2023spike} into a meta architecture, and explore the impact of structure, spike-driven self-attention, and skip connection on its performance. On ImageNet-1K, Meta-SpikeFormer achieves 80.0\% top-1 accuracy (55M), surpassing the current state-of-the-art (SOTA) SNN baselines (66M) by 3.7\%. This is the first direct training SNN backbone that can simultaneously supports classification, detection, and segmentation, obtaining SOTA results in SNNs. Finally, we discuss the inspiration of the meta SNN architecture for neuromorphic chip design.
|
[
"Neuromorphic Computing",
"Spiking Neural Networks ",
"Transformer Architectures",
"Energy-efficient AI",
"Computer Vision",
"Machine Learning Hardware Design"
] | |
ICLR
| 2,023
| 11,804
|
Causality Compensated Attention for Contextual Biased Visual Recognition
|
Visual attention does not always capture the essential object representation desired for robust predictions. Attention modules tend to underline not only the target object but also the common co-occurring context that the module thinks helpful in the training. The problem is rooted in the confounding effect of the context leading to incorrect causalities between objects and predictions, which is further exacerbated by visual attention. In this paper, to learn causal object features robust for contextual bias, we propose a novel attention module named Interventional Dual Attention (IDA) for visual recognition. Specifically, IDA adopts two attention layers with multiple sampling intervention, which compensates the attention against the confounder context. Note that our method is model-agnostic and thus can be implemented on various backbones. Extensive experiments show our model obtains significant improvements in classification and detection with lower computation. In particular, we achieve the state-of-the-art results in multi-label classification on MS-COCO and PASCAL-VOC.
|
[
"Computer Vision",
"Visual Recognition",
"Attention Mechanisms",
"Causal Inference in AI"
] | |
NeurIPS
| 2,023
| 76,254
|
Gradient weighted physics-informed neural networks for capturing shocks in porous media flows
|
Physics-informed neural networks (PINNs) seamlessly integrate physical laws into machine learning models, enabling accurate simulations while preserving the underlying physics. However, PINNs are still suboptimal in approximating discontinuities in the form of shocks compared to the traditional numerical shock-capturing methods. This paper proposes a framework to approximate shocks arising in dynamic porous media flows by weighting the governing nonlinear partial differential equation (PDE) with a physical gradient-based term in the loss function. The applicability of the proposed framework is investigated on the forward problem of immiscible two-phase fluid transport in porous media governed by a nonlinear first-order hyperbolic Buckley–Leverett PDE. Particularly, convex and non-convex flux functions are studied involving shocks and rarefaction. The results demonstrate that the proposed framework consistently learns accurate approximations containing shocks and rarefaction by weighting the underlying PDE with a physical gradient term and outperforms state-of-the-art artificial viscosity-based neural network methods to capture shocks on the standard L2-norm metric.
|
[
"Computational Physics",
"Fluid Dynamics",
"Porous Media",
"Numerical Methods",
"Partial Differential Equations "
] | |
ICML
| 2,023
| 24,731
|
Information-Theoretic State Space Model for Multi-View Reinforcement Learning
|
Multi-View Reinforcement Learning (MVRL) seeks to find an optimal control for an agent given multi-view observations from various sources. Despite recent advances in multi-view learning that aim to extract the latent representation from multi-view data, it is not straightforward to apply them to control tasks, especially when the observations are temporally dependent on one another. The problem can be even more challenging if the observations are intermittently missing for a subset of views. In this paper, we introduce Fuse2Control (F2C), an information-theoretic approach to capturing the underlying state space model from the sequences of multi-view observations. We conduct an extensive set of experiments in various control tasks showing that our method is highly effective in aggregating task-relevant information across many views, that scales linearly with the number of views while retaining robustness to arbitrary missing view scenarios.
|
[
"Multi-View Learning",
"Reinforcement Learning",
"Information Theory",
"State Space Models",
"Control Systems"
] | |
ICLR
| 2,024
| 21,369
|
INTEGRAL PINNS FOR HYPERBOLIC CONSERVATION LAWS
|
Traditional physics-informed neural networks (PINNs) are trained based on differential equations and thus have difficulty capturing shock discontinuities in weak solutions of hyperbolic PDEs, since the differential equation doesn’t apply at the discontinuity. We propose Integral PINNs (IPINNs), which are trained based on the integral form of the conservation law, which holds at both continuous and discontinuous points of the solution. We use neural nets to model the integrals of the solution instead of the solution itself. We apply IPINNs to systems of hyperbolic conservation laws and show that they are much better at capturing the correct location and speed of shocks, compared to traditional PINNs. We also present a heuristic approach for detecting shock locations.
|
[
"Computational Mathematics",
"Numerical Analysis",
"Applied Mathematics",
"Partial Differential Equations "
] | |
ICML
| 2,024
| 32,847
|
Multi-Factor Adaptive Vision Selection for Egocentric Video Question Answering
|
The challenge of interpreting the world from a human perspective in Artificial Intelligence (AI) is particularly evident in egocentric video question answering, which grapples with issues like small object recognition, noise suppression, and spatial-temporal reasoning. To address these challenges, we introduce the Multi-Factor Adaptive vision Selection (MFAS) framework. MFAS integrates a patch partition and merging module for enhanced small object recognition, a prior-guided patch selection module for noise suppression and focused analysis, and a hierarchical aggregation network to aggregate visual semantics guided by questions. Extensive experiments on several public egocentric datasets have validated the effectiveness and generalization of our framework. Code and data are available in https://github.com/Hyu-Zhang/EgoVideoQA.
|
[
"Computer Vision",
"Egocentric Vision",
"Video Question Answering",
"Spatial-Temporal Reasoning"
] | |
ICLR
| 2,022
| 7,014
|
A Johnson-Lindenstrauss Framework for Randomly Initialized CNNs
|
How does the geometric representation of a dataset change after the application of each randomly initialized layer of a neural network? The celebrated Johnson-Lindenstrauss lemma answers this question for linear fully-connected neural networks (FNNs), stating that the geometry is essentially preserved. For FNNs with the ReLU activation, the angle between two input contracts according to a known mapping. The question for non-linear convolutional neural networks (CNNs) becomes much more intricate. To answer this question, we introduce a geometric framework. For linear CNNs, we show that the Johnson--Lindenstrauss lemma continues to hold, namely, that the angle between two inputs is preserved. For CNNs with ReLU activation, on the other hand, the behavior is richer: The angle between the outputs contracts, where the level of contraction depends on the nature of the inputs. In particular, after one layer, the geometry of natural images is essentially preserved, whereas for Gaussian correlated inputs, CNNs exhibit the same contracting behavior as FNNs with ReLU activation.
|
[
"Neural Networks",
"Convolutional Neural Networks",
"Geometric Representation",
"Johnson-Lindenstrauss Lemma"
] | |
NeurIPS
| 2,022
| 59,783
|
Rationale-aware Autonomous Driving Policy utilizing Safety Force Field implemented on CARLA Simulator
|
Despite the rapid improvement of autonomous driving technology in recent years, automotive manufacturers must resolve liability issues to commercialize autonomous passenger car of SAE J3016 Level 3 or higher. To cope with the product liability law, manufacturers develop autonomous driving systems in compliance with international standards for safety such as ISO 26262 and ISO 21448. Concerning the safety of the intended functionality (SOTIF) requirement in ISO 26262, the driving policy recommends providing an explicit rational basis for maneuver decisions. In this case, mathematical models such as Safety Force Field (SFF) and Responsibility-Sensitive Safety (RSS) which have interpretability on decision, may be suitable. In this work, we implement SFF from scratch to substitute the undisclosed NVIDIA's source code and integrate it with CARLA open-source simulator. Using SFF and CARLA, we present a predictor for claimed sets of vehicles, and based on the predictor, propose an integrated driving policy that consistently operates regardless of safety conditions it encounters while passing through dynamic traffic. The policy does not have a separate plan for each condition, but using safety potential, it aims human-like driving blended in with traffic flow.
|
[
"Autonomous Driving",
"Safety in Autonomous Systems",
"Automotive Safety Standards",
"Simulation and Modeling",
"Machine Learning in Transportation"
] | |
ICLR
| 2,023
| 11,723
|
LightGCL: Simple Yet Effective Graph Contrastive Learning for Recommendation
|
Graph neural network (GNN) is a powerful learning approach for graph-based recommender systems. Recently, GNNs integrated with contrastive learning have shown superior performance in recommendation with their data augmentation schemes, aiming at dealing with highly sparse data. Despite their success, most existing graph contrastive learning methods either perform stochastic augmentation (e.g., node/edge perturbation) on the user-item interaction graph, or rely on the heuristic-based augmentation techniques (e.g., user clustering) for generating contrastive views. We argue that these methods cannot well preserve the intrinsic semantic structures and are easily biased by the noise perturbation. In this paper, we propose a simple yet effective graph contrastive learning paradigm LightGCL that mitigates these issues impairing the generality and robustness of CL-based recommenders. Our model exclusively utilizes singular value decomposition for contrastive augmentation, which enables the unconstrained structural refinement with global collaborative relation modeling. Experiments conducted on several benchmark datasets demonstrate the significant improvement in performance of our model over the state-of-the-arts. Further analyses demonstrate the superiority of LightGCL's robustness against data sparsity and popularity bias. The source code of our model is available at https://github.com/HKUDS/LightGCL.
|
[
"Graph Neural Networks",
"Contrastive Learning",
"Recommender Systems",
"Data Augmentation"
] | |
ICML
| 2,023
| 24,220
|
Convergence of First-Order Methods for Constrained Nonconvex Optimization with Dependent Data
|
We focus on analyzing the classical stochastic projected gradient methods under a general dependent data sampling scheme for constrained smooth nonconvex optimization. We show the worst-case rate of convergence $\tilde{O}(t^{-1/4})$ and complexity $\tilde{O}(\varepsilon^{-4})$ for achieving an $\varepsilon$-near stationary point in terms of the norm of the gradient of Moreau envelope and gradient mapping. While classical convergence guarantee requires i.i.d. data sampling from the target distribution, we only require a mild mixing condition of the conditional distribution, which holds for a wide class of Markov chain sampling algorithms. This improves the existing complexity for the constrained smooth nonconvex optimization with dependent data from $\tilde{O}(\varepsilon^{-8})$ to $\tilde{O}(\varepsilon^{-4})$ with a significantly simpler analysis. We illustrate the generality of our approach by deriving convergence results with dependent data for stochastic proximal gradient methods, adaptive stochastic gradient algorithm AdaGrad and stochastic gradient algorithm with heavy ball momentum. As an application, we obtain first online nonnegative matrix factorization algorithms for dependent data based on stochastic projected gradient methods with adaptive step sizes and optimal rate of convergence.
|
[
"Optimization",
"Nonconvex Optimization",
"Stochastic Gradient Methods",
"Constrained Optimization",
"Machine Learning Algorithms",
"Numerical Analysis",
"Data-Dependent Algorithms"
] | |
ICML
| 2,024
| 33,109
|
Momentum Particle Maximum Likelihood
|
Maximum likelihood estimation (MLE) of latent variable models is often recast as the minimization of a free energy functional over an extended space of parameters and probability distributions. This perspective was recently combined with insights from optimal transport to obtain novel particle-based algorithms for fitting latent variable models to data. Drawing inspiration from prior works which interpret `momentum-enriched' optimization algorithms as discretizations of ordinary differential equations, we propose an analogous dynamical-systems-inspired approach to minimizing the free energy functional. The result is a dynamical system that blends elements of Nesterov's Accelerated Gradient method, the underdamped Langevin diffusion, and particle methods. Under suitable assumptions, we prove that the continuous-time system minimizes the functional. By discretizing the system, we obtain a practical algorithm for MLE in latent variable models. The algorithm outperforms existing particle methods in numerical experiments and compares favourably with other MLE algorithms.
|
[
"Optimization",
"Dynamical Systems",
"Statistical Methods",
"Computational Statistics"
] | |
ICML
| 2,024
| 34,045
|
How Far Can Fairness Constraints Help Recover From Biased Data?
|
A general belief in fair classification is that fairness constraints incur a trade-off with accuracy, which biased data may worsen. Contrary to this belief, Blum & Stangl (2019) show that fair classification with equal opportunity constraints even on extremely biased data can recover optimally accurate and fair classifiers on the original data distribution. Their result is interesting because it demonstrates that fairness constraints can implicitly rectify data bias and simultaneously overcome a perceived fairness-accuracy trade-off. Their data bias model simulates under-representation and label bias in underprivileged population, and they show the above result on a stylized data distribution with i.i.d. label noise, under simple conditions on the data distribution and bias parameters. We propose a general approach to extend the result of Blum & Stangl (2019) to different fairness constraints, data bias models, data distributions, and hypothesis classes. We strengthen their result, and extend it to the case when their stylized distribution has labels with Massart noise instead of i.i.d. noise. We prove a similar recovery result for arbitrary data distributions using fair reject option classifiers. We further generalize it to arbitrary data distributions and arbitrary hypothesis classes, i.e., we prove that for any data distribution, if the optimally accurate classifier in a given hypothesis class is fair and robust, then it can be recovered through fair classification with equal opportunity constraints on the biased distribution whenever the bias parameters satisfy certain simple conditions. Finally, we show applications of our technique to time-varying data bias in classification and fair machine learning pipelines.
|
[
"Fairness in Machine Learning",
"Bias Mitigation",
"Fair Classification",
"Data Bias and Fairness Constraints",
"Machine Learning Theory",
"Algorithmic Fairness"
] | |
ICML
| 2,024
| 33,627
|
Unbiased Multi-Label Learning from Crowdsourced Annotations
|
This work studies the novel Crowdsourced Multi-Label Learning (CMLL) problem, where each instance is related to multiple true labels but the model only receives unreliable labels from different annotators. Although a few Crowdsourced Multi-Label Inference (CMLI) methods have been developed, they require both the training and testing sets to be assigned crowdsourced labels and focus on true label inferring rather than prediction, making them less practical. In this paper, by excavating the generation process of crowdsourced labels, we establish the firstunbiased risk estimatorfor CMLL based on the crowdsourced transition matrices. To facilitate transition matrix estimation, we upgrade our unbiased risk estimator by aggregating crowdsourced labels and transition matrices from all annotators while guaranteeing its theoretical characteristics. Integrating with the unbiased risk estimator, we further propose a decoupled autoencoder framework to exploit label correlations and boost performance. We also provide a generalization error bound to ensure the convergence of the empirical risk estimator. Experiments on various CMLL scenarios demonstrate the effectiveness of our proposed method. The source code is available at https://github.com/MingxuanXia/CLEAR.
|
[
"Crowdsourcing",
"Multi-Label Learning",
"Data Annotation",
"Unsupervised Learning",
"Risk Estimation"
] | |
ICML
| 2,023
| 26,119
|
Rethinking Robust Contrastive Learning from the Adversarial Perspective
|
To advance the understanding of robust deep learning, we delve into the effects of adversarial training on self-supervised and supervised contrastive learning, alongside supervised learning. Our analysis uncovers significant disparities between adversarial and clean representations in standard-trained networks, across various learning algorithms. Remarkably, adversarial training mitigates these disparities and fosters the convergence of representations toward a universal set, regardless of the learning scheme used. Additionally, we observe that increasing the similarity between adversarial and clean representations, particularly near the end of the network, enhances network robustness. These findings offer valuable insights for designing and training effective and robust deep learning networks.
|
[
"Deep Learning",
"Adversarial Machine Learning",
"Contrastive Learning",
"Self-Supervised Learning",
"Robustness in Machine Learning"
] | |
ICML
| 2,022
| 16,307
|
Modeling Structure with Undirected Neural Networks
|
Neural networks are powerful function estimators, leading to their status as a paradigm of choice for modeling structured data. However, unlike other structured representations that emphasize the modularity of the problem – e.g., factor graphs – neural networks are usually monolithic mappings from inputs to outputs, with a fixed computation order. This limitation prevents them from capturing different directions of computation and interaction between the modeled variables. In this paper, we combine the representational strengths of factor graphs and of neural networks, proposing undirected neural networks (UNNs): a flexible framework for specifying computations that can be performed in any order. For particular choices, our proposed models subsume and extend many existing architectures: feed-forward, recurrent, self-attention networks, auto-encoders, and networks with implicit layers. We demonstrate the effectiveness of undirected neural architectures, both unstructured and structured, on a range of tasks: tree-constrained dependency parsing, convolutional image classification, and sequence completion with attention. By varying the computation order, we show how a single UNN can be used both as a classifier and a prototype generator, and how it can fill in missing parts of an input sequence, making them a promising field for further research.
|
[
"Neural Networks",
"Structured Data Modeling",
"Deep Learning Architectures",
"Computational Graphs"
] | |
NeurIPS
| 2,023
| 74,566
|
Adaptive Algorithms for Continuous-Time Transport: Homotopy-Driven Sampling and a New Interacting Particle System
|
We propose a new dynamic algorithm which transports samples from a reference distribution to a target distribution in unit time, given access to the target-to-reference density ratio. Our approach is to seek a sequence of transport maps that push forward the reference along a path given by a geometric mixture of the two densities. We take the maps to be simply parameterized, local, sample-driven optimal transport maps which we identify by approximately solving a root-finding problem formulated using importance weights. When feature functions for the maps are taken to be kernels, we obtain a novel interacting particle system from which we derive finite-particle and mean-field ODEs. In discrete time, we introduce an adaptive algorithm for simulating this interacting particle system which adjusts the ODE time steps based on the quality of the transport, automatically uncovering a good "schedule" for traversing the geometric mixture of densities.
|
[
"Computational Statistics",
"Optimal Transport",
"Stochastic Processes",
"Numerical Analysis",
"Dynamical Systems"
] | |
NeurIPS
| 2,022
| 55,147
|
LasUIE: Unifying Information Extraction with Latent Adaptive Structure-aware Generative Language Model
|
Universally modeling all typical information extraction tasks (UIE) with one generative language model (GLM) has revealed great potential by the latest study, where various IE predictions are unified into a linearized hierarchical expression under a GLM. Syntactic structure information, a type of effective feature which has been extensively utilized in IE community, should also be beneficial to UIE. In this work, we propose a novel structure-aware GLM, fully unleashing the power of syntactic knowledge for UIE. A heterogeneous structure inductor is explored to unsupervisedly induce rich heterogeneous structural representations by post-training an existing GLM. In particular, a structural broadcaster is devised to compact various latent trees into explicit high-order forests, helping to guide a better generation during decoding. We finally introduce a task-oriented structure fine-tuning mechanism, further adjusting the learned structures to most coincide with the end-task's need. Over 12 IE benchmarks across 7 tasks our system shows significant improvements over the baseline UIE system. Further in-depth analyses show that our GLM learns rich task-adaptive structural bias that greatly resolves the UIE crux, the long-range dependence issue and boundary identifying.
|
[
"Information Extraction",
"Natural Language Processing",
"Generative Language Models",
"Syntactic Structure Analysis"
] | |
ICML
| 2,022
| 16,291
|
Plan Your Target and Learn Your Skills: Transferable State-Only Imitation Learning via Decoupled Policy Optimization
|
Recent progress in state-only imitation learning extends the scope of applicability of imitation learning to real-world settings by relieving the need for observing expert actions.However, existing solutions only learn to extract a state-to-action mapping policy from the data, without considering how the expert plans to the target. This hinders the ability to leverage demonstrations and limits the flexibility of the policy.In this paper, we introduce Decoupled Policy Optimization (DePO), which explicitly decouples the policy as a high-level state planner and an inverse dynamics model. With embedded decoupled policy gradient and generative adversarial training, DePO enables knowledge transfer to different action spaces or state transition dynamics, and can generalize the planner to out-of-demonstration state regions.Our in-depth experimental analysis shows the effectiveness of DePO on learning a generalized target state planner while achieving the best imitation performance. We demonstrate the appealing usage of DePO for transferring across different tasks by pre-training, and the potential for co-training agents with various skills.
|
[
"Imitation Learning",
"Reinforcement Learning",
"Transfer Learning",
"Robotics"
] | |
NeurIPS
| 2,022
| 60,679
|
Bayesian Q-learning With Imperfect Expert Demonstrations
|
Guided exploration with expert demonstrations improves data efficiency for reinforcement learning, but current algorithms often overuse expert information. We propose a novel algorithm to speed up Q-learning with the help of a limited amount of imperfect expert demonstrations. The algorithm avoids excessive reliance on expert data by relaxing the optimal expert assumption and gradually reducing the usage of uninformative expert data. Experimentally, we evaluate our approach on a sparse-reward chain environment and six more complicated Atari games with delayed rewards. We can achieve better results with the proposed methods than Deep Q-learning from Demonstrations (Hester et al., 2017) in most environments.
|
[
"Reinforcement Learning",
"Bayesian Methods"
] | |
ICLR
| 2,022
| 6,452
|
Effect of scale on catastrophic forgetting in neural networks
|
Catastrophic forgetting presents a challenge in developing deep learning models capable of continual learning, i.e. learning tasks sequentially. Recently, both computer vision and natural-language processing have witnessed great progress through the use of large-scale pretrained models. In this work, we present an empirical study of catastrophic forgetting in this pretraining paradigm.Our experiments indicate that large, pretrained ResNets and Transformers are significantly more resistant to forgetting than randomly-initialized, trained-from-scratch models; this robustness systematically improves with scale of both model and pretraining dataset size.We take initial steps towards characterizing what aspect of model representations allows them to perform continual learning so well, finding that in the pretrained models, distinct class representations grow more orthogonal with scale. Our results suggest that, when possible, scale and a diverse pretraining dataset can be useful ingredients in mitigating catastrophic forgetting.
|
[
"Deep Learning",
"Continual Learning",
"Neural Networks",
"Catastrophic Forgetting",
"Transfer Learning",
"Computer Vision",
"Natural Language Processing"
] | |
ICML
| 2,024
| 38,220
|
Predictive Performance Comparison of Decision Policies Under Confounding
|
Predictive models are often introduced to decision-making tasks under the rationale that they improve performance over an existing decision-making policy. However, it is challenging to compare predictive performance against an existing decision-making policy that is generally under-specified and dependent on unobservable factors. These sources of uncertainty are often addressed in practice by making strong assumptions about the data-generating mechanism. In this work, we propose a method to compare the predictive performance of decision policies under a variety of modern identification approaches from the causal inference and off-policy evaluation literatures (e.g., instrumental variable, marginal sensitivity model, proximal variable). Key to our method is the insight that there are regions of uncertainty that we can safely ignore in the policy comparison. We develop a practical approach for finite-sample estimation of regret intervals under no assumptions on the parametric form of the status quo policy. We verify our framework theoretically and via synthetic data experiments. We conclude with a real-world application using our framework to support a pre-deployment evaluation of a proposed modification to a healthcare enrollment policy.
|
[
"Causal Inference",
"Decision-Making",
"Predictive Modeling",
"Off-Policy Evaluation",
"Healthcare Policy Evaluation"
] | |
ICML
| 2,024
| 34,214
|
Conditional Normalizing Flows for Active Learning of Coarse-Grained Molecular Representations
|
Efficient sampling of the Boltzmann distribution of molecular systems is a long-standing challenge. Recently, instead of generating long molecular dynamics simulations, generative machine learning methods such as normalizing flows have been used to learn the Boltzmann distribution directly, without samples. However, this approach is susceptible to mode collapse and thus often does not explore the full configurational space. In this work, we address this challenge by separating the problem into two levels, the fine-grained and coarse-grained degrees of freedom. A normalizing flow conditioned on the coarse-grained space yields a probabilistic connection between the two levels. To explore the configurational space, we employ coarse-grained simulations with active learning which allows us to update the flow and make all-atom potential energy evaluations only when necessary. Using alanine dipeptide as an example, we show that our methods obtain a speedup to molecular dynamics simulations of approximately $15.9$ to $216.2$ compared to the speedup of $4.5$ of the current state-of-the-art machine learning approach.
|
[
"Computational Chemistry",
"Molecular Dynamics",
"Generative Models",
"Active Learning"
] | |
ICML
| 2,023
| 24,725
|
Detecting Out-of-distribution Data through In-distribution Class Prior
|
Given a pre-trained in-distribution (ID) model, the inference-time out-of-distribution (OOD) detection aims to recognize OOD data during the inference stage. However, some representative methods share an unproven assumption that the probability that OOD data belong to every ID class should be the same, i.e., these OOD-to-ID probabilities actually form a uniform distribution. In this paper, we show that this assumption makes the above methods incapable when the ID model is trained with class-imbalanced data.Fortunately, by analyzing the causal relations between ID/OOD classes and features, we identify several common scenarios where the OOD-to-ID probabilities should be the ID-class-prior distribution and propose two strategies to modify existing inference-time detection methods: 1) replace the uniform distribution with the ID-class-prior distribution if they explicitly use the uniform distribution; 2) otherwise, reweight their scores according to the similarity between the ID-class-prior distribution and the softmax outputs of the pre-trained model. Extensive experiments show that both strategies can improve the OOD detection performance when the ID model is pre-trained with imbalanced data, reflecting the importance of ID-class prior in OOD detection.
|
[
"Out-of-Distribution Detection",
"Inference Methods",
"Class Imbalance",
"Causal Inference in Machine Learning"
] | |
ICML
| 2,024
| 34,806
|
SPP: Sparsity-Preserved Parameter-Efficient Fine-Tuning for Large Language Models
|
Large Language Models (LLMs) have become pivotal in advancing the field of artificial intelligence, yet their immense sizes pose significant challenges for both fine-tuning and deployment. Current post-training pruning methods, while reducing the sizes of LLMs, often fail to maintain their original performance. To address these challenges, this paper introduces SPP, aSparsity-PreservedParameter-efficient fine-tuning method. Different from existing post-training pruning approaches that struggle with performance retention, SPP proposes to employ lightweight learnable column and row matrices to optimize sparse LLM weights,keeping the structure and sparsity of pruned pre-trained models intact. By element-wise multiplication and residual addition, SPP ensures the consistency of model sparsity pattern and ratio during both training and weight-merging processes. We demonstrate the effectiveness of SPP by applying it to the LLaMA and LLaMA-2 model families with recent post-training pruning methods. Our results show that SPP significantly enhances the performance of models with different sparsity patterns (i.e. unstructured and N:M sparsity), especially for those with high sparsity ratios (e.g. 75%), making it a promising solution for the efficient fine-tuning of sparse LLMs. Code will be made available at https://github.com/Lucky-Lance/SPP.
|
[
"Natural Language Processing",
"Model Compression",
"Fine-Tuning Techniques",
"Large Language Models"
] | |
ICML
| 2,023
| 24,809
|
Preprocessors Matter! Realistic Decision-Based Attacks on Machine Learning Systems
|
Decision-based attacks construct adversarial examples against a machine learning (ML) model by making only hard-label queries. These attacks have mainly been applied directly to standalone neural networks. However, in practice, ML models are just one component of a larger learning system. We find that by adding a single preprocessor in front of a classifier, state-of-the-art query-based attacks are up to seven× less effective at attacking a prediction pipeline than at attacking the model alone. We explain this discrepancy by the fact that most preprocessors introduce some notion of invariance to the input space. Hence, attacks that are unaware of this invariance inevitably waste a large number of queries to re-discover or overcome it. We, therefore, develop techniques to (i) reverse-engineer the preprocessor and then (ii) use this extracted information to attack the end-to-end system. Our preprocessors extraction method requires only a few hundred queries, and our preprocessor-aware attacks recover the same efficacy as when attacking the model alone. The code can be found at https://github.com/google-research/preprocessor-aware-black-box-attack.
|
[
"Adversarial Machine Learning",
"Machine Learning Security",
"Neural Networks",
"Cybersecurity",
"Computer Vision"
] | |
ICML
| 2,024
| 34,011
|
Time-Series Forecasting for Out-of-Distribution Generalization Using Invariant Learning
|
Time-series forecasting (TSF) finds broad applications in real-world scenarios. Due to the dynamic nature of time-series data, it is crucial for TSF models to preserve out-of-distribution (OOD) generalization abilities, as training and test sets represent historical and future data respectively. In this paper, we aim to alleviate the inherent OOD problem in TSF via invariant learning. We identify fundamental challenges of invariant learning for TSF. First, the target variables in TSF may not be sufficiently determined by the input due to unobserved core variables in TSF, breaking the fundamental assumption of invariant learning. Second, time-series datasets lack adequate environment labels, while existing environmental inference methods are not suitable for TSF. To address these challenges, we propose FOIL, a model-agnostic framework that endows time-series forecasting for out-of-distribution generalization via invariant learning. Specifically, FOIL employs a novel surrogate loss to mitigate the impact of unobserved variables. Further, FOIL implements joint optimization by alternately inferring environments effectively with a multi-head network while preserving the temporal adjacency structure and learning invariant representations across inferred environments for OOD generalized TSF. Extensive experiments demonstrate that the proposed FOIL significantly and consistently improves the performance of various TSF models, achieving gains of up to 85%.
|
[
"Time-Series Forecasting",
"Out-of-Distribution Generalization",
"Invariant Learning",
"Data Science",
"Predictive Modeling"
] | |
ICLR
| 2,022
| 6,971
|
Differentially Private Fractional Frequency Moments Estimation with Polylogarithmic Space
|
We prove that $\mathbb{F}_p$ sketch, a well-celebrated streaming algorithm for frequency moments estimation, is differentially private as is when $p\in(0, 1]$. $\mathbb{F}_p$ sketch uses only polylogarithmic space, exponentially better than existing DP baselines and only worse than the optimal non-private baseline by a logarithmic factor. The evaluation shows that $\mathbb{F}_p$ sketch can achieve reasonable accuracy with strong privacy guarantees. The code for evaluation is included in the supplementary material.
|
[
"Differential Privacy",
"Streaming Algorithms",
"Frequency Moments Estimation",
"Data Privacy",
"Algorithmic Efficiency"
] | |
NeurIPS
| 2,022
| 65,597
|
CUDA: Curriculum of Data Augmentation for Long-tailed Recognition
|
Class imbalance problems frequently occur in real-world tasks, and conventional deep learning algorithms are well known for performance degradation on imbalanced training datasets. To mitigate this problem, many approaches have aimed to balance among given classes by re-weighting or re-sampling training samples. These re-balancing methods increase the impact of minority classes and reduce the influence of majority classes on the output of models. Despite extensive recent studies, no deep analysis has been conducted on determination of classes to be augmented and strength of augmentation has been conducted. In this study, we propose a simple and efficient novel curriculum, which is designed to find the appropriate per-class strength of data augmentation, called CUDA: CUrriculum of Data Augmentation for long-tailed recognition. CUDA can simply be integrated into existing long-tailed recognition methods. We present the results of experiments showing that CUDA effectively achieves better generalization performance compared to the state-of-the-art method on imbalanced datasets such as CIFAR-100-LT.
|
[
"Deep Learning",
"Data Augmentation",
"Imbalanced Learning",
"Computer Vision"
] | |
NeurIPS
| 2,022
| 52,908
|
Toward Efficient Robust Training against Union of $\ell_p$ Threat Models
|
The overwhelming vulnerability of deep neural networks to carefully crafted perturbations known as adversarial attacks has led to the development of various training techniques to produce robust models. While the primary focus of existing approaches has been directed toward addressing the worst-case performance achieved under a single-threat model, it is imperative that safety-critical systems are robust with respect to multiple threat models simultaneously. Existing approaches that address worst-case performance under the union of such threat models ($\ell_{\infty}, \ell_2, \ell_1$) either utilize adversarial training methods that require multi-step attacks which are computationally expensive in practice, or rely upon fine-tuning of pre-trained models that are robust with respect to a single-threat model. In this work, we show that by carefully choosing the objective function used for robust training, it is possible to achieve similar, or improved worst-case performance over a union of threat models while utilizing only single-step attacks, thereby achieving a significant reduction in computational resources necessary for training. Furthermore, prior work showed that adversarial training specific to the $\ell_1$ threat model is relatively difficult, to the extent that even multi-step adversarially trained models were shown to be prone to gradient-masking. However, the proposed method—when applied on the $\ell_1$ threat model specifically—enables us to obtain the first $\ell_1$ robust model trained solely with single-step adversaries. Finally, to demonstrate the merits of our approach, we utilize a modern set of attack evaluations to better estimate the worst-case performance under the considered union of threat models.
|
[
"Adversarial Machine Learning",
"Robustness in Deep Learning",
"Neural Network Security",
"Computational Efficiency in Machine Learning"
] | |
ICML
| 2,024
| 34,383
|
Feasibility Consistent Representation Learning for Safe Reinforcement Learning
|
In the field of safe reinforcement learning (RL), finding a balance between satisfying safety constraints and optimizing reward performance presents a significant challenge. A key obstacle in this endeavor is the estimation of safety constraints, which is typically more difficult than estimating a reward metric due to the sparse nature of the constraint signals. To address this issue, we introduce a novel framework named Feasibility Consistent Safe Reinforcement Learning (FCSRL). This framework combines representation learning with feasibility-oriented objectives to identify and extract safety-related information from the raw state for safe RL. Leveraging self-supervised learning techniques and a more learnable safety metric, our approach enhances the policy learning and constraint estimation. Empirical evaluations across a range of vector-state and image-based tasks demonstrate that our method is capable of learning a better safety-aware embedding and achieving superior performance than previous representation learning baselines.
|
[
"Safe Reinforcement Learning",
"Representation Learning",
"Self-supervised Learning"
] | |
NeurIPS
| 2,023
| 72,443
|
Neural Data Transformer 2: Multi-context Pretraining for Neural Spiking Activity
|
The neural population spiking activity recorded by intracortical brain-computer interfaces (iBCIs) contain rich structure. Current models of such spiking activity are largely prepared for individual experimental contexts, restricting data volume to that collectable within a single session and limiting the effectiveness of deep neural networks (DNNs). The purported challenge in aggregating neural spiking data is the pervasiveness of context-dependent shifts in the neural data distributions. However, large scale unsupervised pretraining by nature spans heterogeneous data, and has proven to be a fundamental recipe for successful representation learning across deep learning. We thus develop Neural Data Transformer 2 (NDT2), a spatiotemporal Transformer for neural spiking activity, and demonstrate that pretraining can leverage motor BCI datasets that span sessions, subjects, and experimental tasks. NDT2 enables rapid adaptation to novel contexts in downstream decoding tasks and opens the path to deployment of pretrained DNNs for iBCI control. Code: https://github.com/joel99/contextgeneralbci
|
[
"Brain-Computer Interfaces ",
"Neural Data Analysis",
"Deep Learning",
"Neuroscience",
"Computational Neuroscience",
"Neural Networks",
"Spiking Neural Networks",
"Data Preprocessing and Transformation"
] | |
NeurIPS
| 2,022
| 54,623
|
TokenMixup: Efficient Attention-guided Token-level Data Augmentation for Transformers
|
Mixup is a commonly adopted data augmentation technique for image classification. Recent advances in mixup methods primarily focus on mixing based on saliency. However, many saliency detectors require intense computation and are especially burdensome for parameter-heavy transformer models. To this end, we propose TokenMixup, an efficient attention-guided token-level data augmentation method that aims to maximize the saliency of a mixed set of tokens. TokenMixup provides ×15 faster saliency-aware data augmentation compared to gradient-based methods. Moreover, we introduce a variant of TokenMixup which mixes tokens within a single instance, thereby enabling multi-scale feature augmentation. Experiments show that our methods significantly improve the baseline models’ performance on CIFAR and ImageNet-1K, while being more efficient than previous methods. We also reach state-of-the-art performance on CIFAR-100 among from-scratch transformer models. Code is available at https://github.com/mlvlab/TokenMixup.
|
[
"Data Augmentation",
"Computer Vision",
"Transformers",
"Deep Learning"
] | |
NeurIPS
| 2,023
| 70,748
|
CAST: Cross-Attention in Space and Time for Video Action Recognition
|
Recognizing human actions in videos requires spatial and temporal understanding. Most existing action recognition models lack a balanced spatio-temporal understanding of videos. In this work, we propose a novel two-stream architecture, called Cross-Attention in Space and Time (CAST), that achieves a balanced spatio-temporal understanding of videos using only RGB input. Our proposed bottleneck cross-attention mechanism enables the spatial and temporal expert models to exchange information and make synergistic predictions, leading to improved performance. We validate the proposed method with extensive experiments on public benchmarks with different characteristics: EPIC-Kitchens-100, Something-Something-V2, and Kinetics-400. Our method consistently shows favorable performance across these datasets, while the performance of existing methods fluctuates depending on the dataset characteristics. The code is available at https://github.com/KHU-VLL/CAST.
|
[
"Computer Vision",
"Video Action Recognition",
"Deep Learning",
"Spatio-Temporal Modeling"
] | |
NeurIPS
| 2,023
| 72,333
|
Zero-Regret Performative Prediction Under Inequality Constraints
|
Performative prediction is a recently proposed framework where predictions guide decision-making and hence influence future data distributions. Such performative phenomena are ubiquitous in various areas, such as transportation, finance, public policy, and recommendation systems. To date, work on performative prediction has only focused on unconstrained problems, neglecting the fact that many real-world learning problems are subject to constraints. This paper bridges this gap by studying performative prediction under inequality constraints. Unlike most existing work that provides only performative stable points, we aim to find the optimal solutions. Anticipating performative gradient is a challenging task, due to the agnostic performative effect on data distributions. To address this issue, we first develop a robust primal-dual framework that requires only approximate gradients up to a certain accuracy, yet delivers the same order of performance as the stationary stochastic primal-dual algorithm without performativity. Based on this framework, we then propose an adaptive primal-dual algorithm for location families. Our analysis demonstrates that the proposed adaptive primal-dual algorithm attains $\mathcal{O}(\sqrt{T})$ regret and constraint violations, using only $\sqrt{T} + 2T$ samples, where $T$ is the time horizon. To our best knowledge, this is the first study and analysis on the optimality of the performative prediction problem under inequality constraints. Finally, we validate the effectiveness of our algorithm and theoretical results through numerical simulations.
|
[
"Optimization",
"Algorithm Design",
"Decision-Making Systems",
"Constrained Optimization"
] | |
ICLR
| 2,022
| 7,113
|
Differentiable DAG Sampling
|
We propose a new differentiable probabilistic model over DAGs (DP-DAG). DP-DAG allows fast and differentiable DAG sampling suited to continuous optimization. To this end, DP-DAG samples a DAG by successively (1) sampling a linear ordering of the node and (2) sampling edges consistent with the sampled linear ordering. We further propose VI-DP-DAG, a new method for DAG learning from observational data which combines DP-DAG with variational inference. Hence,VI-DP-DAG approximates the posterior probability over DAG edges given the observed data. VI-DP-DAG is guaranteed to output a valid DAG at any time during training and does not require any complex augmented Lagrangian optimization scheme in contrast to existing differentiable DAG learning approaches. In our extensive experiments, we compare VI-DP-DAG to other differentiable DAG learning baselines on synthetic and real datasets. VI-DP-DAG significantly improves DAG structure and causal mechanism learning while training faster than competitors.
|
[
"Probabilistic Graphical Models",
"Causal Inference",
"Bayesian Inference",
"Optimization Methods"
] | |
NeurIPS
| 2,022
| 54,525
|
Data Augmentation MCMC for Bayesian Inference from Privatized Data
|
Differentially private mechanisms protect privacy by introducing additional randomness into the data. Restricting access to only the privatized data makes it challenging to perform valid statistical inference on parameters underlying the confidential data. Specifically, the likelihood function of the privatized data requires integrating over the large space of confidential databases and is typically intractable. For Bayesian analysis, this results in a posterior distribution that is doubly intractable, rendering traditional MCMC techniques inapplicable. We propose an MCMC framework to perform Bayesian inference from the privatized data, which is applicable to a wide range of statistical models and privacy mechanisms. Our MCMC algorithm augments the model parameters with the unobserved confidential data, and alternately updates each one. For the potentially challenging step of updating the confidential data, we propose a generic approach that exploits the privacy guarantee of the mechanism to ensure efficiency. We give results on the computational complexity, acceptance rate, and mixing properties of our MCMC. We illustrate the efficacy and applicability of our methods on a naïve-Bayes log-linear model and on a linear regression model.
|
[
"Bayesian Inference",
"Differential Privacy",
"Markov Chain Monte Carlo ",
"Statistical Modeling",
"Data Privacy and Security"
] | |
ICML
| 2,022
| 17,793
|
Generalized Data Distribution Iteration
|
To obtain higher sample efficiency and superior final performance simultaneously has been one of the major challenges for deep reinforcement learning (DRL). Previous work could handle one of these challenges but typically failed to address them concurrently. In this paper, we try to tackle these two challenges simultaneously. To achieve this, we firstly decouple these challenges into two classic RL problems: data richness and exploration-exploitation trade-off. Then, we cast these two problems into the training data distribution optimization problem, namely to obtain desired training data within limited interactions, and address them concurrently via i) explicit modeling and control of the capacity and diversity of behavior policy and ii) more fine-grained and adaptive control of selective/sampling distribution of the behavior policy using a monotonic data distribution optimization. Finally, we integrate this process into Generalized Policy Iteration (GPI) and obtain a more general framework called Generalized Data Distribution Iteration (GDI). We use the GDI framework to introduce operator-based versions of well-known RL methods from DQN to Agent57. Theoretical guarantee of the superiority of GDI compared with GPI is concluded. We also demonstrate our state-of-the-art (SOTA) performance on Arcade Learning Environment (ALE), wherein our algorithm has achieved 9620.98% mean human normalized score (HNS), 1146.39% median HNS, and surpassed 22 human world records using only 200M training frames. Our performance is comparable to Agent57’s while we consume 500 times less data. We argue that there is still a long way to go before obtaining real superhuman agents in ALE.
|
[
"Deep Reinforcement Learning",
"Data Efficiency",
"Exploration-Exploitation Trade-off",
"Policy Iteration",
"Algorithm Optimization"
] |
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