NeurIPS
Collection
Accepted papers for NeurIPS (Conference on Neural Information Processing Systems), one dataset per year. • 13 items • Updated
paper_id string | title string | paper_url string | authors list | type string | primary_area string | abstract large_string | keywords list | TL;DR large_string | submission_number int64 | arxiv_id string | arxiv_id_source string |
|---|---|---|---|---|---|---|---|---|---|---|---|
01f78be6f7cad02658508fe4616098a9 | Quartz: Randomized Dual Coordinate Ascent with Arbitrary Sampling | https://proceedings.neurips.cc/paper_files/paper/2015/hash/01f78be6f7cad02658508fe4616098a9-Abstract.html | [
"Zheng Qu",
"Peter Richtarik",
"Tong Zhang"
] | null | null | We study the problem of minimizing the average of a large number of smooth convex functions penalized with a strongly convex regularizer. We propose and analyze a novel primal-dual method (Quartz) which at every iteration samples and updates a random subset of the dual variables, chosen according to an arbitrary distri... | [] | null | 1 | 1411.5873 | title_judge |
020c8bfac8de160d4c5543b96d1fdede | Associative Memory via a Sparse Recovery Model | https://proceedings.neurips.cc/paper_files/paper/2015/hash/020c8bfac8de160d4c5543b96d1fdede-Abstract.html | [
"Arya Mazumdar",
"Ankit Singh Rawat"
] | null | null | An associative memory is a structure learned from a dataset $\mathcal{M}$ of vectors (signals) in a way such that, given a noisy version of one of the vectors as input, the nearest valid vector from $\mathcal{M}$ (nearest neighbor) is provided as output, preferably via a fast iterative algorithm. Traditionally, binary ... | [] | null | 2 | null | null |
024d7f84fff11dd7e8d9c510137a2381 | Policy Gradient for Coherent Risk Measures | https://proceedings.neurips.cc/paper_files/paper/2015/hash/024d7f84fff11dd7e8d9c510137a2381-Abstract.html | [
"Aviv Tamar",
"Yinlam Chow",
"Mohammad Ghavamzadeh",
"Shie Mannor"
] | null | null | Several authors have recently developed risk-sensitive policy gradient methods that augment the standard expected cost minimization problem with a measure of variability in cost. These studies have focused on specific risk-measures, such as the variance or conditional value at risk (CVaR). In this work, we extend the p... | [] | null | 3 | 1502.03919 | title_snapshot |
02522a2b2726fb0a03bb19f2d8d9524d | A fast, universal algorithm to learn parametric nonlinear embeddings | https://proceedings.neurips.cc/paper_files/paper/2015/hash/02522a2b2726fb0a03bb19f2d8d9524d-Abstract.html | [
"Miguel A. Carreira-Perpinan",
"Max Vladymyrov"
] | null | null | Nonlinear embedding algorithms such as stochastic neighbor embedding do dimensionality reduction by optimizing an objective function involving similarities between pairs of input patterns. The result is a low-dimensional projection of each input pattern. A common way to define an out-of-sample mapping is to optimize th... | [] | null | 4 | null | null |
0266e33d3f546cb5436a10798e657d97 | Stochastic Online Greedy Learning with Semi-bandit Feedbacks | https://proceedings.neurips.cc/paper_files/paper/2015/hash/0266e33d3f546cb5436a10798e657d97-Abstract.html | [
"Tian Lin",
"Jian Li",
"Wei Chen"
] | null | null | The greedy algorithm is extensively studied in the field of combinatorial optimization for decades. In this paper, we address the online learning problem when the input to the greedy algorithm is stochastic with unknown parameters that have to be learned over time. We first propose the greedy regret and $\epsilon$-quas... | [] | null | 5 | null | null |
02a32ad2669e6fe298e607fe7cc0e1a0 | SubmodBoxes: Near-Optimal Search for a Set of Diverse Object Proposals | https://proceedings.neurips.cc/paper_files/paper/2015/hash/02a32ad2669e6fe298e607fe7cc0e1a0-Abstract.html | [
"Qing Sun",
"Dhruv Batra"
] | null | null | This paper formulates the search for a set of bounding boxes (as needed in object proposal generation) as a monotone submodular maximization problem over the space of all possible bounding boxes in an image. Since the number of possible bounding boxes in an image is very large $O(#pixels^2)$, even a single linear scan ... | [] | null | 6 | null | null |
02e74f10e0327ad868d138f2b4fdd6f0 | Robust Portfolio Optimization | https://proceedings.neurips.cc/paper_files/paper/2015/hash/02e74f10e0327ad868d138f2b4fdd6f0-Abstract.html | [
"Huitong Qiu",
"Fang Han",
"Han Liu",
"Brian Caffo"
] | null | null | We propose a robust portfolio optimization approach based on quantile statistics. The proposed method is robust to extreme events in asset returns, and accommodates large portfolios under limited historical data. Specifically, we show that the risk of the estimated portfolio converges to the oracle optimal risk with pa... | [] | null | 7 | null | null |
0336dcbab05b9d5ad24f4333c7658a0e | Top-k Multiclass SVM | https://proceedings.neurips.cc/paper_files/paper/2015/hash/0336dcbab05b9d5ad24f4333c7658a0e-Abstract.html | [
"Maksim Lapin",
"Matthias Hein",
"Bernt Schiele"
] | null | null | Class ambiguity is typical in image classification problems with a large number of classes. When classes are difficult to discriminate, it makes sense to allow k guesses and evaluate classifiers based on the top-k error instead of the standard zero-one loss. We propose top-k multiclass SVM as a direct method to optimiz... | [] | null | 8 | 1511.06683 | title_snapshot |
03e0704b5690a2dee1861dc3ad3316c9 | Less is More: Nyström Computational Regularization | https://proceedings.neurips.cc/paper_files/paper/2015/hash/03e0704b5690a2dee1861dc3ad3316c9-Abstract.html | [
"Alessandro Rudi",
"Raffaello Camoriano",
"Lorenzo Rosasco"
] | null | null | We study Nyström type subsampling approaches to large scale kernel methods, and prove learning bounds in the statistical learning setting, where random sampling and high probability estimates are considered. In particular, we prove that these approaches can achieve optimal learning bounds, provided the subsampling leve... | [] | null | 9 | 1507.04717 | title_snapshot |
04ecb1fa28506ccb6f72b12c0245ddbc | Parallel Recursive Best-First AND/OR Search for Exact MAP Inference in Graphical Models | https://proceedings.neurips.cc/paper_files/paper/2015/hash/04ecb1fa28506ccb6f72b12c0245ddbc-Abstract.html | [
"Akihiro Kishimoto",
"Radu Marinescu",
"Adi Botea"
] | null | null | The paper presents and evaluates the power of parallel search for exact MAP inference in graphical models. We introduce a new parallel shared-memory recursive best-first AND/OR search algorithm, called SPRBFAOO, that explores the search space in a best-first manner while operating with restricted memory. Our experiment... | [] | null | 10 | null | null |
051e4e127b92f5d98d3c79b195f2b291 | Differentially private subspace clustering | https://proceedings.neurips.cc/paper_files/paper/2015/hash/051e4e127b92f5d98d3c79b195f2b291-Abstract.html | [
"Yining Wang",
"Yu-Xiang Wang",
"Aarti Singh"
] | null | null | Subspace clustering is an unsupervised learning problem that aims at grouping data points into multiple clusters'' so that data points in a single cluster lie approximately on a low-dimensional linear subspace. It is originally motivated by 3D motion segmentation in computer vision, but has recently been generically ap... | [] | null | 11 | null | null |
0609154fa35b3194026346c9cac2a248 | Matrix Completion with Noisy Side Information | https://proceedings.neurips.cc/paper_files/paper/2015/hash/0609154fa35b3194026346c9cac2a248-Abstract.html | [
"Kai-Yang Chiang",
"Cho-Jui Hsieh",
"Inderjit S Dhillon"
] | null | null | We study matrix completion problem with side information. Side information has been considered in several matrix completion applications, and is generally shown to be useful empirically. Recently, Xu et al. studied the effect of side information for matrix completion under a theoretical viewpoint, showing that sample c... | [] | null | 12 | null | null |
06138bc5af6023646ede0e1f7c1eac75 | Nonparametric von Mises Estimators for Entropies, Divergences and Mutual Informations | https://proceedings.neurips.cc/paper_files/paper/2015/hash/06138bc5af6023646ede0e1f7c1eac75-Abstract.html | [
"Kirthevasan Kandasamy",
"Akshay Krishnamurthy",
"Barnabas Poczos",
"Larry Wasserman",
"james m robins"
] | null | null | We propose and analyse estimators for statistical functionals of one or moredistributions under nonparametric assumptions.Our estimators are derived from the von Mises expansion andare based on the theory of influence functions, which appearin the semiparametric statistics literature.We show that estimators based eithe... | [] | null | 13 | 1411.4342 | title_judge |
06a15eb1c3836723b53e4abca8d9b879 | Semi-Supervised Factored Logistic Regression for High-Dimensional Neuroimaging Data | https://proceedings.neurips.cc/paper_files/paper/2015/hash/06a15eb1c3836723b53e4abca8d9b879-Abstract.html | [
"Danilo Bzdok",
"Michael Eickenberg",
"Olivier Grisel",
"Bertrand Thirion",
"Gael Varoquaux"
] | null | null | Imaging neuroscience links human behavior to aspects of brain biology in ever-increasing datasets. Existing neuroimaging methods typically perform either discovery of unknown neural structure or testing of neural structure associated with mental tasks. However, testing hypotheses on the neural correlates underlying lar... | [] | null | 14 | null | null |
07563a3fe3bbe7e3ba84431ad9d055af | Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting | https://proceedings.neurips.cc/paper_files/paper/2015/hash/07563a3fe3bbe7e3ba84431ad9d055af-Abstract.html | [
"Xingjian SHI",
"Zhourong Chen",
"Hao Wang",
"Dit-Yan Yeung",
"Wai-kin Wong",
"Wang-chun WOO"
] | null | null | The goal of precipitation nowcasting is to predict the future rainfall intensity in a local region over a relatively short period of time. Very few previous studies have examined this crucial and challenging weather forecasting problem from the machine learning perspective. In this paper, we formulate precipitation now... | [] | null | 15 | 1506.04214 | title_snapshot |
0768281a05da9f27df178b5c39a51263 | Infinite Factorial Dynamical Model | https://proceedings.neurips.cc/paper_files/paper/2015/hash/0768281a05da9f27df178b5c39a51263-Abstract.html | [
"Isabel Valera",
"Francisco Ruiz",
"Lennart Svensson",
"Fernando Perez-Cruz"
] | null | null | We propose the infinite factorial dynamic model (iFDM), a general Bayesian nonparametric model for source separation. Our model builds on the Markov Indian buffet process to consider a potentially unbounded number of hidden Markov chains (sources) that evolve independently according to some dynamics, in which the state... | [] | null | 16 | null | null |
07a4e20a7bbeeb7a736682b26b16ebe8 | Dependent Multinomial Models Made Easy: Stick-Breaking with the Polya-gamma Augmentation | https://proceedings.neurips.cc/paper_files/paper/2015/hash/07a4e20a7bbeeb7a736682b26b16ebe8-Abstract.html | [
"Scott Linderman",
"Matthew J Johnson",
"Ryan P. Adams"
] | null | null | Many practical modeling problems involve discrete data that are best represented as draws from multinomial or categorical distributions. For example, nucleotides in a DNA sequence, children's names in a given state and year, and text documents are all commonly modeled with multinomial distributions. In all of these cas... | [] | null | 17 | 1506.05843 | title_snapshot |
0966289037ad9846c5e994be2a91bafa | Sparse Linear Programming via Primal and Dual Augmented Coordinate Descent | https://proceedings.neurips.cc/paper_files/paper/2015/hash/0966289037ad9846c5e994be2a91bafa-Abstract.html | [
"Ian En-Hsu Yen",
"Kai Zhong",
"Cho-Jui Hsieh",
"Pradeep K Ravikumar",
"Inderjit S Dhillon"
] | null | null | Over the past decades, Linear Programming (LP) has been widely used in different areas and considered as one of the mature technologies in numerical optimization. However, the complexity offered by state-of-the-art algorithms (i.e. interior-point method and primal, dual simplex methods) is still unsatisfactory for prob... | [] | null | 18 | null | null |
09b15d48a1514d8209b192a8b8f34e48 | Data Generation as Sequential Decision Making | https://proceedings.neurips.cc/paper_files/paper/2015/hash/09b15d48a1514d8209b192a8b8f34e48-Abstract.html | [
"Philip Bachman",
"Doina Precup"
] | null | null | We connect a broad class of generative models through their shared reliance on sequential decision making. Motivated by this view, we develop extensions to an existing model, and then explore the idea further in the context of data imputation -- perhaps the simplest setting in which to investigate the relation between ... | [] | null | 19 | 1506.03504 | title_snapshot |
0a1bf96b7165e962e90cb14648c9462d | Online Gradient Boosting | https://proceedings.neurips.cc/paper_files/paper/2015/hash/0a1bf96b7165e962e90cb14648c9462d-Abstract.html | [
"Alina Beygelzimer",
"Elad Hazan",
"Satyen Kale",
"Haipeng Luo"
] | null | null | We extend the theory of boosting for regression problems to the online learning setting. Generalizing from the batch setting for boosting, the notion of a weak learning algorithm is modeled as an online learning algorithm with linear loss functions that competes with a base class of regression functions, while a strong... | [] | null | 20 | 1506.04820 | title_snapshot |
0aa1883c6411f7873cb83dacb17b0afc | Optimal Ridge Detection using Coverage Risk | https://proceedings.neurips.cc/paper_files/paper/2015/hash/0aa1883c6411f7873cb83dacb17b0afc-Abstract.html | [
"Yen-Chi Chen",
"Christopher R Genovese",
"Shirley Ho",
"Larry Wasserman"
] | null | null | We introduce the concept of coverage risk as an error measure for density ridge estimation.The coverage risk generalizes the mean integrated square error to set estimation.We propose two risk estimators for the coverage risk and we show that we can select tuning parameters by minimizing the estimated risk.We study the ... | [] | null | 21 | 1506.02278 | title_snapshot |
0b8aff0438617c055eb55f0ba5d226fa | A Tractable Approximation to Optimal Point Process Filtering: Application to Neural Encoding | https://proceedings.neurips.cc/paper_files/paper/2015/hash/0b8aff0438617c055eb55f0ba5d226fa-Abstract.html | [
"Yuval Harel",
"Ron Meir",
"Manfred Opper"
] | null | null | The process of dynamic state estimation (filtering) based on point process observations is in general intractable. Numerical sampling techniques are often practically useful, but lead to limited conceptual insight about optimal encoding/decoding strategies, which are of significant relevance to Computational Neuroscien... | [] | null | 22 | 1507.07813 | title_judge |
0c74b7f78409a4022a2c4c5a5ca3ee19 | Barrier Frank-Wolfe for Marginal Inference | https://proceedings.neurips.cc/paper_files/paper/2015/hash/0c74b7f78409a4022a2c4c5a5ca3ee19-Abstract.html | [
"Rahul G Krishnan",
"Simon Lacoste-Julien",
"David Sontag"
] | null | null | We introduce a globally-convergent algorithm for optimizing the tree-reweighted (TRW) variational objective over the marginal polytope. The algorithm is based on the conditional gradient method (Frank-Wolfe) and moves pseudomarginals within the marginal polytope through repeated maximum a posteriori (MAP) calls. This m... | [] | null | 23 | 1511.02124 | title_snapshot |
0ce2ffd21fc958d9ef0ee9ba5336e357 | Combinatorial Bandits Revisited | https://proceedings.neurips.cc/paper_files/paper/2015/hash/0ce2ffd21fc958d9ef0ee9ba5336e357-Abstract.html | [
"Richard Combes",
"Mohammad Sadegh Talebi Mazraeh Shahi",
"Alexandre Proutiere",
"marc lelarge"
] | null | null | This paper investigates stochastic and adversarial combinatorial multi-armed bandit problems. In the stochastic setting under semi-bandit feedback, we derive a problem-specific regret lower bound, and discuss its scaling with the dimension of the decision space. We propose ESCB, an algorithm that efficiently exploits t... | [] | null | 24 | 1502.03475 | title_snapshot |
0d4f4805c36dc6853edfa4c7e1638b48 | Efficient and Parsimonious Agnostic Active Learning | https://proceedings.neurips.cc/paper_files/paper/2015/hash/0d4f4805c36dc6853edfa4c7e1638b48-Abstract.html | [
"Tzu-Kuo Huang",
"Alekh Agarwal",
"Daniel J. Hsu",
"John Langford",
"Robert E. Schapire"
] | null | null | We develop a new active learning algorithm for the streaming settingsatisfying three important properties: 1) It provably works for anyclassifier representation and classification problem including thosewith severe noise. 2) It is efficiently implementable with an ERMoracle. 3) It is more aggressive than all previous a... | [] | null | 25 | 1506.08669 | title_snapshot |
0e65972dce68dad4d52d063967f0a705 | Policy Evaluation Using the Ω-Return | https://proceedings.neurips.cc/paper_files/paper/2015/hash/0e65972dce68dad4d52d063967f0a705-Abstract.html | [
"Philip S. Thomas",
"Scott Niekum",
"Georgios Theocharous",
"George Konidaris"
] | null | null | We propose the Ω-return as an alternative to the λ-return currently used by the TD(λ) family of algorithms. The benefit of the Ω-return is that it accounts for the correlation of different length returns. Because it is difficult to compute exactly, we suggest one way of approximating the Ω-return. We provide empirical ... | [] | null | 26 | null | null |
0ebcc77dc72360d0eb8e9504c78d38bd | Bayesian Optimization with Exponential Convergence | https://proceedings.neurips.cc/paper_files/paper/2015/hash/0ebcc77dc72360d0eb8e9504c78d38bd-Abstract.html | [
"Kenji Kawaguchi",
"Leslie Pack Kaelbling",
"Tomás Lozano-Pérez"
] | null | null | This paper presents a Bayesian optimization method with exponential convergence without the need of auxiliary optimization and without the delta-cover sampling. Most Bayesian optimization methods require auxiliary optimization: an additional non-convex global optimization problem, which can be time-consuming and hard t... | [] | null | 27 | 1604.01348 | title_snapshot |
0fcbc61acd0479dc77e3cccc0f5ffca7 | Statistical Model Criticism using Kernel Two Sample Tests | https://proceedings.neurips.cc/paper_files/paper/2015/hash/0fcbc61acd0479dc77e3cccc0f5ffca7-Abstract.html | [
"James R Lloyd",
"Zoubin Ghahramani"
] | null | null | We propose an exploratory approach to statistical model criticism using maximum mean discrepancy (MMD) two sample tests. Typical approaches to model criticism require a practitioner to select a statistic by which to measure discrepancies between data and a statistical model. MMD two sample tests are instead constructed... | [] | null | 28 | null | null |
1068c6e4c8051cfd4e9ea8072e3189e2 | Attention-Based Models for Speech Recognition | https://proceedings.neurips.cc/paper_files/paper/2015/hash/1068c6e4c8051cfd4e9ea8072e3189e2-Abstract.html | [
"Jan K Chorowski",
"Dzmitry Bahdanau",
"Dmitriy Serdyuk",
"Kyunghyun Cho",
"Yoshua Bengio"
] | null | null | Recurrent sequence generators conditioned on input data through an attention mechanism have recently shown very good performance on a range of tasks including machine translation, handwriting synthesis and image caption generation. We extend the attention-mechanism with features needed for speech recognition. We show t... | [] | null | 29 | 1506.07503 | title_snapshot |
109a0ca3bc27f3e96597370d5c8cf03d | Weakly-supervised Disentangling with Recurrent Transformations for 3D View Synthesis | https://proceedings.neurips.cc/paper_files/paper/2015/hash/109a0ca3bc27f3e96597370d5c8cf03d-Abstract.html | [
"Jimei Yang",
"Scott E Reed",
"Ming-Hsuan Yang",
"Honglak Lee"
] | null | null | An important problem for both graphics and vision is to synthesize novel views of a 3D object from a single image. This is in particular challenging due to the partial observability inherent in projecting a 3D object onto the image space, and the ill-posedness of inferring object shape and pose. However, we can train a... | [] | null | 30 | 1601.00706 | title_snapshot |
10a5ab2db37feedfdeaab192ead4ac0e | Backpropagation for Energy-Efficient Neuromorphic Computing | https://proceedings.neurips.cc/paper_files/paper/2015/hash/10a5ab2db37feedfdeaab192ead4ac0e-Abstract.html | [
"Steve K Esser",
"Rathinakumar Appuswamy",
"Paul Merolla",
"John V. Arthur",
"Dharmendra S Modha"
] | null | null | Solving real world problems with embedded neural networks requires both training algorithms that achieve high performance and compatible hardware that runs in real time while remaining energy efficient. For the former, deep learning using backpropagation has recently achieved a string of successes across many domains a... | [] | null | 31 | null | null |
11d0e6287202fced83f79975ec59a3a6 | Efficient and Robust Automated Machine Learning | https://proceedings.neurips.cc/paper_files/paper/2015/hash/11d0e6287202fced83f79975ec59a3a6-Abstract.html | [
"Matthias Feurer",
"Aaron Klein",
"Katharina Eggensperger",
"Jost Springenberg",
"Manuel Blum",
"Frank Hutter"
] | null | null | The success of machine learning in a broad range of applications has led to an ever-growing demand for machine learning systems that can be used off the shelf by non-experts. To be effective in practice, such systems need to automatically choose a good algorithm and feature preprocessing steps for a new dataset at hand... | [] | null | 32 | null | null |
136f951362dab62e64eb8e841183c2a9 | Time-Sensitive Recommendation From Recurrent User Activities | https://proceedings.neurips.cc/paper_files/paper/2015/hash/136f951362dab62e64eb8e841183c2a9-Abstract.html | [
"Nan Du",
"Yichen Wang",
"Niao He",
"Jimeng Sun",
"Le Song"
] | null | null | By making personalized suggestions, a recommender system is playing a crucial role in improving the engagement of users in modern web-services. However, most recommendation algorithms do not explicitly take into account the temporal behavior and the recurrent activities of users. Two central but less explored questions... | [] | null | 33 | null | null |
1373b284bc381890049e92d324f56de0 | Local Expectation Gradients for Black Box Variational Inference | https://proceedings.neurips.cc/paper_files/paper/2015/hash/1373b284bc381890049e92d324f56de0-Abstract.html | [
"Michalis Titsias RC AUEB",
"Miguel Lázaro-Gredilla"
] | null | null | We introduce local expectation gradients which is a general purpose stochastic variational inference algorithm for constructing stochastic gradients by sampling from the variational distribution. This algorithm divides the problem of estimating the stochastic gradients over multiple variational parameters into smaller ... | [] | null | 34 | 1503.01494 | title_judge |
13f3cf8c531952d72e5847c4183e6910 | Training Restricted Boltzmann Machine via the Thouless-Anderson-Palmer free energy | https://proceedings.neurips.cc/paper_files/paper/2015/hash/13f3cf8c531952d72e5847c4183e6910-Abstract.html | [
"Marylou Gabrie",
"Eric W Tramel",
"Florent Krzakala"
] | null | null | Restricted Boltzmann machines are undirected neural networks which have been shown tobe effective in many applications, including serving as initializations fortraining deep multi-layer neural networks. One of the main reasons for their success is theexistence of efficient and practical stochastic algorithms, such as c... | [] | null | 35 | 1506.02914 | title_judge |
1415db70fe9ddb119e23e9b2808cde38 | High Dimensional EM Algorithm: Statistical Optimization and Asymptotic Normality | https://proceedings.neurips.cc/paper_files/paper/2015/hash/1415db70fe9ddb119e23e9b2808cde38-Abstract.html | [
"Zhaoran Wang",
"Quanquan Gu",
"Yang Ning",
"Han Liu"
] | null | null | We provide a general theory of the expectation-maximization (EM) algorithm for inferring high dimensional latent variable models. In particular, we make two contributions: (i) For parameter estimation, we propose a novel high dimensional EM algorithm which naturally incorporates sparsity structure into parameter estima... | [] | null | 36 | null | null |
148510031349642de5ca0c544f31b2ef | Learning Continuous Control Policies by Stochastic Value Gradients | https://proceedings.neurips.cc/paper_files/paper/2015/hash/148510031349642de5ca0c544f31b2ef-Abstract.html | [
"Nicolas Heess",
"Gregory Wayne",
"David Silver",
"Timothy Lillicrap",
"Tom Erez",
"Yuval Tassa"
] | null | null | We present a unified framework for learning continuous control policies usingbackpropagation. It supports stochastic control by treating stochasticity in theBellman equation as a deterministic function of exogenous noise. The productis a spectrum of general policy gradient algorithms that range from model-freemethods w... | [] | null | 37 | 1510.09142 | title_snapshot |
14bfa6bb14875e45bba028a21ed38046 | Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks | https://proceedings.neurips.cc/paper_files/paper/2015/hash/14bfa6bb14875e45bba028a21ed38046-Abstract.html | [
"Shaoqing Ren",
"Kaiming He",
"Ross Girshick",
"Jian Sun"
] | null | null | State-of-the-art object detection networks depend on region proposal algorithms to hypothesize object locations. Advances like SPPnet and Fast R-CNN have reduced the running time of these detection networks, exposing region proposal computation as a bottleneck. In this work, we introduce a Region Proposal Network (RPN)... | [] | null | 38 | 1506.01497 | title_snapshot |
1579779b98ce9edb98dd85606f2c119d | Efficient Non-greedy Optimization of Decision Trees | https://proceedings.neurips.cc/paper_files/paper/2015/hash/1579779b98ce9edb98dd85606f2c119d-Abstract.html | [
"Mohammad Norouzi",
"Maxwell Collins",
"Matthew A Johnson",
"David J Fleet",
"Pushmeet Kohli"
] | null | null | Decision trees and randomized forests are widely used in computer vision and machine learning. Standard algorithms for decision tree induction optimize the split functions one node at a time according to some splitting criteria. This greedy procedure often leads to suboptimal trees. In this paper, we present an algorit... | [] | null | 39 | 1511.04056 | title_snapshot |
1587965fb4d4b5afe8428a4a024feb0d | Learning with Incremental Iterative Regularization | https://proceedings.neurips.cc/paper_files/paper/2015/hash/1587965fb4d4b5afe8428a4a024feb0d-Abstract.html | [
"Lorenzo Rosasco",
"Silvia Villa"
] | null | null | Within a statistical learning setting, we propose and study an iterative regularization algorithm for least squares defined by an incremental gradient method. In particular, we show that, if all other parameters are fixed a priori, the number of passes over the data (epochs) acts as a regularization parameter, and prov... | [] | null | 40 | 1405.0042 | title_snapshot |
15de21c670ae7c3f6f3f1f37029303c9 | Maximum Likelihood Learning With Arbitrary Treewidth via Fast-Mixing Parameter Sets | https://proceedings.neurips.cc/paper_files/paper/2015/hash/15de21c670ae7c3f6f3f1f37029303c9-Abstract.html | [
"Justin Domke"
] | null | null | Inference is typically intractable in high-treewidth undirected graphical models, making maximum likelihood learning a challenge. One way to overcome this is to restrict parameters to a tractable set, most typically the set of tree-structured parameters. This paper explores an alternative notion of a tractable set, nam... | [] | null | 41 | 1509.08992 | title_snapshot |
160c88652d47d0be60bfbfed25111412 | Sampling from Probabilistic Submodular Models | https://proceedings.neurips.cc/paper_files/paper/2015/hash/160c88652d47d0be60bfbfed25111412-Abstract.html | [
"Alkis Gotovos",
"Hamed Hassani",
"Andreas Krause"
] | null | null | Submodular and supermodular functions have found wide applicability in machine learning, capturing notions such as diversity and regularity, respectively. These notions have deep consequences for optimization, and the problem of (approximately) optimizing submodular functions has received much attention. However, beyon... | [] | null | 42 | null | null |
17c3433fecc21b57000debdf7ad5c930 | A class of network models recoverable by spectral clustering | https://proceedings.neurips.cc/paper_files/paper/2015/hash/17c3433fecc21b57000debdf7ad5c930-Abstract.html | [
"Yali Wan",
"Marina Meila"
] | null | null | Finding communities in networks is a problem that remains difficult, in spite of the amount of attention it has recently received. The Stochastic Block-Model (SBM) is a generative model for graphs with communities for which, because of its simplicity, the theoretical understanding has advanced fast in recent years. In ... | [] | null | 43 | 2104.10347 | title_snapshot |
17d63b1625c816c22647a73e1482372b | Closed-form Estimators for High-dimensional Generalized Linear Models | https://proceedings.neurips.cc/paper_files/paper/2015/hash/17d63b1625c816c22647a73e1482372b-Abstract.html | [
"Eunho Yang",
"Aurelie C. Lozano",
"Pradeep K Ravikumar"
] | null | null | We propose a class of closed-form estimators for GLMs under high-dimensional sampling regimes. Our class of estimators is based on deriving closed-form variants of the vanilla unregularized MLE but which are (a) well-defined even under high-dimensional settings, and (b) available in closed-form. We then perform thresho... | [] | null | 44 | null | null |
17e62166fc8586dfa4d1bc0e1742c08b | Expressing an Image Stream with a Sequence of Natural Sentences | https://proceedings.neurips.cc/paper_files/paper/2015/hash/17e62166fc8586dfa4d1bc0e1742c08b-Abstract.html | [
"Cesc C Park",
"Gunhee Kim"
] | null | null | We propose an approach for generating a sequence of natural sentences for an image stream. Since general users usually take a series of pictures on their special moments, much online visual information exists in the form of image streams, for which it would better take into consideration of the whole set to generate na... | [] | null | 45 | null | null |
186a157b2992e7daed3677ce8e9fe40f | Learning spatiotemporal trajectories from manifold-valued longitudinal data | https://proceedings.neurips.cc/paper_files/paper/2015/hash/186a157b2992e7daed3677ce8e9fe40f-Abstract.html | [
"Jean-Baptiste SCHIRATTI",
"Stéphanie ALLASSONNIERE",
"Olivier Colliot",
"Stanley DURRLEMAN"
] | null | null | We propose a Bayesian mixed-effects model to learn typical scenarios of changes from longitudinal manifold-valued data, namely repeated measurements of the same objects or individuals at several points in time. The model allows to estimate a group-average trajectory in the space of measurements. Random variations of th... | [] | null | 46 | null | null |
192fc044e74dffea144f9ac5dc9f3395 | Fast Classification Rates for High-dimensional Gaussian Generative Models | https://proceedings.neurips.cc/paper_files/paper/2015/hash/192fc044e74dffea144f9ac5dc9f3395-Abstract.html | [
"Tianyang Li",
"Adarsh Prasad",
"Pradeep K Ravikumar"
] | null | null | We consider the problem of binary classification when the covariates conditioned on the each of the response values follow multivariate Gaussian distributions. We focus on the setting where the covariance matrices for the two conditional distributions are the same. The corresponding generative model classifier, derived... | [] | null | 47 | null | null |
19de10adbaa1b2ee13f77f679fa1483a | Adaptive Online Learning | https://proceedings.neurips.cc/paper_files/paper/2015/hash/19de10adbaa1b2ee13f77f679fa1483a-Abstract.html | [
"Dylan J Foster",
"Alexander Rakhlin",
"Karthik Sridharan"
] | null | null | We propose a general framework for studying adaptive regret bounds in the online learning setting, subsuming model selection and data-dependent bounds. Given a data- or model-dependent bound we ask, “Does there exist some algorithm achieving this bound?” We show that modifications to recently introduced sequential comp... | [] | null | 48 | 1508.05170 | title_snapshot |
1be3bc32e6564055d5ca3e5a354acbef | Robust Regression via Hard Thresholding | https://proceedings.neurips.cc/paper_files/paper/2015/hash/1be3bc32e6564055d5ca3e5a354acbef-Abstract.html | [
"Kush Bhatia",
"Prateek Jain",
"Purushottam Kar"
] | null | null | We study the problem of Robust Least Squares Regression (RLSR) where several response variables can be adversarially corrupted. More specifically, for a data matrix X \in \R^{p x n} and an underlying model w*, the response vector is generated as y = X'w* + b where b \in n is the corruption vector supported over at most... | [] | null | 49 | 1506.02428 | title_snapshot |
1c65cef3dfd1e00c0b03923a1c591db4 | b-bit Marginal Regression | https://proceedings.neurips.cc/paper_files/paper/2015/hash/1c65cef3dfd1e00c0b03923a1c591db4-Abstract.html | [
"Martin Slawski",
"Ping Li"
] | null | null | We consider the problem of sparse signal recovery from $m$ linear measurements quantized to $b$ bits. $b$-bit Marginal Regression is proposed as recovery algorithm. We study the question of choosing $b$ in the setting of a given budget of bits $B = m \cdot b$ and derive a single easy-to-compute expression characterizin... | [] | null | 50 | null | null |
1ee3dfcd8a0645a25a35977997223d22 | Spectral Norm Regularization of Orthonormal Representations for Graph Transduction | https://proceedings.neurips.cc/paper_files/paper/2015/hash/1ee3dfcd8a0645a25a35977997223d22-Abstract.html | [
"Rakesh Shivanna",
"Bibaswan K Chatterjee",
"Raman Sankaran",
"Chiranjib Bhattacharyya",
"Francis Bach"
] | null | null | Recent literature~\cite{ando} suggests that embedding a graph on an unit sphere leads to better generalization for graph transduction. However, the choice of optimal embedding and an efficient algorithm to compute the same remains open. In this paper, we show that orthonormal representations, a class of unit-sphere gra... | [] | null | 51 | null | null |
1efa39bcaec6f3900149160693694536 | Randomized Block Krylov Methods for Stronger and Faster Approximate Singular Value Decomposition | https://proceedings.neurips.cc/paper_files/paper/2015/hash/1efa39bcaec6f3900149160693694536-Abstract.html | [
"Cameron Musco",
"Christopher Musco"
] | null | null | Since being analyzed by Rokhlin, Szlam, and Tygert and popularized by Halko, Martinsson, and Tropp, randomized Simultaneous Power Iteration has become the method of choice for approximate singular value decomposition. It is more accurate than simpler sketching algorithms, yet still converges quickly for any matrix, ind... | [] | null | 52 | 1504.05477 | title_snapshot |
1f36c15d6a3d18d52e8d493bc8187cb9 | Optimal Testing for Properties of Distributions | https://proceedings.neurips.cc/paper_files/paper/2015/hash/1f36c15d6a3d18d52e8d493bc8187cb9-Abstract.html | [
"Jayadev Acharya",
"Constantinos Daskalakis",
"Gautam Kamath"
] | null | null | Given samples from an unknown distribution, p, is it possible to distinguish whether p belongs to some class of distributions C versus p being far from every distribution in C? This fundamental question has receivedtremendous attention in Statistics, albeit focusing onasymptotic analysis, as well as in Computer Science... | [] | null | 53 | 1507.05952 | title_snapshot |
1f50893f80d6830d62765ffad7721742 | Combinatorial Cascading Bandits | https://proceedings.neurips.cc/paper_files/paper/2015/hash/1f50893f80d6830d62765ffad7721742-Abstract.html | [
"Branislav Kveton",
"Zheng Wen",
"Azin Ashkan",
"Csaba Szepesvari"
] | null | null | We propose combinatorial cascading bandits, a class of partial monitoring problems where at each step a learning agent chooses a tuple of ground items subject to constraints and receives a reward if and only if the weights of all chosen items are one. The weights of the items are binary, stochastic, and drawn independe... | [] | null | 54 | 1507.04208 | title_snapshot |
20b5e1cf8694af7a3c1ba4a87f073021 | Probabilistic Curve Learning: Coulomb Repulsion and the Electrostatic Gaussian Process | https://proceedings.neurips.cc/paper_files/paper/2015/hash/20b5e1cf8694af7a3c1ba4a87f073021-Abstract.html | [
"Ye Wang",
"David B Dunson"
] | null | null | Learning of low dimensional structure in multidimensional data is a canonical problem in machine learning. One common approach is to suppose that the observed data are close to a lower-dimensional smooth manifold. There are a rich variety of manifold learning methods available, which allow mapping of data points to the... | [] | null | 55 | 1506.03768 | title_snapshot |
215a71a12769b056c3c32e7299f1c5ed | Training Very Deep Networks | https://proceedings.neurips.cc/paper_files/paper/2015/hash/215a71a12769b056c3c32e7299f1c5ed-Abstract.html | [
"Rupesh K Srivastava",
"Klaus Greff",
"Jürgen Schmidhuber"
] | null | null | Theoretical and empirical evidence indicates that the depth of neural networks is crucial for their success. However, training becomes more difficult as depth increases, and training of very deep networks remains an open problem. Here we introduce a new architecture designed to overcome this. Our so-called highway netw... | [] | null | 56 | 1507.06228 | title_snapshot |
21be9a4bd4f81549a9d1d241981cec3c | Fast and Memory Optimal Low-Rank Matrix Approximation | https://proceedings.neurips.cc/paper_files/paper/2015/hash/21be9a4bd4f81549a9d1d241981cec3c-Abstract.html | [
"Se-Young Yun",
"marc lelarge",
"Alexandre Proutiere"
] | null | null | In this paper, we revisit the problem of constructing a near-optimal rank $k$ approximation of a matrix $M\in [0,1]^{m\times n}$ under the streaming data model where the columns of $M$ are revealed sequentially. We present SLA (Streaming Low-rank Approximation), an algorithm that is asymptotically accurate, when $k s_{... | [] | null | 57 | null | null |
250cf8b51c773f3f8dc8b4be867a9a02 | Character-level Convolutional Networks for Text Classification | https://proceedings.neurips.cc/paper_files/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html | [
"Xiang Zhang",
"Junbo Zhao",
"Yann LeCun"
] | null | null | This article offers an empirical exploration on the use of character-level convolutional networks (ConvNets) for text classification. We constructed several large-scale datasets to show that character-level convolutional networks could achieve state-of-the-art or competitive results. Comparisons are offered against tra... | [] | null | 58 | 1509.01626 | title_snapshot |
2612aa892d962d6f8056b195ca6e550d | Interactive Control of Diverse Complex Characters with Neural Networks | https://proceedings.neurips.cc/paper_files/paper/2015/hash/2612aa892d962d6f8056b195ca6e550d-Abstract.html | [
"Igor Mordatch",
"Kendall Lowrey",
"Galen Andrew",
"Zoran Popovic",
"Emanuel V. Todorov"
] | null | null | We present a method for training recurrent neural networks to act as near-optimal feedback controllers. It is able to generate stable and realistic behaviors for a range of dynamical systems and tasks -- swimming, flying, biped and quadruped walking with different body morphologies. It does not require motion capture o... | [] | null | 59 | null | null |
26657d5ff9020d2abefe558796b99584 | Inferring Algorithmic Patterns with Stack-Augmented Recurrent Nets | https://proceedings.neurips.cc/paper_files/paper/2015/hash/26657d5ff9020d2abefe558796b99584-Abstract.html | [
"Armand Joulin",
"Tomas Mikolov"
] | null | null | Despite the recent achievements in machine learning, we are still very far from achieving real artificial intelligence. In this paper, we discuss the limitations of standard deep learning approaches and show that some of these limitations can be overcome by learning how to grow the complexity of a model in a structured... | [] | null | 60 | 1503.01007 | title_snapshot |
277281aada22045c03945dcb2ca6f2ec | Grammar as a Foreign Language | https://proceedings.neurips.cc/paper_files/paper/2015/hash/277281aada22045c03945dcb2ca6f2ec-Abstract.html | [
"Oriol Vinyals",
"Łukasz Kaiser",
"Terry Koo",
"Slav Petrov",
"Ilya Sutskever",
"Geoffrey Hinton"
] | null | null | Syntactic constituency parsing is a fundamental problem in naturallanguage processing which has been the subject of intensive researchand engineering for decades. As a result, the most accurate parsersare domain specific, complex, and inefficient. In this paper we showthat the domain agnostic attention-enhanced sequenc... | [] | null | 61 | 1412.7449 | title_snapshot |
2823f4797102ce1a1aec05359cc16dd9 | Practical and Optimal LSH for Angular Distance | https://proceedings.neurips.cc/paper_files/paper/2015/hash/2823f4797102ce1a1aec05359cc16dd9-Abstract.html | [
"Alexandr Andoni",
"Piotr Indyk",
"Thijs Laarhoven",
"Ilya Razenshteyn",
"Ludwig Schmidt"
] | null | null | We show the existence of a Locality-Sensitive Hashing (LSH) family for the angular distance that yields an approximate Near Neighbor Search algorithm with the asymptotically optimal running time exponent. Unlike earlier algorithms with this property (e.g., Spherical LSH (Andoni-Indyk-Nguyen-Razenshteyn 2014) (Andoni-Ra... | [] | null | 62 | 1509.02897 | title_snapshot |
285ab9448d2751ee57ece7f762c39095 | GP Kernels for Cross-Spectrum Analysis | https://proceedings.neurips.cc/paper_files/paper/2015/hash/285ab9448d2751ee57ece7f762c39095-Abstract.html | [
"Kyle R Ulrich",
"David E Carlson",
"Kafui Dzirasa",
"Lawrence Carin"
] | null | null | Multi-output Gaussian processes provide a convenient framework for multi-task problems. An illustrative and motivating example of a multi-task problem is multi-region electrophysiological time-series data, where experimentalists are interested in both power and phase coherence between channels. Recently, Wilson and Ada... | [] | null | 63 | null | null |
285e19f20beded7d215102b49d5c09a0 | A Framework for Individualizing Predictions of Disease Trajectories by Exploiting Multi-Resolution Structure | https://proceedings.neurips.cc/paper_files/paper/2015/hash/285e19f20beded7d215102b49d5c09a0-Abstract.html | [
"Peter Schulam",
"Suchi Saria"
] | null | null | For many complex diseases, there is a wide variety of ways in which an individual can manifest the disease. The challenge of personalized medicine is to develop tools that can accurately predict the trajectory of an individual's disease, which can in turn enable clinicians to optimize treatments. We represent an indivi... | [] | null | 64 | 1601.04674 | title_snapshot |
286674e3082feb7e5afb92777e48821f | Local Smoothness in Variance Reduced Optimization | https://proceedings.neurips.cc/paper_files/paper/2015/hash/286674e3082feb7e5afb92777e48821f-Abstract.html | [
"Daniel Vainsencher",
"Han Liu",
"Tong Zhang"
] | null | null | Abstract We propose a family of non-uniform sampling strategies to provably speed up a class of stochastic optimization algorithms with linear convergence including Stochastic Variance Reduced Gradient (SVRG) and Stochastic Dual Coordinate Ascent (SDCA). For a large family of penalized empirical risk minimization probl... | [] | null | 65 | null | null |
28dd2c7955ce926456240b2ff0100bde | Unlocking neural population non-stationarities using hierarchical dynamics models | https://proceedings.neurips.cc/paper_files/paper/2015/hash/28dd2c7955ce926456240b2ff0100bde-Abstract.html | [
"Mijung Park",
"Gergo Bohner",
"Jakob H. Macke"
] | null | null | Neural population activity often exhibits rich variability. This variability is thought to arise from single-neuron stochasticity, neural dynamics on short time-scales, as well as from modulations of neural firing properties on long time-scales, often referred to as non-stationarity. To better understand the nature of ... | [] | null | 66 | null | null |
29921001f2f04bd3baee84a12e98098f | Pointer Networks | https://proceedings.neurips.cc/paper_files/paper/2015/hash/29921001f2f04bd3baee84a12e98098f-Abstract.html | [
"Oriol Vinyals",
"Meire Fortunato",
"Navdeep Jaitly"
] | null | null | We introduce a new neural architecture to learn the conditional probability of an output sequence with elements that arediscrete tokens corresponding to positions in an input sequence.Such problems cannot be trivially addressed by existent approaches such as sequence-to-sequence and Neural Turing Machines,because the n... | [] | null | 67 | 1506.03134 | title_snapshot |
2a38a4a9316c49e5a833517c45d31070 | Fast and Accurate Inference of Plackett–Luce Models | https://proceedings.neurips.cc/paper_files/paper/2015/hash/2a38a4a9316c49e5a833517c45d31070-Abstract.html | [
"Lucas Maystre",
"Matthias Grossglauser"
] | null | null | We show that the maximum-likelihood (ML) estimate of models derived from Luce's choice axiom (e.g., the Plackett-Luce model) can be expressed as the stationary distribution of a Markov chain. This conveys insight into several recently proposed spectral inference algorithms. We take advantage of this perspective and for... | [] | null | 68 | null | null |
2b38c2df6a49b97f706ec9148ce48d86 | Learning Bayesian Networks with Thousands of Variables | https://proceedings.neurips.cc/paper_files/paper/2015/hash/2b38c2df6a49b97f706ec9148ce48d86-Abstract.html | [
"Mauro Scanagatta",
"Cassio P de Campos",
"Giorgio Corani",
"Marco Zaffalon"
] | null | null | We present a method for learning Bayesian networks from data sets containingthousands of variables without the need for structure constraints. Our approachis made of two parts. The first is a novel algorithm that effectively explores thespace of possible parent sets of a node. It guides the exploration towards themost ... | [] | null | 69 | null | null |
2b3bf3eee2475e03885a110e9acaab61 | Differentially Private Learning of Structured Discrete Distributions | https://proceedings.neurips.cc/paper_files/paper/2015/hash/2b3bf3eee2475e03885a110e9acaab61-Abstract.html | [
"Ilias Diakonikolas",
"Moritz Hardt",
"Ludwig Schmidt"
] | null | null | We investigate the problem of learning an unknown probability distribution over a discrete population from random samples. Our goal is to design efficient algorithms that simultaneously achieve low error in total variation norm while guaranteeing Differential Privacy to the individuals of the population.We describe a g... | [] | null | 70 | null | null |
2b6d65b9a9445c4271ab9076ead5605a | Generative Image Modeling Using Spatial LSTMs | https://proceedings.neurips.cc/paper_files/paper/2015/hash/2b6d65b9a9445c4271ab9076ead5605a-Abstract.html | [
"Lucas Theis",
"Matthias Bethge"
] | null | null | Modeling the distribution of natural images is challenging, partly because of strong statistical dependencies which can extend over hundreds of pixels. Recurrent neural networks have been successful in capturing long-range dependencies in a number of problems but only recently have found their way into generative image... | [] | null | 71 | 1506.03478 | title_snapshot |
2b8a61594b1f4c4db0902a8a395ced93 | Sparse PCA via Bipartite Matchings | https://proceedings.neurips.cc/paper_files/paper/2015/hash/2b8a61594b1f4c4db0902a8a395ced93-Abstract.html | [
"Megasthenis Asteris",
"Dimitris Papailiopoulos",
"Anastasios Kyrillidis",
"Alexandros G Dimakis"
] | null | null | We consider the following multi-component sparse PCA problem:given a set of data points, we seek to extract a small number of sparse components with \emph{disjoint} supports that jointly capture the maximum possible variance.Such components can be computed one by one, repeatedly solving the single-component problem and... | [] | null | 72 | 1508.00625 | title_snapshot |
2bd7f907b7f5b6bbd91822c0c7b835f6 | Market Scoring Rules Act As Opinion Pools For Risk-Averse Agents | https://proceedings.neurips.cc/paper_files/paper/2015/hash/2bd7f907b7f5b6bbd91822c0c7b835f6-Abstract.html | [
"Mithun Chakraborty",
"Sanmay Das"
] | null | null | A market scoring rule (MSR) – a popular tool for designing algorithmic prediction markets – is an incentive-compatible mechanism for the aggregation of probabilistic beliefs from myopic risk-neutral agents. In this paper, we add to a growing body of research aimed at understanding the precise manner in which the price ... | [] | null | 73 | null | null |
2d00f43f07911355d4151f13925ff292 | Lifted Inference Rules With Constraints | https://proceedings.neurips.cc/paper_files/paper/2015/hash/2d00f43f07911355d4151f13925ff292-Abstract.html | [
"Happy Mittal",
"Anuj Mahajan",
"Vibhav G Gogate",
"Parag Singla"
] | null | null | Lifted inference rules exploit symmetries for fast reasoning in statistical rela-tional models. Computational complexity of these rules is highly dependent onthe choice of the constraint language they operate on and therefore coming upwith the right kind of representation is critical to the success of lifted inference.... | [] | null | 74 | null | null |
2d1b2a5ff364606ff041650887723470 | LASSO with Non-linear Measurements is Equivalent to One With Linear Measurements | https://proceedings.neurips.cc/paper_files/paper/2015/hash/2d1b2a5ff364606ff041650887723470-Abstract.html | [
"CHRISTOS THRAMPOULIDIS",
"Ehsan Abbasi",
"Babak Hassibi"
] | null | null | Consider estimating an unknown, but structured (e.g. sparse, low-rank, etc.), signal $x_0\in R^n$ from a vector $y\in R^m$ of measurements of the form $y_i=g_i(a_i^Tx_0)$, where the $a_i$'s are the rows of a known measurement matrix $A$, and, $g$ is a (potentially unknown) nonlinear and random link-function. Such measu... | [] | null | 75 | 1506.02181 | title_judge |
2de5d16682c3c35007e4e92982f1a2ba | Natural Neural Networks | https://proceedings.neurips.cc/paper_files/paper/2015/hash/2de5d16682c3c35007e4e92982f1a2ba-Abstract.html | [
"Guillaume Desjardins",
"Karen Simonyan",
"Razvan Pascanu",
"koray kavukcuoglu"
] | null | null | We introduce Natural Neural Networks, a novel family of algorithms that speed up convergence by adapting their internal representation during training to improve conditioning of the Fisher matrix. In particular, we show a specific example that employs a simple and efficient reparametrization of the neural network weigh... | [] | null | 76 | 1507.00210 | title_snapshot |
2e65f2f2fdaf6c699b223c61b1b5ab89 | Scalable Adaptation of State Complexity for Nonparametric Hidden Markov Models | https://proceedings.neurips.cc/paper_files/paper/2015/hash/2e65f2f2fdaf6c699b223c61b1b5ab89-Abstract.html | [
"Michael C Hughes",
"William T Stephenson",
"Erik Sudderth"
] | null | null | Bayesian nonparametric hidden Markov models are typically learned via fixed truncations of the infinite state space or local Monte Carlo proposals that make small changes to the state space. We develop an inference algorithm for the sticky hierarchical Dirichlet process hidden Markov model that scales to big datasets b... | [] | null | 77 | null | null |
2f25f6e326adb93c5787175dda209ab6 | Inference for determinantal point processes without spectral knowledge | https://proceedings.neurips.cc/paper_files/paper/2015/hash/2f25f6e326adb93c5787175dda209ab6-Abstract.html | [
"Rémi Bardenet",
"Michalis Titsias RC AUEB"
] | null | null | Determinantal point processes (DPPs) are point process models thatnaturally encode diversity between the points of agiven realization, through a positive definite kernel $K$. DPPs possess desirable properties, such as exactsampling or analyticity of the moments, but learning the parameters ofkernel $K$ through likeliho... | [] | null | 78 | 1507.01154 | title_snapshot |
309928d4b100a5d75adff48a9bfc1ddb | A Bayesian Framework for Modeling Confidence in Perceptual Decision Making | https://proceedings.neurips.cc/paper_files/paper/2015/hash/309928d4b100a5d75adff48a9bfc1ddb-Abstract.html | [
"Koosha Khalvati",
"Rajesh P. Rao"
] | null | null | The degree of confidence in one's choice or decision is a critical aspect of perceptual decision making. Attempts to quantify a decision maker's confidence by measuring accuracy in a task have yielded limited success because confidence and accuracy are typically not equal. In this paper, we introduce a Bayesian framewo... | [] | null | 79 | null | null |
309fee4e541e51de2e41f21bebb342aa | Sample Complexity of Episodic Fixed-Horizon Reinforcement Learning | https://proceedings.neurips.cc/paper_files/paper/2015/hash/309fee4e541e51de2e41f21bebb342aa-Abstract.html | [
"Christoph Dann",
"Emma Brunskill"
] | null | null | Recently, there has been significant progress in understanding reinforcement learning in discounted infinite-horizon Markov decision processes (MDPs) by deriving tight sample complexity bounds. However, in many real-world applications, an interactive learning agent operates for a fixed or bounded period of time, for ex... | [] | null | 80 | 1510.08906 | title_snapshot |
310dcbbf4cce62f762a2aaa148d556bd | Algorithms with Logarithmic or Sublinear Regret for Constrained Contextual Bandits | https://proceedings.neurips.cc/paper_files/paper/2015/hash/310dcbbf4cce62f762a2aaa148d556bd-Abstract.html | [
"Huasen Wu",
"R. Srikant",
"Xin Liu",
"Chong Jiang"
] | null | null | We study contextual bandits with budget and time constraints under discrete contexts, referred to as constrained contextual bandits. The time and budget constraints significantly complicate the exploration and exploitation tradeoff because they introduce complex coupling among contexts over time. To gain insight, we fi... | [] | null | 81 | 1504.06937 | title_snapshot |
312351bff07989769097660a56395065 | Latent Bayesian melding for integrating individual and population models | https://proceedings.neurips.cc/paper_files/paper/2015/hash/312351bff07989769097660a56395065-Abstract.html | [
"Mingjun Zhong",
"Nigel Goddard",
"Charles Sutton"
] | null | null | In many statistical problems, a more coarse-grained model may be suitable for population-level behaviour, whereas a more detailed model is appropriate for accurate modelling of individual behaviour. This raises the question of how to integrate both types of models. Methods such as posterior regularization follow the id... | [] | null | 82 | 1510.09130 | title_snapshot |
31857b449c407203749ae32dd0e7d64a | Regressive Virtual Metric Learning | https://proceedings.neurips.cc/paper_files/paper/2015/hash/31857b449c407203749ae32dd0e7d64a-Abstract.html | [
"Michaël Perrot",
"Amaury Habrard"
] | null | null | We are interested in supervised metric learning of Mahalanobis like distances. Existing approaches mainly focus on learning a new distance using similarity and dissimilarity constraints between examples. In this paper, instead of bringing closer examples of the same class and pushing far away examples of different clas... | [] | null | 83 | null | null |
31b3b31a1c2f8a370206f111127c0dbd | Halting in Random Walk Kernels | https://proceedings.neurips.cc/paper_files/paper/2015/hash/31b3b31a1c2f8a370206f111127c0dbd-Abstract.html | [
"Mahito Sugiyama",
"Karsten Borgwardt"
] | null | null | Random walk kernels measure graph similarity by counting matching walks in two graphs. In their most popular form of geometric random walk kernels, longer walks of length $k$ are downweighted by a factor of $\lambda^k$ ($\lambda < 1$) to ensure convergence of the corresponding geometric series. We know from the field o... | [] | null | 84 | null | null |
3214a6d842cc69597f9edf26df552e43 | Kullback-Leibler Proximal Variational Inference | https://proceedings.neurips.cc/paper_files/paper/2015/hash/3214a6d842cc69597f9edf26df552e43-Abstract.html | [
"Mohammad Emtiyaz Khan",
"Pierre Baque",
"François Fleuret",
"Pascal Fua"
] | null | null | We propose a new variational inference method based on the Kullback-Leibler (KL) proximal term. We make two contributions towards improving efficiency of variational inference. Firstly, we derive a KL proximal-point algorithm and show its equivalence to gradient descent with natural gradient in stochastic variational i... | [] | null | 85 | null | null |
32bb90e8976aab5298d5da10fe66f21d | A Convergent Gradient Descent Algorithm for Rank Minimization and Semidefinite Programming from Random Linear Measurements | https://proceedings.neurips.cc/paper_files/paper/2015/hash/32bb90e8976aab5298d5da10fe66f21d-Abstract.html | [
"Qinqing Zheng",
"John Lafferty"
] | null | null | We propose a simple, scalable, and fast gradient descent algorithm to optimize a nonconvex objective for the rank minimization problem and a closely related family of semidefinite programs. With $O(r^3 \kappa^2 n \log n)$ random measurements of a positive semidefinite $n\times n$ matrix of rank $r$ and condition number... | [] | null | 86 | 1506.06081 | title_snapshot |
333222170ab9edca4785c39f55221fe7 | On-the-Job Learning with Bayesian Decision Theory | https://proceedings.neurips.cc/paper_files/paper/2015/hash/333222170ab9edca4785c39f55221fe7-Abstract.html | [
"Keenon Werling",
"Arun Tejasvi Chaganty",
"Percy Liang",
"Christopher D. Manning"
] | null | null | Our goal is to deploy a high-accuracy system starting with zero training examples. We consider an “on-the-job” setting, where as inputs arrive, we use real-time crowdsourcing to resolve uncertainty where needed and output our prediction when confident. As the model improves over time, the reliance on crowdsourcing quer... | [] | null | 87 | 1506.03140 | title_snapshot |
33ceb07bf4eeb3da587e268d663aba1a | Spatial Transformer Networks | https://proceedings.neurips.cc/paper_files/paper/2015/hash/33ceb07bf4eeb3da587e268d663aba1a-Abstract.html | [
"Max Jaderberg",
"Karen Simonyan",
"Andrew Zisserman",
"koray kavukcuoglu"
] | null | null | Convolutional Neural Networks define an exceptionallypowerful class of model, but are still limited by the lack of abilityto be spatially invariant to the input data in a computationally and parameterefficient manner. In this work we introduce a new learnable module, theSpatial Transformer, which explicitly allows the ... | [] | null | 88 | 1506.02025 | title_snapshot |
33e8075e9970de0cfea955afd4644bb2 | Precision-Recall-Gain Curves: PR Analysis Done Right | https://proceedings.neurips.cc/paper_files/paper/2015/hash/33e8075e9970de0cfea955afd4644bb2-Abstract.html | [
"Peter Flach",
"Meelis Kull"
] | null | null | Precision-Recall analysis abounds in applications of binary classification where true negatives do not add value and hence should not affect assessment of the classifier's performance. Perhaps inspired by the many advantages of receiver operating characteristic (ROC) curves and the area under such curves for accuracy-b... | [] | null | 89 | null | null |
3416a75f4cea9109507cacd8e2f2aefc | Planar Ultrametrics for Image Segmentation | https://proceedings.neurips.cc/paper_files/paper/2015/hash/3416a75f4cea9109507cacd8e2f2aefc-Abstract.html | [
"Julian E Yarkony",
"Charless Fowlkes"
] | null | null | We study the problem of hierarchical clustering on planar graphs. We formulate this in terms of finding the closest ultrametric to a specified set of distances and solve it using an LP relaxation that leverages minimum cost perfect matching as a subroutine to efficiently explore the space of planar partitions. We apply... | [] | null | 90 | null | null |
35051070e572e47d2c26c241ab88307f | Sparse Local Embeddings for Extreme Multi-label Classification | https://proceedings.neurips.cc/paper_files/paper/2015/hash/35051070e572e47d2c26c241ab88307f-Abstract.html | [
"Kush Bhatia",
"Himanshu Jain",
"Purushottam Kar",
"Manik Varma",
"Prateek Jain"
] | null | null | The objective in extreme multi-label learning is to train a classifier that can automatically tag a novel data point with the most relevant subset of labels from an extremely large label set. Embedding based approaches make training and prediction tractable by assuming that the training label matrix is low-rank and hen... | [] | null | 91 | null | null |
351b33587c5fdd93bd42ef7ac9995a28 | Super-Resolution Off the Grid | https://proceedings.neurips.cc/paper_files/paper/2015/hash/351b33587c5fdd93bd42ef7ac9995a28-Abstract.html | [
"Qingqing Huang",
"Sham M. Kakade"
] | null | null | Super-resolution is the problem of recovering a superposition of point sources using bandlimited measurements, which may be corrupted with noise. This signal processing problem arises in numerous imaging problems, ranging from astronomy to biology to spectroscopy, where it is common to take (coarse) Fourier measurement... | [] | null | 92 | 1509.07943 | title_snapshot |
352fe25daf686bdb4edca223c921acea | Automatic Variational Inference in Stan | https://proceedings.neurips.cc/paper_files/paper/2015/hash/352fe25daf686bdb4edca223c921acea-Abstract.html | [
"Alp Kucukelbir",
"Rajesh Ranganath",
"Andrew Gelman",
"David Blei"
] | null | null | Variational inference is a scalable technique for approximate Bayesian inference. Deriving variational inference algorithms requires tedious model-specific calculations; this makes it difficult for non-experts to use. We propose an automatic variational inference algorithm, automatic differentiation variational inferen... | [] | null | 93 | 1506.03431 | title_snapshot |
3636638817772e42b59d74cff571fbb3 | Extending Gossip Algorithms to Distributed Estimation of U-statistics | https://proceedings.neurips.cc/paper_files/paper/2015/hash/3636638817772e42b59d74cff571fbb3-Abstract.html | [
"Igor Colin",
"Aurélien Bellet",
"Joseph Salmon",
"Stéphan Clémençon"
] | null | null | Efficient and robust algorithms for decentralized estimation in networks are essential to many distributed systems. Whereas distributed estimation of sample mean statistics has been the subject of a good deal of attention, computation of U-statistics, relying on more expensive averaging over pairs of observations, is a... | [] | null | 94 | 1511.05464 | title_snapshot |
36ac8e558ac7690b6f44e2cb5ef93322 | Model-Based Relative Entropy Stochastic Search | https://proceedings.neurips.cc/paper_files/paper/2015/hash/36ac8e558ac7690b6f44e2cb5ef93322-Abstract.html | [
"Abbas Abdolmaleki",
"Rudolf Lioutikov",
"Jan R Peters",
"Nuno Lau",
"Luis Pualo Reis",
"Gerhard Neumann"
] | null | null | Stochastic search algorithms are general black-box optimizers. Due to their ease of use and their generality, they have recently also gained a lot of attention in operations research, machine learning and policy search. Yet, these algorithms require a lot of evaluations of the objective, scale poorly with the problem d... | [] | null | 95 | null | null |
378a063b8fdb1db941e34f4bde584c7d | Semi-supervised Learning with Ladder Networks | https://proceedings.neurips.cc/paper_files/paper/2015/hash/378a063b8fdb1db941e34f4bde584c7d-Abstract.html | [
"Antti Rasmus",
"Mathias Berglund",
"Mikko Honkala",
"Harri Valpola",
"Tapani Raiko"
] | null | null | We combine supervised learning with unsupervised learning in deep neural networks. The proposed model is trained to simultaneously minimize the sum of supervised and unsupervised cost functions by backpropagation, avoiding the need for layer-wise pre-training. Our work builds on top of the Ladder network proposed by Va... | [] | null | 96 | 1507.02672 | title_snapshot |
37f0e884fbad9667e38940169d0a3c95 | Empirical Localization of Homogeneous Divergences on Discrete Sample Spaces | https://proceedings.neurips.cc/paper_files/paper/2015/hash/37f0e884fbad9667e38940169d0a3c95-Abstract.html | [
"Takashi Takenouchi",
"Takafumi Kanamori"
] | null | null | In this paper, we propose a novel parameter estimator for probabilistic models on discrete space. The proposed estimator is derived from minimization of homogeneous divergence and can be constructed without calculation of the normalization constant, which is frequently infeasible for models in the discrete space. We in... | [] | null | 97 | null | null |
3871bd64012152bfb53fdf04b401193f | Enforcing balance allows local supervised learning in spiking recurrent networks | https://proceedings.neurips.cc/paper_files/paper/2015/hash/3871bd64012152bfb53fdf04b401193f-Abstract.html | [
"Ralph Bourdoukan",
"Sophie Denève"
] | null | null | To predict sensory inputs or control motor trajectories, the brain must constantly learn temporal dynamics based on error feedback. However, it remains unclear how such supervised learning is implemented in biological neural networks. Learning in recurrent spiking networks is notoriously difficult because local changes... | [] | null | 98 | null | null |
38913e1d6a7b94cb0f55994f679f5956 | Online Learning for Adversaries with Memory: Price of Past Mistakes | https://proceedings.neurips.cc/paper_files/paper/2015/hash/38913e1d6a7b94cb0f55994f679f5956-Abstract.html | [
"Oren Anava",
"Elad Hazan",
"Shie Mannor"
] | null | null | The framework of online learning with memory naturally captures learning problems with temporal effects, and was previously studied for the experts setting. In this work we extend the notion of learning with memory to the general Online Convex Optimization (OCO) framework, and present two algorithms that attain low reg... | [] | null | 99 | null | null |
38af86134b65d0f10fe33d30dd76442e | Streaming, Distributed Variational Inference for Bayesian Nonparametrics | https://proceedings.neurips.cc/paper_files/paper/2015/hash/38af86134b65d0f10fe33d30dd76442e-Abstract.html | [
"Trevor Campbell",
"Julian Straub",
"John W. Fisher III",
"Jonathan P How"
] | null | null | This paper presents a methodology for creating streaming, distributed inference algorithms for Bayesian nonparametric (BNP) models. In the proposed framework, processing nodes receive a sequence of data minibatches, compute a variational posterior for each, and make asynchronous streaming updates to a central model. In... | [] | null | 100 | 1510.09161 | title_snapshot |
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