NeurIPS 2013

360 papers

On the Representational Efficiency of Restricted Boltzmann Machines

James Martens, Arkadev Chattopadhya, Toni Pitassi, Richard Zemel

Adaptive dropout for training deep neural networks

Jimmy Ba, Brendan Frey

Pass-efficient unsupervised feature selection

Crystal Maung, Haim Schweitzer

On the Complexity and Approximation of Binary Evidence in Lifted Inference

Guy Van den Broeck, Adnan Darwiche

Recurrent linear models of simultaneously-recorded neural populations

Marius Pachitariu, Biljana Petreska, Maneesh Sahani

Σ-Optimality for Active Learning on Gaussian Random Fields

Yifei Ma, Roman Garnett, Jeff Schneider

Memory Limited, Streaming PCA

Ioannis Mitliagkas, Constantine Caramanis, Prateek Jain

Dimension-Free Exponentiated Gradient

Francesco Orabona

Learning Feature Selection Dependencies in Multi-task Learning

Daniel Hernández-lobato, José Miguel Hernández-Lobato

Approximate Dynamic Programming Finally Performs Well in the Game of Tetris

Victor Gabillon, Mohammad Ghavamzadeh, Bruno Scherrer

The Pareto Regret Frontier

Wouter M. Koolen

Supervised Sparse Analysis and Synthesis Operators

Pablo Sprechmann, Roee Litman, Tal Ben Yakar, Alexander M Bronstein, Guillermo Sapiro

Understanding Dropout

Pierre Baldi, Peter J Sadowski

Correlations strike back (again): the case of associative memory retrieval

Cristina Savin, Peter Dayan, Máté Lengyel

What Are the Invariant Occlusive Components of Image Patches? A Probabilistic Generative Approach

Zhenwen Dai, Georgios Exarchakis, Jörg Lücke

Universal models for binary spike patterns using centered Dirichlet processes

ll Memming Park, Evan W Archer, Kenneth Latimer, Jonathan W Pillow

A Comparative Framework for Preconditioned Lasso Algorithms

Fabian L. Wauthier, Nebojsa Jojic, Michael I. Jordan, Michael I Jordan

Near-Optimal Entrywise Sampling for Data Matrices

Dimitris Achlioptas, Zohar S Karnin, Edo Liberty

Optimal Neural Population Codes for High-dimensional Stimulus Variables

Zhuo Wang, Alan Stocker, Daniel D. Lee, Daniel D Lee

Embed and Project: Discrete Sampling with Universal Hashing

Stefano Ermon, Carla P. Gomes, Ashish Sabharwal, Bart Selman

Stochastic Optimization of PCA with Capped MSG

Raman Arora, Andy Cotter, Nati Srebro

Forgetful Bayes and myopic planning: Human learning and decision-making in a bandit setting

Shunan Zhang, Angela J. Yu

(More) Efficient Reinforcement Learning via Posterior Sampling

Ian Osband, Daniel Russo, Benjamin Van Roy

Bayesian Estimation of Latently-grouped Parameters in Undirected Graphical Models

Jie Liu, David Page

Decision Jungles: Compact and Rich Models for Classification

Jamie Shotton, Toby Sharp, Pushmeet Kohli, Sebastian Nowozin, John Winn

Error-Minimizing Estimates and Universal Entry-Wise Error Bounds for Low-Rank Matrix Completion

Franz Kiraly, Louis Theran

Global MAP-Optimality by Shrinking the Combinatorial Search Area with Convex Relaxation

Bogdan Savchynskyy, Jörg Hendrik Kappes, Paul Swoboda, Christoph Schnörr

Bayesian inference for low rank spatiotemporal neural receptive fields

Mijung Park, Jonathan W Pillow

Online learning in episodic Markovian decision processes by relative entropy policy search

Alexander Zimin, Gergely Neu

Non-Uniform Camera Shake Removal Using a Spatially-Adaptive Sparse Penalty

Haichao Zhang, David Wipf

Gaussian Process Conditional Copulas with Applications to Financial Time Series

José Miguel Hernández-Lobato, James R Lloyd, Daniel Hernández-lobato

Discovering Hidden Variables in Noisy-Or Networks using Quartet Tests

Yacine Jernite, Yonatan Halpern, David Sontag

Online Learning of Dynamic Parameters in Social Networks

Shahin Shahrampour, Sasha Rakhlin, Ali Jadbabaie

Convex Relaxations for Permutation Problems

Fajwel Fogel, Rodolphe Jenatton, Francis Bach, Alexandre D'aspremont

Faster Ridge Regression via the Subsampled Randomized Hadamard Transform

Yichao Lu, Paramveer Dhillon, Dean P. Foster, Lyle Ungar

Reservoir Boosting : Between Online and Offline Ensemble Learning

Leonidas Lefakis, Francois Fleuret

Global Solver and Its Efficient Approximation for Variational Bayesian Low-rank Subspace Clustering

Shinichi Nakajima, Akiko Takeda, S. Derin Babacan, Masashi Sugiyama, Ichiro Takeuchi

Probabilistic Low-Rank Matrix Completion with Adaptive Spectral Regularization Algorithms

Adrien Todeschini, Francois Caron, Marie Chavent

Sparse nonnegative deconvolution for compressive calcium imaging: algorithms and phase transitions

Eftychios A Pnevmatikakis, Liam Paninski

Compressive Feature Learning

Hristo S Paskov, Robert West, John C Mitchell, Trevor Hastie

Reflection methods for user-friendly submodular optimization

Stefanie Jegelka, Francis Bach, Suvrit Sra

Modeling Overlapping Communities with Node Popularities

Prem Gopalan, Chong Wang, David Blei

Inverse Density as an Inverse Problem: the Fredholm Equation Approach

Qichao Que, Mikhail Belkin

Low-rank matrix reconstruction and clustering via approximate message passing

Ryosuke Matsushita, Toshiyuki Tanaka

Projecting Ising Model Parameters for Fast Mixing

Justin Domke, Xianghang Liu

It is all in the noise: Efficient multi-task Gaussian process inference with structured residuals

Barbara Rakitsch, Christoph Lippert, Karsten Borgwardt, Oliver Stegle

Distributed Exploration in Multi-Armed Bandits

Eshcar Hillel, Zohar S Karnin, Tomer Koren, Ronny Lempel, Oren Somekh

Reward Mapping for Transfer in Long-Lived Agents

Xiaoxiao Guo, Satinder Singh, Richard L. Lewis

Local Privacy and Minimax Bounds: Sharp Rates for Probability Estimation

John Duchi, Martin J. Wainwright, Michael I. Jordan, Michael I Jordan

Generalized Denoising Auto-Encoders as Generative Models

Yoshua Bengio, Li Yao, Guillaume Alain, Pascal Vincent

Parametric Task Learning

Ichiro Takeuchi, Tatsuya Hongo, Masashi Sugiyama, Shinichi Nakajima

Dynamic Clustering via Asymptotics of the Dependent Dirichlet Process Mixture

Trevor Campbell, Miao Liu, Brian Kulis, Jonathan P How, Lawrence Carin

Estimating the Unseen: Improved Estimators for Entropy and other Properties

Paul Valiant, Gregory Valiant

RNADE: The real-valued neural autoregressive density-estimator

Benigno Uria, Iain Murray, Hugo Larochelle

Online Variational Approximations to non-Exponential Family Change Point Models: With Application to Radar Tracking

Ryan D Turner, Steven Bottone, Clay J Stanek

Large Scale Distributed Sparse Precision Estimation

Huahua Wang, Arindam Banerjee, Cho-jui Hsieh, Pradeep K Ravikumar, Inderjit S Dhillon

One-shot learning by inverting a compositional causal process

Brenden M Lake, Ruslan Salakhutdinov, Josh Tenenbaum

Streaming Variational Bayes

Tamara Broderick, Nicholas Boyd, Andre Wibisono, Ashia C Wilson, Michael I. Jordan

Approximate Inference in Continuous Determinantal Processes

Raja Hafiz Affandi, Emily B. Fox, Ben Taskar

Improved and Generalized Upper Bounds on the Complexity of Policy Iteration

Bruno Scherrer

Online Learning in Markov Decision Processes with Adversarially Chosen Transition Probability Distributions

Yasin Abbasi Yadkori, Varun Kanade, Yevgeny Seldin, Csaba Szepesvári, Peter L. Bartlett

Stochastic Majorization-Minimization Algorithms for Large-Scale Optimization

Julien Mairal

Fast Algorithms for Gaussian Noise Invariant Independent Component Analysis

James R Voss, Luis Rademacher, Mikhail Belkin

Learning the Local Statistics of Optical Flow

Dan Rosenbaum, Daniel Zoran, Yair Weiss

Robust Low Rank Kernel Embeddings of Multivariate Distributions

Le Song, Bo Dai

Better Approximation and Faster Algorithm Using the Proximal Average

Yaoliang Yu, Yao-Liang Yu

Causal Inference on Time Series using Restricted Structural Equation Models

Jonas Peters, Dominik Janzing, Bernhard Schölkopf

Robust learning of low-dimensional dynamics from large neural ensembles

David Pfau, Eftychios A Pnevmatikakis, Liam Paninski

Efficient Optimization for Sparse Gaussian Process Regression

Yanshuai Cao, Marcus A Brubaker, David J Fleet, Aaron Hertzmann

Minimax Theory for High-dimensional Gaussian Mixtures with Sparse Mean Separation

Martin Azizyan, Aarti Singh, Larry Wasserman

Factorized Asymptotic Bayesian Inference for Latent Feature Models

Kohei Hayashi, Ryohei Fujimaki

On Sampling from the Gibbs Distribution with Random Maximum A-Posteriori Perturbations

Tamir Hazan, Subhransu Maji, Tommi Jaakkola

On Poisson Graphical Models

Eunho Yang, Pradeep K Ravikumar, Genevera I Allen, Zhandong Liu

Buy-in-Bulk Active Learning

Liu Yang, Jaime Carbonell

Efficient Algorithm for Privately Releasing Smooth Queries

Ziteng Wang, Kai Fan, Jiaqi Zhang, Liwei Wang

Eluder Dimension and the Sample Complexity of Optimistic Exploration

Daniel Russo, Benjamin Van Roy

On the Linear Convergence of the Proximal Gradient Method for Trace Norm Regularization

Ke Hou, Zirui Zhou, Anthony Man-Cho So, Zhi-Quan Luo

Restricting exchangeable nonparametric distributions

Sinead A Williamson, Steve N MacEachern, Eric P. Xing, Eric P Xing

Auditing: Active Learning with Outcome-Dependent Query Costs

Sivan Sabato, Anand D Sarwate, Nati Srebro

Solving the multi-way matching problem by permutation synchronization

Deepti Pachauri, Risi Kondor, Vikas Singh

A message-passing algorithm for multi-agent trajectory planning

José Bento, Nate Derbinsky, Javier Alonso-Mora, Jonathan S. Yedidia

New Subsampling Algorithms for Fast Least Squares Regression

Paramveer Dhillon, Yichao Lu, Dean P. Foster, Lyle Ungar

Manifold-based Similarity Adaptation for Label Propagation

Masayuki Karasuyama, Hiroshi Mamitsuka

Capacity of strong attractor patterns to model behavioural and cognitive prototypes

Abbas Edalat

Geometric optimisation on positive definite matrices for elliptically contoured distributions

Suvrit Sra, Reshad Hosseini

Prior-free and prior-dependent regret bounds for Thompson Sampling

Sébastien Bubeck, Che-Yu Liu

Dropout Training as Adaptive Regularization

Stefan Wager, Sida Wang, Percy Liang

Variational Policy Search via Trajectory Optimization

Sergey Levine, Vladlen Koltun

Learning with Noisy Labels

Nagarajan Natarajan, Inderjit S Dhillon, Pradeep K Ravikumar, Ambuj Tewari

Linear Convergence with Condition Number Independent Access of Full Gradients

Lijun Zhang, Mehrdad Mahdavi, Rong Jin

Deep Fisher Networks for Large-Scale Image Classification

Karen Simonyan, Andrea Vedaldi, Andrew Zisserman

Contrastive Learning Using Spectral Methods

James Y Zou, Daniel J. Hsu, David C. Parkes, Ryan P. Adams

Bayesian Hierarchical Community Discovery

Charles Blundell, Yee Whye Teh

Stochastic Convex Optimization with Multiple Objectives

Mehrdad Mahdavi, Tianbao Yang, Rong Jin

Wavelets on Graphs via Deep Learning

Raif Rustamov, Leonidas Guibas

Solving inverse problem of Markov chain with partial observations

Tetsuro Morimura, Takayuki Osogami, Tsuyoshi Ide

Transfer Learning in a Transductive Setting

Marcus Rohrbach, Sandra Ebert, Bernt Schiele

Sign Cauchy Projections and Chi-Square Kernel

Ping Li, Gennady Samorodnitsk, John Hopcroft

When are Overcomplete Topic Models Identifiable? Uniqueness of Tensor Tucker Decompositions with Structured Sparsity

Anima Anandkumar, Daniel J. Hsu, Majid Janzamin, Sham M. Kakade

Stochastic Gradient Riemannian Langevin Dynamics on the Probability Simplex

Sam Patterson, Yee Whye Teh