NeurIPS 2015

403 papers

Recognizing retinal ganglion cells in the dark

Emile Richard, Georges A Goetz, E. J. Chichilnisky

Local Causal Discovery of Direct Causes and Effects

Tian Gao, Qiang Ji

Learning Large-Scale Poisson DAG Models based on OverDispersion Scoring

Gunwoong Park, Garvesh Raskutti

Cross-Domain Matching for Bag-of-Words Data via Kernel Embeddings of Latent Distributions

Yuya Yoshikawa, Tomoharu Iwata, Hiroshi Sawada, Takeshi Yamada

Fast Second Order Stochastic Backpropagation for Variational Inference

Kai Fan, Ziteng Wang, Jeff Beck, James Kwok, Katherine A. Heller

On the Pseudo-Dimension of Nearly Optimal Auctions

Jamie H Morgenstern, Tim Roughgarden

Are You Talking to a Machine? Dataset and Methods for Multilingual Image Question

Haoyuan Gao, Junhua Mao, Jie Zhou, Zhiheng Huang, Lei Wang

The Brain Uses Reliability of Stimulus Information when Making Perceptual Decisions

Sebastian Bitzer, Stefan Kiebel

Decomposition Bounds for Marginal MAP

Wei Ping, Qiang Liu, Alex Ihler

Convolutional Networks on Graphs for Learning Molecular Fingerprints

David K. Duvenaud, Dougal Maclaurin, Jorge Iparraguirre, Rafael Bombarell, Timothy Hirzel

A Dual Augmented Block Minimization Framework for Learning with Limited Memory

Ian En-Hsu Yen, Shan-Wei Lin, Shou-De Lin

Spherical Random Features for Polynomial Kernels

Jeffrey Pennington, Felix Xinnan X Yu, Sanjiv Kumar

Monotone k-Submodular Function Maximization with Size Constraints

Naoto Ohsaka, Yuichi Yoshida

Accelerated Proximal Gradient Methods for Nonconvex Programming

Huan Li, Zhouchen Lin

Accelerated Mirror Descent in Continuous and Discrete Time

Walid Krichene, Alexandre Bayen, Peter L. Bartlett, Peter L Bartlett

Preconditioned Spectral Descent for Deep Learning

David E Carlson, Edo Collins, Ya-Ping Hsieh, Lawrence Carin, Volkan Cevher

Discrete Rényi Classifiers

Meisam Razaviyayn, Farzan Farnia, David Tse

Decoupled Deep Neural Network for Semi-supervised Semantic Segmentation

Seunghoon Hong, Hyeonwoo Noh, Bohyung Han

Gaussian Process Random Fields

David Moore, Stuart Russell

Collaborative Filtering with Graph Information: Consistency and Scalable Methods

Nikhil Rao, Hsiang-Fu Yu, Pradeep K Ravikumar, Inderjit S Dhillon

Skip-Thought Vectors

Ryan Kiros, Yukun Zhu, Ruslan Salakhutdinov, Richard Zemel, Raquel Urtasun

Fast Randomized Kernel Ridge Regression with Statistical Guarantees

Ahmed Alaoui, Michael W. Mahoney

Beyond Sub-Gaussian Measurements: High-Dimensional Structured Estimation with Sub-Exponential Designs

Vidyashankar Sivakumar, Arindam Banerjee, Pradeep K Ravikumar

Stochastic Expectation Propagation

Yingzhen Li, José Miguel Hernández-Lobato, Richard E Turner

Fixed-Length Poisson MRF: Adding Dependencies to the Multinomial

David I Inouye, Pradeep K Ravikumar, Inderjit S Dhillon

The Poisson Gamma Belief Network

Mingyuan Zhou, Yulai Cong, Bo Chen

HONOR: Hybrid Optimization for NOn-convex Regularized problems

Pinghua Gong, Jieping Ye

Principal Geodesic Analysis for Probability Measures under the Optimal Transport Metric

Vivien Seguy, Marco Cuturi

Matrix Completion Under Monotonic Single Index Models

Ravi Sastry Ganti, Laura Balzano, Rebecca Willett

Learning with Relaxed Supervision

Jacob Steinhardt, Percy Liang

On Elicitation Complexity

Rafael Frongillo, Ian Kash

Variance Reduced Stochastic Gradient Descent with Neighbors

Thomas Hofmann, Aurelien Lucchi, Simon Lacoste-Julien, Brian McWilliams

Learning to Linearize Under Uncertainty

Ross Goroshin, Michael F Mathieu, Yann LeCun

Fast Bidirectional Probability Estimation in Markov Models

Siddhartha Banerjee, Peter Lofgren

Softstar: Heuristic-Guided Probabilistic Inference

Mathew Monfort, Brenden M Lake, Brian Ziebart, Patrick Lucey, Josh Tenenbaum

Where are they looking?

Adria Recasens, Aditya Khosla, Carl Vondrick, Antonio Torralba

Sum-of-Squares Lower Bounds for Sparse PCA

Tengyu Ma, Avi Wigderson

Active Learning from Weak and Strong Labelers

Chicheng Zhang, Kamalika Chaudhuri

M-Statistic for Kernel Change-Point Detection

Shuang Li, Yao Xie, Hanjun Dai, Le Song

Orthogonal NMF through Subspace Exploration

Megasthenis Asteris, Dimitris Papailiopoulos, Alexandros G Dimakis

Path-SGD: Path-Normalized Optimization in Deep Neural Networks

Behnam Neyshabur, Ruslan Salakhutdinov, Nati Srebro

Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks

Samy Bengio, Oriol Vinyals, Navdeep Jaitly, Noam Shazeer

Variational Consensus Monte Carlo

Maxim Rabinovich, Elaine Angelino, Michael I. Jordan, Michael I Jordan

Subspace Clustering with Irrelevant Features via Robust Dantzig Selector

Chao Qu, Huan Xu

Explore no more: Improved high-probability regret bounds for non-stochastic bandits

Gergely Neu

Exactness of Approximate MAP Inference in Continuous MRFs

Nicholas Ruozzi

Attractor Network Dynamics Enable Preplay and Rapid Path Planning in Maze–like Environments

Dane S Corneil, Wulfram Gerstner

Copula variational inference

Dustin Tran, David Blei, Edoardo M. Airoldi

Logarithmic Time Online Multiclass prediction

Anna E Choromanska, John Langford

Structured Estimation with Atomic Norms: General Bounds and Applications

Sheng Chen, Arindam Banerjee

Rate-Agnostic (Causal) Structure Learning

Sergey Plis, David Danks, Cynthia Freeman, Vince Calhoun

Deep Visual Analogy-Making

Scott E Reed, Yi Zhang, Yuting Zhang, Honglak Lee

Variational Information Maximisation for Intrinsically Motivated Reinforcement Learning

Shakir Mohamed, Danilo Jimenez Rezende

Adversarial Prediction Games for Multivariate Losses

Hong Wang, Wei Xing, Kaiser Asif, Brian Ziebart

A Universal Primal-Dual Convex Optimization Framework

Alp Yurtsever, Quoc Tran Dinh, Volkan Cevher

Adaptive Stochastic Optimization: From Sets to Paths

Zhan Wei Lim, David Hsu, Wee Sun Lee

Rectified Factor Networks

Djork-Arné Clevert, Andreas Mayr, Thomas Unterthiner, Sepp Hochreiter

Gradient Estimation Using Stochastic Computation Graphs

John Schulman, Nicolas Heess, Theophane Weber, Pieter Abbeel

Mixed Robust/Average Submodular Partitioning: Fast Algorithms, Guarantees, and Applications

Kai Wei, Rishabh K Iyer, Shengjie Wang, Wenruo Bai, Jeff A. Bilmes

Bounding the Cost of Search-Based Lifted Inference

David B Smith, Vibhav G Gogate

Human Memory Search as Initial-Visit Emitting Random Walk

Kwang-Sung Jun, Xiaojin Zhu, Timothy T. Rogers, Zhuoran Yang, Ming Yuan

Minimum Weight Perfect Matching via Blossom Belief Propagation

Sejun Park, Michael Chertkov, Jinwoo Shin, Sungsoo Ahn, Sung-Soo Ahn

Matrix Manifold Optimization for Gaussian Mixtures

Reshad Hosseini, Suvrit Sra

Learning Wake-Sleep Recurrent Attention Models

Jimmy Ba, Ruslan Salakhutdinov, Roger B Grosse, Brendan J. Frey

Shepard Convolutional Neural Networks

Jimmy SJ Ren, Li Xu, Qiong Yan, Wenxiu Sun

Collaboratively Learning Preferences from Ordinal Data

Sewoong Oh, Kiran K Thekumparampil, Jiaming Xu

Bayesian Active Model Selection with an Application to Automated Audiometry

Jacob Gardner, Gustavo Malkomes, Roman Garnett, Kilian Q. Weinberger, Dennis Barbour

Deeply Learning the Messages in Message Passing Inference

Guosheng Lin, Chunhua Shen, Ian Reid, Anton van den Hengel

Spectral Learning of Large Structured HMMs for Comparative Epigenomics

Chicheng Zhang, Jimin Song, Kamalika Chaudhuri, Kevin Chen

Embedding Inference for Structured Multilabel Prediction

Farzaneh Mirzazadeh, Siamak Ravanbakhsh, Nan Ding, Dale Schuurmans

Competitive Distribution Estimation: Why is Good-Turing Good

Alon Orlitsky, Ananda Theertha Suresh

Max-Margin Majority Voting for Learning from Crowds

TIAN TIAN, Jun Zhu

Deep Poisson Factor Modeling

Ricardo Henao, Zhe Gan, James Lu, Lawrence Carin

Discriminative Robust Transformation Learning

Jiaji Huang, Qiang Qiu, Guillermo Sapiro, Robert Calderbank

Parallel Multi-Dimensional LSTM, With Application to Fast Biomedical Volumetric Image Segmentation

Marijn F Stollenga, Wonmin Byeon, Marcus Liwicki, Jürgen Schmidhuber

Column Selection via Adaptive Sampling

Saurabh Paul, Malik Magdon-Ismail, Petros Drineas

Smooth Interactive Submodular Set Cover

Bryan D He, Yisong Yue

Bayesian Manifold Learning: The Locally Linear Latent Variable Model (LL-LVM)

Mijung Park, Wittawat Jitkrittum, Ahmad Qamar, Zoltan Szabo, Lars Buesing

Online F-Measure Optimization

Róbert Busa-Fekete, Balázs Szörényi, Krzysztof Dembczynski, Eyke Hüllermeier

Deep learning with Elastic Averaging SGD

Sixin Zhang, Anna E Choromanska, Yann LeCun

Optimal Rates for Random Fourier Features

Bharath Sriperumbudur, Zoltan Szabo

Galileo: Perceiving Physical Object Properties by Integrating a Physics Engine with Deep Learning

Jiajun Wu, Ilker Yildirim, Joseph J. Lim, Bill Freeman, Josh Tenenbaum

On Variance Reduction in Stochastic Gradient Descent and its Asynchronous Variants

Sashank J. Reddi, Ahmed Hefny, Suvrit Sra, Barnabás Póczos, Alexander J Smola

Recovering Communities in the General Stochastic Block Model Without Knowing the Parameters

Emmanuel Abbe, Colin Sandon

Convolutional spike-triggered covariance analysis for neural subunit models

Anqi Wu, ll Memming Park, Jonathan W Pillow

Efficient Compressive Phase Retrieval with Constrained Sensing Vectors

Sohail Bahmani, Justin Romberg

Smooth and Strong: MAP Inference with Linear Convergence

Ofer Meshi, Mehrdad Mahdavi, Alex Schwing

Deep Convolutional Inverse Graphics Network

Tejas D Kulkarni, William F. Whitney, Pushmeet Kohli, Josh Tenenbaum

Scalable Semi-Supervised Aggregation of Classifiers

Akshay Balsubramani, Yoav Freund

Online Prediction at the Limit of Zero Temperature

Mark Herbster, Stephen Pasteris, Shaona Ghosh

Regret Lower Bound and Optimal Algorithm in Finite Stochastic Partial Monitoring

Junpei Komiyama, Junya Honda, Hiroshi Nakagawa

Adaptive Primal-Dual Splitting Methods for Statistical Learning and Image Processing

Tom Goldstein, Min Li, Xiaoming Yuan

Distributionally Robust Logistic Regression

Soroosh Shafieezadeh-Abadeh, Peyman Mohajerin Esfahani, Daniel Huhn

Convergence Rates of Active Learning for Maximum Likelihood Estimation

Kamalika Chaudhuri, Sham M. Kakade, Praneeth Netrapalli, Sujay Sanghavi

On the Limitation of Spectral Methods: From the Gaussian Hidden Clique Problem to Rank-One Perturbations of Gaussian Tensors

Andrea Montanari, Daniel Reichman, Ofer Zeitouni

Bounding errors of Expectation-Propagation

Guillaume P Dehaene, Simon Barthelmé

Asynchronous stochastic convex optimization: the noise is in the noise and SGD don't care

Sorathan Chaturapruek, John C. Duchi, Christopher Re

Double or Nothing: Multiplicative Incentive Mechanisms for Crowdsourcing

Nihar Bhadresh Shah, Dengyong Zhou

Bidirectional Recurrent Neural Networks as Generative Models

Mathias Berglund, Tapani Raiko, Mikko Honkala, Leo Kärkkäinen, Akos Vetek

Adaptive Low-Complexity Sequential Inference for Dirichlet Process Mixture Models

Theodoros Tsiligkaridis, Keith Forsythe