Book

Quartz: Randomized Dual Coordinate Ascent with Arbitrary Sampling Zheng Qu, Peter Richtarik, Tong Zhang

Associative Memory via a Sparse Recovery Model Arya Mazumdar, Ankit Singh Rawat

Policy Gradient for Coherent Risk Measures Aviv Tamar, Yinlam Chow, Mohammad Ghavamzadeh, Shie Mannor

A fast, universal algorithm to learn parametric nonlinear embeddings Miguel A. Carreira-Perpinan, Max Vladymyrov

Stochastic Online Greedy Learning with Semi-bandit Feedbacks Tian Lin, Jian Li, Wei Chen

SubmodBoxes: Near-Optimal Search for a Set of Diverse Object Proposals Qing Sun, Dhruv Batra

Robust Portfolio Optimization Huitong Qiu, Fang Han, Han Liu, Brian Caffo

Top-k Multiclass SVM Maksim Lapin, Matthias Hein, Bernt Schiele

Less is More: Nyström Computational Regularization Alessandro Rudi, Raffaello Camoriano, Lorenzo Rosasco

Parallel Recursive Best-First AND/OR Search for Exact MAP Inference in Graphical Models Akihiro Kishimoto, Radu Marinescu, Adi Botea

Differentially private subspace clustering Yining Wang, Yu-Xiang Wang, Aarti Singh

Matrix Completion with Noisy Side Information Kai-Yang Chiang, Cho-Jui Hsieh, Inderjit S. Dhillon

Nonparametric von Mises Estimators for Entropies, Divergences and Mutual Informations Kirthevasan Kandasamy, Akshay Krishnamurthy, Barnabas Poczos, Larry Wasserman, james m. robins

Semi-Supervised Factored Logistic Regression for High-Dimensional Neuroimaging Data Danilo Bzdok, Michael Eickenberg, Olivier Grisel, Bertrand Thirion, Gael Varoquaux

Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting Xingjian SHI, Zhourong Chen, Hao Wang, Dit-Yan Yeung, Wai-kin Wong, Wang-chun WOO

Infinite Factorial Dynamical Model Isabel Valera, Francisco Ruiz, Lennart Svensson, Fernando Perez-Cruz

Dependent Multinomial Models Made Easy: Stick-Breaking with the Polya-gamma Augmentation Scott Linderman, Matthew J. Johnson, Ryan P. Adams

Sparse Linear Programming via Primal and Dual Augmented Coordinate Descent Ian En-Hsu Yen, Kai Zhong, Cho-Jui Hsieh, Pradeep K. Ravikumar, Inderjit S. Dhillon

Data Generation as Sequential Decision Making Philip Bachman, Doina Precup

Online Gradient Boosting Alina Beygelzimer, Elad Hazan, Satyen Kale, Haipeng Luo

Optimal Ridge Detection using Coverage Risk Yen-Chi Chen, Christopher R. Genovese, Shirley Ho, Larry Wasserman

A Tractable Approximation to Optimal Point Process Filtering: Application to Neural Encoding Yuval Harel, Ron Meir, Manfred Opper

Barrier Frank-Wolfe for Marginal Inference Rahul G. Krishnan, Simon Lacoste-Julien, David Sontag

Combinatorial Bandits Revisited Richard Combes, Mohammad Sadegh Talebi Mazraeh Shahi, Alexandre Proutiere, marc lelarge

Efficient and Parsimonious Agnostic Active Learning Tzu-Kuo Huang, Alekh Agarwal, Daniel J. Hsu, John Langford, Robert E. Schapire

Policy Evaluation Using the Ω-Return Philip S. Thomas, Scott Niekum, Georgios Theocharous, George Konidaris

Bayesian Optimization with Exponential Convergence Kenji Kawaguchi, Leslie Pack Kaelbling, Tomás Lozano-Pérez

Statistical Model Criticism using Kernel Two Sample Tests James R. Lloyd, Zoubin Ghahramani

Attention-Based Models for Speech Recognition Jan K. Chorowski, Dzmitry Bahdanau, Dmitriy Serdyuk, Kyunghyun Cho, Yoshua Bengio

Weakly-supervised Disentangling with Recurrent Transformations for 3D View Synthesis Jimei Yang, Scott E. Reed, Ming-Hsuan Yang, Honglak Lee

Backpropagation for Energy-Efficient Neuromorphic Computing Steve K. Esser, Rathinakumar Appuswamy, Paul Merolla, John V. Arthur, Dharmendra S. Modha

Efficient and Robust Automated Machine Learning Matthias Feurer, Aaron Klein, Katharina Eggensperger, Jost Springenberg, Manuel Blum, Frank Hutter

Time-Sensitive Recommendation From Recurrent User Activities Nan Du, Yichen Wang, Niao He, Jimeng Sun, Le Song

Local Expectation Gradients for Black Box Variational Inference Michalis Titsias RC AUEB, Miguel Lázaro-Gredilla

Training Restricted Boltzmann Machine via the Thouless-Anderson-Palmer free energy Marylou Gabrie, Eric W. Tramel, Florent Krzakala

High Dimensional EM Algorithm: Statistical Optimization and Asymptotic Normality Zhaoran Wang, Quanquan Gu, Yang Ning, Han Liu

Learning Continuous Control Policies by Stochastic Value Gradients Nicolas Heess, Gregory Wayne, David Silver, Timothy Lillicrap, Tom Erez, Yuval Tassa

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks Shaoqing Ren, Kaiming He, Ross Girshick, Jian Sun

Efficient Non-greedy Optimization of Decision Trees Mohammad Norouzi, Maxwell Collins, Matthew A. Johnson, David J. Fleet, Pushmeet Kohli

Learning with Incremental Iterative Regularization Lorenzo Rosasco, Silvia Villa

Maximum Likelihood Learning With Arbitrary Treewidth via Fast-Mixing Parameter Sets Justin Domke

Sampling from Probabilistic Submodular Models Alkis Gotovos, Hamed Hassani, Andreas Krause

A class of network models recoverable by spectral clustering Yali Wan, Marina Meila

Closed-form Estimators for High-dimensional Generalized Linear Models Eunho Yang, Aurelie C. Lozano, Pradeep K. Ravikumar

Expressing an Image Stream with a Sequence of Natural Sentences Cesc C. Park, Gunhee Kim

Learning spatiotemporal trajectories from manifold-valued longitudinal data Jean-Baptiste SCHIRATTI, Stéphanie ALLASSONNIERE, Olivier Colliot, Stanley DURRLEMAN

Fast Classification Rates for High-dimensional Gaussian Generative Models Tianyang Li, Adarsh Prasad, Pradeep K. Ravikumar

Adaptive Online Learning Dylan J. Foster, Alexander Rakhlin, Karthik Sridharan

Robust Regression via Hard Thresholding Kush Bhatia, Prateek Jain, Purushottam Kar

b-bit Marginal Regression Martin Slawski, Ping Li

Spectral Norm Regularization of Orthonormal Representations for Graph Transduction Rakesh Shivanna, Bibaswan K. Chatterjee, Raman Sankaran, Chiranjib Bhattacharyya, Francis Bach

Randomized Block Krylov Methods for Stronger and Faster Approximate Singular Value Decomposition Cameron Musco, Christopher Musco

Optimal Testing for Properties of Distributions Jayadev Acharya, Constantinos Daskalakis, Gautam Kamath

Combinatorial Cascading Bandits Branislav Kveton, Zheng Wen, Azin Ashkan, Csaba Szepesvari

Probabilistic Curve Learning: Coulomb Repulsion and the Electrostatic Gaussian Process Ye Wang, David B. Dunson

Training Very Deep Networks Rupesh K. Srivastava, Klaus Greff, Jürgen Schmidhuber

Fast and Memory Optimal Low-Rank Matrix Approximation Se-Young Yun, marc lelarge, Alexandre Proutiere

Character-level Convolutional Networks for Text Classification Xiang Zhang, Junbo Zhao, Yann LeCun

Interactive Control of Diverse Complex Characters with Neural Networks Igor Mordatch, Kendall Lowrey, Galen Andrew, Zoran Popovic, Emanuel V. Todorov

Inferring Algorithmic Patterns with Stack-Augmented Recurrent Nets Armand Joulin, Tomas Mikolov

Grammar as a Foreign Language Oriol Vinyals, Łukasz Kaiser, Terry Koo, Slav Petrov, Ilya Sutskever, Geoffrey Hinton

Practical and Optimal LSH for Angular Distance Alexandr Andoni, Piotr Indyk, Thijs Laarhoven, Ilya Razenshteyn, Ludwig Schmidt

GP Kernels for Cross-Spectrum Analysis Kyle R. Ulrich, David E. Carlson, Kafui Dzirasa, Lawrence Carin

A Framework for Individualizing Predictions of Disease Trajectories by Exploiting Multi-Resolution Structure Peter Schulam, Suchi Saria

Local Smoothness in Variance Reduced Optimization Daniel Vainsencher, Han Liu, Tong Zhang

Unlocking neural population non-stationarities using hierarchical dynamics models Mijung Park, Gergo Bohner, Jakob H. Macke

Pointer Networks Oriol Vinyals, Meire Fortunato, Navdeep Jaitly

Fast and Accurate Inference of Plackett–Luce Models Lucas Maystre, Matthias Grossglauser

Learning Bayesian Networks with Thousands of Variables Mauro Scanagatta, Cassio P. de Campos, Giorgio Corani, Marco Zaffalon

Differentially Private Learning of Structured Discrete Distributions Ilias Diakonikolas, Moritz Hardt, Ludwig Schmidt

Generative Image Modeling Using Spatial LSTMs Lucas Theis, Matthias Bethge

Sparse PCA via Bipartite Matchings Megasthenis Asteris, Dimitris Papailiopoulos, Anastasios Kyrillidis, Alexandros G. Dimakis

Market Scoring Rules Act As Opinion Pools For Risk-Averse Agents Mithun Chakraborty, Sanmay Das

Lifted Inference Rules With Constraints Happy Mittal, Anuj Mahajan, Vibhav G. Gogate, Parag Singla

LASSO with Non-linear Measurements is Equivalent to One With Linear Measurements CHRISTOS THRAMPOULIDIS, Ehsan Abbasi, Babak Hassibi

Natural Neural Networks Guillaume Desjardins, Karen Simonyan, Razvan Pascanu, koray kavukcuoglu

Scalable Adaptation of State Complexity for Nonparametric Hidden Markov Models Michael C. Hughes, William T. Stephenson, Erik Sudderth

Inference for determinantal point processes without spectral knowledge Rémi Bardenet, Michalis Titsias RC AUEB

A Bayesian Framework for Modeling Confidence in Perceptual Decision Making Koosha Khalvati, Rajesh PN Rao

Sample Complexity of Episodic Fixed-Horizon Reinforcement Learning Christoph Dann, Emma Brunskill

Algorithms with Logarithmic or Sublinear Regret for Constrained Contextual Bandits Huasen Wu, R. Srikant, Xin Liu, Chong Jiang

Latent Bayesian melding for integrating individual and population models Mingjun Zhong, Nigel Goddard, Charles Sutton

Regressive Virtual Metric Learning Michaël Perrot, Amaury Habrard

Halting in Random Walk Kernels Mahito Sugiyama, Karsten Borgwardt

Kullback-Leibler Proximal Variational Inference Mohammad Emtiyaz E. Khan, Pierre Baque, François Fleuret, Pascal Fua

A Convergent Gradient Descent Algorithm for Rank Minimization and Semidefinite Programming from Random Linear Measurements Qinqing Zheng, John Lafferty

On-the-Job Learning with Bayesian Decision Theory Keenon Werling, Arun Tejasvi Chaganty, Percy S. Liang, Christopher D. Manning

Spatial Transformer Networks Max Jaderberg, Karen Simonyan, Andrew Zisserman, koray kavukcuoglu

Precision-Recall-Gain Curves: PR Analysis Done Right Peter Flach, Meelis Kull

Planar Ultrametrics for Image Segmentation Julian E. Yarkony, Charless Fowlkes

Sparse Local Embeddings for Extreme Multi-label Classification Kush Bhatia, Himanshu Jain, Purushottam Kar, Manik Varma, Prateek Jain

Super-Resolution Off the Grid Qingqing Huang, Sham M. Kakade

Automatic Variational Inference in Stan Alp Kucukelbir, Rajesh Ranganath, Andrew Gelman, David Blei

Extending Gossip Algorithms to Distributed Estimation of U-statistics Igor Colin, Aurélien Bellet, Joseph Salmon, Stéphan Clémençon

Model-Based Relative Entropy Stochastic Search Abbas Abdolmaleki, Rudolf Lioutikov, Jan R. Peters, Nuno Lau, Luis Pualo Reis, Gerhard Neumann

Semi-supervised Learning with Ladder Networks Antti Rasmus, Mathias Berglund, Mikko Honkala, Harri Valpola, Tapani Raiko

Empirical Localization of Homogeneous Divergences on Discrete Sample Spaces Takashi Takenouchi, Takafumi Kanamori

Enforcing balance allows local supervised learning in spiking recurrent networks Ralph Bourdoukan, Sophie Denève

Online Learning for Adversaries with Memory: Price of Past Mistakes Oren Anava, Elad Hazan, Shie Mannor

Streaming, Distributed Variational Inference for Bayesian Nonparametrics Trevor Campbell, Julian Straub, John W. Fisher III, Jonathan P. How

Tree-Guided MCMC Inference for Normalized Random Measure Mixture Models Juho Lee, Seungjin Choi

The Self-Normalized Estimator for Counterfactual Learning Adith Swaminathan, Thorsten Joachims

Information-theoretic lower bounds for convex optimization with erroneous oracles Yaron Singer, Jan Vondrak

A Nonconvex Optimization Framework for Low Rank Matrix Estimation Tuo Zhao, Zhaoran Wang, Han Liu

Recursive Training of 2D-3D Convolutional Networks for Neuronal Boundary Prediction Kisuk Lee, Aleksandar Zlateski, Vishwanathan Ashwin, H. Sebastian Seung

Multi-class SVMs: From Tighter Data-Dependent Generalization Bounds to Novel Algorithms Yunwen Lei, Urun Dogan, Alexander Binder, Marius Kloft

Scalable Inference for Gaussian Process Models with Black-Box Likelihoods Amir Dezfouli, Edwin V. Bonilla

M-Best-Diverse Labelings for Submodular Energies and Beyond Alexander Kirillov, Dmytro Shlezinger, Dmitry P. Vetrov, Carsten Rother, Bogdan Savchynskyy

BinaryConnect: Training Deep Neural Networks with binary weights during propagations Matthieu Courbariaux, Yoshua Bengio, Jean-Pierre David

No-Regret Learning in Bayesian Games Jason Hartline, Vasilis Syrgkanis, Eva Tardos

Robust Gaussian Graphical Modeling with the Trimmed Graphical Lasso Eunho Yang, Aurelie C. Lozano

Parallelizing MCMC with Random Partition Trees Xiangyu Wang, Fangjian Guo, Katherine A. Heller, David B. Dunson

Convergence rates of sub-sampled Newton methods Murat A. Erdogdu, Andrea Montanari

Learning Theory and Algorithms for Forecasting Non-stationary Time Series Vitaly Kuznetsov, Mehryar Mohri

Equilibrated adaptive learning rates for non-convex optimization Yann Dauphin, Harm de Vries, Yoshua Bengio

Optimal Linear Estimation under Unknown Nonlinear Transform Xinyang Yi, Zhaoran Wang, Constantine Caramanis, Han Liu

Analysis of Robust PCA via Local Incoherence Huishuai Zhang, Yi Zhou, Yingbin Liang

Probabilistic Variational Bounds for Graphical Models Qiang Liu, John W. Fisher III, Alexander T. Ihler

The Human Kernel Andrew G. Wilson, Christoph Dann, Chris Lucas, Eric P. Xing

Asynchronous Parallel Stochastic Gradient for Nonconvex Optimization Xiangru Lian, Yijun Huang, Yuncheng Li, Ji Liu

Evaluating the statistical significance of biclusters Jason D. Lee, Yuekai Sun, Jonathan E. Taylor

Fast and Guaranteed Tensor Decomposition via Sketching Yining Wang, Hsiao-Yu Tung, Alexander J. Smola, Anima Anandkumar

Inverse Reinforcement Learning with Locally Consistent Reward Functions Quoc Phong Nguyen, Bryan Kian Hsiang Low, Patrick Jaillet

A hybrid sampler for Poisson-Kingman mixture models Maria Lomeli, Stefano Favaro, Yee Whye Teh

Learning with Symmetric Label Noise: The Importance of Being Unhinged Brendan van Rooyen, Aditya Menon, Robert C. Williamson

Visalogy: Answering Visual Analogy Questions Fereshteh Sadeghi, C. Lawrence Zitnick, Ali Farhadi

Cornering Stationary and Restless Mixing Bandits with Remix-UCB Julien Audiffren, Liva Ralaivola

The Consistency of Common Neighbors for Link Prediction in Stochastic Blockmodels Purnamrita Sarkar, Deepayan Chakrabarti, peter j. bickel

On the Accuracy of Self-Normalized Log-Linear Models Jacob Andreas, Maxim Rabinovich, Michael I. Jordan, Dan Klein

Learnability of Influence in Networks Harikrishna Narasimhan, David C. Parkes, Yaron Singer

Linear Response Methods for Accurate Covariance Estimates from Mean Field Variational Bayes Ryan J. Giordano, Tamara Broderick, Michael I. Jordan

Weighted Theta Functions and Embeddings with Applications to Max-Cut, Clustering and Summarization Fredrik D. Johansson, Ankani Chattoraj, Chiranjib Bhattacharyya, Devdatt Dubhashi

End-to-end Learning of LDA by Mirror-Descent Back Propagation over a Deep Architecture Jianshu Chen, Ji He, Yelong Shen, Lin Xiao, Xiaodong He, Jianfeng Gao, Xinying Song, Li Deng

Robust Spectral Inference for Joint Stochastic Matrix Factorization Moontae Lee, David Bindel, David Mimno

Minimax Time Series Prediction Wouter M. Koolen, Alan Malek, Peter L. Bartlett, Yasin Abbasi Yadkori

Learning to Segment Object Candidates Pedro O. O. Pinheiro, Ronan Collobert, Piotr Dollar

A Theory of Decision Making Under Dynamic Context Michael Shvartsman, Vaibhav Srivastava, Jonathan D. Cohen

Particle Gibbs for Infinite Hidden Markov Models Nilesh Tripuraneni, Shixiang (Shane) Gu, Hong Ge, Zoubin Ghahramani

Bandit Smooth Convex Optimization: Improving the Bias-Variance Tradeoff Ofer Dekel, Ronen Eldan, Tomer Koren

Compressive spectral embedding: sidestepping the SVD Dinesh Ramasamy, Upamanyu Madhow

Winner-Take-All Autoencoders Alireza Makhzani, Brendan J. Frey

Robust Feature-Sample Linear Discriminant Analysis for Brain Disorders Diagnosis Ehsan Adeli-Mosabbeb, Kim-Han Thung, Le An, Feng Shi, Dinggang Shen

COEVOLVE: A Joint Point Process Model for Information Diffusion and Network Co-evolution Mehrdad Farajtabar, Yichen Wang, Manuel Gomez Rodriguez, Shuang Li, Hongyuan Zha, Le Song

Nearly Optimal Private LASSO Kunal Talwar, Abhradeep Guha Thakurta, Li Zhang

Calibrated Structured Prediction Volodymyr Kuleshov, Percy S. Liang

Spectral Representations for Convolutional Neural Networks Oren Rippel, Jasper Snoek, Ryan P. Adams

On the consistency theory of high dimensional variable screening Xiangyu Wang, Chenlei Leng, David B. Dunson

Revenue Optimization against Strategic Buyers Mehryar Mohri, Andres Munoz

The Population Posterior and Bayesian Modeling on Streams James McInerney, Rajesh Ranganath, David Blei

Parallel Predictive Entropy Search for Batch Global Optimization of Expensive Objective Functions Amar Shah, Zoubin Ghahramani

The Return of the Gating Network: Combining Generative Models and Discriminative Training in Natural Image Priors Dan Rosenbaum, Yair Weiss

Fighting Bandits with a New Kind of Smoothness Jacob D. Abernethy, Chansoo Lee, Ambuj Tewari

Sparse and Low-Rank Tensor Decomposition Parikshit Shah, Nikhil Rao, Gongguo Tang

Testing Closeness With Unequal Sized Samples Bhaswar Bhattacharya, Gregory Valiant

Risk-Sensitive and Robust Decision-Making: a CVaR Optimization Approach Yinlam Chow, Aviv Tamar, Shie Mannor, Marco Pavone

Fast Lifted MAP Inference via Partitioning Somdeb Sarkhel, Parag Singla, Vibhav G. Gogate

Algorithmic Stability and Uniform Generalization Ibrahim M. Alabdulmohsin

Learning with Group Invariant Features: A Kernel Perspective. Youssef Mroueh, Stephen Voinea, Tomaso A. Poggio

Tractable Bayesian Network Structure Learning with Bounded Vertex Cover Number Janne H. Korhonen, Pekka Parviainen

Convergence Analysis of Prediction Markets via Randomized Subspace Descent Rafael Frongillo, Mark D. Reid

SGD Algorithms based on Incomplete U-statistics: Large-Scale Minimization of Empirical Risk Guillaume Papa, Stéphan Clémençon, Aurélien Bellet

Multi-Layer Feature Reduction for Tree Structured Group Lasso via Hierarchical Projection Jie Wang, Jieping Ye

From random walks to distances on unweighted graphs Tatsunori Hashimoto, Yi Sun, Tommi Jaakkola

Tensorizing Neural Networks Alexander Novikov, Dmitrii Podoprikhin, Anton Osokin, Dmitry P. Vetrov

On some provably correct cases of variational inference for topic models Pranjal Awasthi, Andrej Risteski

GAP Safe screening rules for sparse multi-task and multi-class models Eugene Ndiaye, Olivier Fercoq, Alexandre Gramfort, Joseph Salmon

The Pareto Regret Frontier for Bandits Tor Lattimore

Measuring Sample Quality with Stein's Method Jackson Gorham, Lester Mackey

Predtron: A Family of Online Algorithms for General Prediction Problems Prateek Jain, Nagarajan Natarajan, Ambuj Tewari

MCMC for Variationally Sparse Gaussian Processes James Hensman, Alexander G. Matthews, Maurizio Filippone, Zoubin Ghahramani

Action-Conditional Video Prediction using Deep Networks in Atari Games Junhyuk Oh, Xiaoxiao Guo, Honglak Lee, Richard L. Lewis, Satinder Singh

Unified View of Matrix Completion under General Structural Constraints Suriya Gunasekar, Arindam Banerjee, Joydeep Ghosh

When are Kalman-Filter Restless Bandits Indexable? Christopher R. Dance, Tomi Silander

3D Object Proposals for Accurate Object Class Detection Xiaozhi Chen, Kaustav Kundu, Yukun Zhu, Andrew G. Berneshawi, Huimin Ma, Sanja Fidler, Raquel Urtasun

Interpolating Convex and Non-Convex Tensor Decompositions via the Subspace Norm Qinqing Zheng, Ryota Tomioka

Biologically Inspired Dynamic Textures for Probing Motion Perception Jonathan Vacher, Andrew Isaac Meso, Laurent U. Perrinet, Gabriel Peyré

Covariance-Controlled Adaptive Langevin Thermostat for Large-Scale Bayesian Sampling Xiaocheng Shang, Zhanxing Zhu, Benedict Leimkuhler, Amos J. Storkey

Semi-supervised Sequence Learning Andrew M. Dai, Quoc V. Le

Non-convex Statistical Optimization for Sparse Tensor Graphical Model Wei Sun, Zhaoran Wang, Han Liu, Guang Cheng

Lifted Symmetry Detection and Breaking for MAP Inference Timothy Kopp, Parag Singla, Henry Kautz

Private Graphon Estimation for Sparse Graphs Christian Borgs, Jennifer Chayes, Adam Smith

Online Learning with Adversarial Delays Kent Quanrud, Daniel Khashabi

Solving Random Quadratic Systems of Equations Is Nearly as Easy as Solving Linear Systems Yuxin Chen, Emmanuel Candes

Statistical Topological Data Analysis - A Kernel Perspective Roland Kwitt, Stefan Huber, Marc Niethammer, Weili Lin, Ulrich Bauer

A Structural Smoothing Framework For Robust Graph Comparison Pinar Yanardag, S.V.N. Vishwanathan

Bandits with Unobserved Confounders: A Causal Approach Elias Bareinboim, Andrew Forney, Judea Pearl

Scale Up Nonlinear Component Analysis with Doubly Stochastic Gradients Bo Xie, Yingyu Liang, Le Song

A Market Framework for Eliciting Private Data Bo Waggoner, Rafael Frongillo, Jacob D. Abernethy

A Generalization of Submodular Cover via the Diminishing Return Property on the Integer Lattice Tasuku Soma, Yuichi Yoshida

Space-Time Local Embeddings Ke Sun, Jun Wang, Alexandros Kalousis, Stephane Marchand-Maillet

Mixing Time Estimation in Reversible Markov Chains from a Single Sample Path Daniel J. Hsu, Aryeh Kontorovich, Csaba Szepesvari

Online Rank Elicitation for Plackett-Luce: A Dueling Bandits Approach Balázs Szörényi, Róbert Busa-Fekete, Adil Paul, Eyke Hüllermeier

Efficient Exact Gradient Update for training Deep Networks with Very Large Sparse Targets Pascal Vincent, Alexandre de Brébisson, Xavier Bouthillier

A Gaussian Process Model of Quasar Spectral Energy Distributions Andrew Miller, Albert Wu, Jeff Regier, Jon McAuliffe, Dustin Lang, Mr. Prabhat, David Schlegel, Ryan P. Adams

Fast Convergence of Regularized Learning in Games Vasilis Syrgkanis, Alekh Agarwal, Haipeng Luo, Robert E. Schapire

Communication Complexity of Distributed Convex Learning and Optimization Yossi Arjevani, Ohad Shamir

Large-Scale Bayesian Multi-Label Learning via Topic-Based Label Embeddings Piyush Rai, Changwei Hu, Ricardo Henao, Lawrence Carin

Probabilistic Line Searches for Stochastic Optimization Maren Mahsereci, Philipp Hennig

Sample Complexity of Learning Mahalanobis Distance Metrics Nakul Verma, Kristin Branson

Sample Efficient Path Integral Control under Uncertainty Yunpeng Pan, Evangelos Theodorou, Michail Kontitsis

Mind the Gap: A Generative Approach to Interpretable Feature Selection and Extraction Been Kim, Julie A. Shah, Finale Doshi-Velez

Regularization Path of Cross-Validation Error Lower Bounds Atsushi Shibagaki, Yoshiki Suzuki, Masayuki Karasuyama, Ichiro Takeuchi

Reflection, Refraction, and Hamiltonian Monte Carlo Hadi Mohasel Afshar, Justin Domke

Exploring Models and Data for Image Question Answering Mengye Ren, Ryan Kiros, Richard Zemel

Learning structured densities via infinite dimensional exponential families Siqi Sun, Mladen Kolar, Jinbo Xu

Streaming Min-max Hypergraph Partitioning Dan Alistarh, Jennifer Iglesias, Milan Vojnovic

Principal Differences Analysis: Interpretable Characterization of Differences between Distributions Jonas W. Mueller, Tommi Jaakkola

An Active Learning Framework using Sparse-Graph Codes for Sparse Polynomials and Graph Sketching Xiao Li, Kannan Ramchandran

Efficient Thompson Sampling for Online Matrix-Factorization Recommendation Jaya Kawale, Hung H. Bui, Branislav Kveton, Long Tran-Thanh, Sanjay Chawla

Structured Transforms for Small-Footprint Deep Learning Vikas Sindhwani, Tara Sainath, Sanjiv Kumar

Linear Multi-Resource Allocation with Semi-Bandit Feedback Tor Lattimore, Koby Crammer, Csaba Szepesvari

On the Optimality of Classifier Chain for Multi-label Classification Weiwei Liu, Ivor Tsang

Consistent Multilabel Classification Oluwasanmi O. Koyejo, Nagarajan Natarajan, Pradeep K. Ravikumar, Inderjit S. Dhillon

A Normative Theory of Adaptive Dimensionality Reduction in Neural Networks Cengiz Pehlevan, Dmitri Chklovskii

Hidden Technical Debt in Machine Learning Systems D. Sculley, Gary Holt, Daniel Golovin, Eugene Davydov, Todd Phillips, Dietmar Ebner, Vinay Chaudhary, Michael Young, Jean-François Crespo, Dan Dennison

NEXT: A System for Real-World Development, Evaluation, and Application of Active Learning Kevin G. Jamieson, Lalit Jain, Chris Fernandez, Nicholas J. Glattard, Rob Nowak

A Pseudo-Euclidean Iteration for Optimal Recovery in Noisy ICA James R. Voss, Mikhail Belkin, Luis Rademacher

Learning Structured Output Representation using Deep Conditional Generative Models Kihyuk Sohn, Honglak Lee, Xinchen Yan

Estimating Mixture Models via Mixtures of Polynomials Sida Wang, Arun Tejasvi Chaganty, Percy S. Liang

Online Learning with Gaussian Payoffs and Side Observations Yifan Wu, András György, Csaba Szepesvari

Gradient-free Hamiltonian Monte Carlo with Efficient Kernel Exponential Families Heiko Strathmann, Dino Sejdinovic, Samuel Livingstone, Zoltan Szabo, Arthur Gretton

Approximating Sparse PCA from Incomplete Data ABHISEK KUNDU, Petros Drineas, Malik Magdon-Ismail

Regularization-Free Estimation in Trace Regression with Symmetric Positive Semidefinite Matrices Martin Slawski, Ping Li, Matthias Hein

Learning visual biases from human imagination Carl Vondrick, Hamed Pirsiavash, Aude Oliva, Antonio Torralba

End-To-End Memory Networks Sainbayar Sukhbaatar, arthur szlam, Jason Weston, Rob Fergus

Fast Distributed k-Center Clustering with Outliers on Massive Data Gustavo Malkomes, Matt J. Kusner, Wenlin Chen, Kilian Q. Weinberger, Benjamin Moseley

BACKSHIFT: Learning causal cyclic graphs from unknown shift interventions Dominik Rothenhäusler, Christina Heinze, Jonas Peters, Nicolai Meinshausen

Lifelong Learning with Non-i.i.d. Tasks Anastasia Pentina, Christoph H. Lampert

Regularized EM Algorithms: A Unified Framework and Statistical Guarantees Xinyang Yi, Constantine Caramanis

Beyond Convexity: Stochastic Quasi-Convex Optimization Elad Hazan, Kfir Levy, Shai Shalev-Shwartz

Learning From Small Samples: An Analysis of Simple Decision Heuristics Özgür Şimşek, Marcus Buckmann

Deep Temporal Sigmoid Belief Networks for Sequence Modeling Zhe Gan, Chunyuan Li, Ricardo Henao, David E. Carlson, Lawrence Carin

Subsampled Power Iteration: a Unified Algorithm for Block Models and Planted CSP's Vitaly Feldman, Will Perkins, Santosh Vempala

Learning Stationary Time Series using Gaussian Processes with Nonparametric Kernels Felipe Tobar, Thang D. Bui, Richard E. Turner

Improved Iteration Complexity Bounds of Cyclic Block Coordinate Descent for Convex Problems Ruoyu Sun, Mingyi Hong

Community Detection via Measure Space Embedding Mark Kozdoba, Shie Mannor

Color Constancy by Learning to Predict Chromaticity from Luminance Ayan Chakrabarti

Sample Complexity Bounds for Iterative Stochastic Policy Optimization Marin Kobilarov

Copeland Dueling Bandits Masrour Zoghi, Zohar S. Karnin, Shimon Whiteson, Maarten de Rijke

Taming the Wild: A Unified Analysis of Hogwild-Style Algorithms Christopher M. De Sa, Ce Zhang, Kunle Olukotun, Christopher Ré, Christopher Ré

High-dimensional neural spike train analysis with generalized count linear dynamical systems Yuanjun Gao, Lars Busing, Krishna V. Shenoy, John P. Cunningham

Neural Adaptive Sequential Monte Carlo Shixiang (Shane) Gu, Zoubin Ghahramani, Richard E. Turner

Supervised Learning for Dynamical System Learning Ahmed Hefny, Carlton Downey, Geoffrey J. Gordon

A Complete Recipe for Stochastic Gradient MCMC Yi-An Ma, Tianqi Chen, Emily Fox

Segregated Graphs and Marginals of Chain Graph Models Ilya Shpitser

Rethinking LDA: Moment Matching for Discrete ICA Anastasia Podosinnikova, Francis Bach, Simon Lacoste-Julien

Max-Margin Deep Generative Models Chongxuan Li, Jun Zhu, Tianlin Shi, Bo Zhang

Convolutional Neural Networks with Intra-Layer Recurrent Connections for Scene Labeling Ming Liang, Xiaolin Hu, Bo Zhang

Individual Planning in Infinite-Horizon Multiagent Settings: Inference, Structure and Scalability Xia Qu, Prashant Doshi

Expectation Particle Belief Propagation Thibaut Lienart, Yee Whye Teh, Arnaud Doucet

Secure Multi-party Differential Privacy Peter Kairouz, Sewoong Oh, Pramod Viswanath

Embed to Control: A Locally Linear Latent Dynamics Model for Control from Raw Images Manuel Watter, Jost Springenberg, Joschka Boedecker, Martin Riedmiller

Newton-Stein Method: A Second Order Method for GLMs via Stein's Lemma Murat A. Erdogdu

Is Approval Voting Optimal Given Approval Votes? Ariel D. Procaccia, Nisarg Shah

Optimization Monte Carlo: Efficient and Embarrassingly Parallel Likelihood-Free Inference Ted Meeds, Max Welling

Basis refinement strategies for linear value function approximation in MDPs Gheorghe Comanici, Doina Precup, Prakash Panangaden

StopWasting My Gradients: Practical SVRG Reza Babanezhad Harikandeh, Mohamed Osama Ahmed, Alim Virani, Mark Schmidt, Jakub Konečný, Scott Sallinen

Saliency, Scale and Information: Towards a Unifying Theory Shafin Rahman, Neil Bruce

Efficient Learning of Continuous-Time Hidden Markov Models for Disease Progression Yu-Ying Liu, Shuang Li, Fuxin Li, Le Song, James M. Rehg

Efficient Output Kernel Learning for Multiple Tasks Pratik Kumar Jawanpuria, Maksim Lapin, Matthias Hein, Bernt Schiele

Texture Synthesis Using Convolutional Neural Networks Leon Gatys, Alexander S. Ecker, Matthias Bethge

Hessian-free Optimization for Learning Deep Multidimensional Recurrent Neural Networks Minhyung Cho, Chandra Dhir, Jaehyung Lee

Matrix Completion from Fewer Entries: Spectral Detectability and Rank Estimation Alaa Saade, Florent Krzakala, Lenka Zdeborová

Large-scale probabilistic predictors with and without guarantees of validity Vladimir Vovk, Ivan Petej, Valentina Fedorova

Learning with a Wasserstein Loss Charlie Frogner, Chiyuan Zhang, Hossein Mobahi, Mauricio Araya, Tomaso A. Poggio

Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks Emily L. Denton, Soumith Chintala, arthur szlam, Rob Fergus

Estimating Jaccard Index with Missing Observations: A Matrix Calibration Approach Wenye Li

On Top-k Selection in Multi-Armed Bandits and Hidden Bipartite Graphs Wei Cao, Jian Li, Yufei Tao, Zhize Li

Black-box optimization of noisy functions with unknown smoothness Jean-Bastien Grill, Michal Valko, Remi Munos, Remi Munos

Semi-supervised Convolutional Neural Networks for Text Categorization via Region Embedding Rie Johnson, Tong Zhang

Fast Rates for Exp-concave Empirical Risk Minimization Tomer Koren, Kfir Levy

Learning both Weights and Connections for Efficient Neural Network Song Han, Jeff Pool, John Tran, William Dally

Bayesian dark knowledge Anoop Korattikara Balan, Vivek Rathod, Kevin P. Murphy, Max Welling

On the Convergence of Stochastic Gradient MCMC Algorithms with High-Order Integrators Changyou Chen, Nan Ding, Lawrence Carin

Teaching Machines to Read and Comprehend Karl Moritz Hermann, Tomas Kocisky, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, Phil Blunsom

Synaptic Sampling: A Bayesian Approach to Neural Network Plasticity and Rewiring David Kappel, Stefan Habenschuss, Robert Legenstein, Wolfgang Maass

Alternating Minimization for Regression Problems with Vector-valued Outputs Prateek Jain, Ambuj Tewari

Anytime Influence Bounds and the Explosive Behavior of Continuous-Time Diffusion Networks Kevin Scaman, Rémi Lemonnier, Nicolas Vayatis

Rapidly Mixing Gibbs Sampling for a Class of Factor Graphs Using Hierarchy Width Christopher M. De Sa, Ce Zhang, Kunle Olukotun, Christopher Ré

A Reduced-Dimension fMRI Shared Response Model Po-Hsuan (Cameron) Chen, Janice Chen, Yaara Yeshurun, Uri Hasson, James Haxby, Peter J. Ramadge

Semi-Proximal Mirror-Prox for Nonsmooth Composite Minimization Niao He, Zaid Harchaoui

Subset Selection by Pareto Optimization Chao Qian, Yang Yu, Zhi-Hua Zhou

Parallel Correlation Clustering on Big Graphs Xinghao Pan, Dimitris Papailiopoulos, Samet Oymak, Benjamin Recht, Kannan Ramchandran, Michael I. Jordan

Fast Two-Sample Testing with Analytic Representations of Probability Measures Kacper P. Chwialkowski, Aaditya Ramdas, Dino Sejdinovic, Arthur Gretton

A Recurrent Latent Variable Model for Sequential Data Junyoung Chung, Kyle Kastner, Laurent Dinh, Kratarth Goel, Aaron C. Courville, Yoshua Bengio

Unsupervised Learning by Program Synthesis Kevin Ellis, Armando Solar-Lezama, Josh Tenenbaum

Learning Causal Graphs with Small Interventions Karthikeyan Shanmugam, Murat Kocaoglu, Alexandros G. Dimakis, Sriram Vishwanath

Learning to Transduce with Unbounded Memory Edward Grefenstette, Karl Moritz Hermann, Mustafa Suleyman, Phil Blunsom

Submodular Hamming Metrics Jennifer A. Gillenwater, Rishabh K. Iyer, Bethany Lusch, Rahul Kidambi, Jeff A. Bilmes

Frank-Wolfe Bayesian Quadrature: Probabilistic Integration with Theoretical Guarantees François-Xavier Briol, Chris Oates, Mark Girolami, Michael A. Osborne

Deep Knowledge Tracing Chris Piech, Jonathan Bassen, Jonathan Huang, Surya Ganguli, Mehran Sahami, Leonidas J. Guibas, Jascha Sohl-Dickstein

Generalization in Adaptive Data Analysis and Holdout Reuse Cynthia Dwork, Vitaly Feldman, Moritz Hardt, Toni Pitassi, Omer Reingold, Aaron Roth

Tractable Learning for Complex Probability Queries Jessa Bekker, Jesse Davis, Arthur Choi, Adnan Darwiche, Guy Van den Broeck

Variational Dropout and the Local Reparameterization Trick Durk P. Kingma, Tim Salimans, Max Welling

Fast, Provable Algorithms for Isotonic Regression in all L_p-norms Rasmus Kyng, Anup Rao, Sushant Sachdeva

On the Global Linear Convergence of Frank-Wolfe Optimization Variants Simon Lacoste-Julien, Martin Jaggi

Efficient Learning by Directed Acyclic Graph For Resource Constrained Prediction Joseph Wang, Kirill Trapeznikov, Venkatesh Saligrama

Finite-Time Analysis of Projected Langevin Monte Carlo Sebastien Bubeck, Ronen Eldan, Joseph Lehec

A Universal Catalyst for First-Order Optimization Hongzhou Lin, Julien Mairal, Zaid Harchaoui

Distributed Submodular Cover: Succinctly Summarizing Massive Data Baharan Mirzasoleiman, Amin Karbasi, Ashwinkumar Badanidiyuru, Andreas Krause

Robust PCA with compressed data Wooseok Ha, Rina Foygel Barber

Bidirectional Recurrent Convolutional Networks for Multi-Frame Super-Resolution Yan Huang, Wei Wang, Liang Wang

Regret-Based Pruning in Extensive-Form Games Noam Brown, Tuomas Sandholm

Adaptive Low-Complexity Sequential Inference for Dirichlet Process Mixture Models Theodoros Tsiligkaridis, Theodoros Tsiligkaridis, Keith Forsythe

Bidirectional Recurrent Neural Networks as Generative Models Mathias Berglund, Tapani Raiko, Mikko Honkala, Leo Kärkkäinen, Akos Vetek, Juha T. Karhunen

Double or Nothing: Multiplicative Incentive Mechanisms for Crowdsourcing Nihar Bhadresh Shah, Dengyong Zhou

Asynchronous stochastic convex optimization: the noise is in the noise and SGD don't care Sorathan Chaturapruek, John C. Duchi, Christopher Ré

Bounding errors of Expectation-Propagation Guillaume P. Dehaene, Simon Barthelmé

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

Convergence Rates of Active Learning for Maximum Likelihood Estimation Kamalika Chaudhuri, Sham M. Kakade, Praneeth Netrapalli, Sujay Sanghavi

Distributionally Robust Logistic Regression Soroosh Shafieezadeh Abadeh, Peyman Mohajerin Mohajerin Esfahani, Daniel Kuhn

Adaptive Primal-Dual Splitting Methods for Statistical Learning and Image Processing Tom Goldstein, Min Li, Xiaoming Yuan

Regret Lower Bound and Optimal Algorithm in Finite Stochastic Partial Monitoring Junpei Komiyama, Junya Honda, Hiroshi Nakagawa

Online Prediction at the Limit of Zero Temperature Mark Herbster, Stephen Pasteris, Shaona Ghosh

Scalable Semi-Supervised Aggregation of Classifiers Akshay Balsubramani, Yoav Freund

Deep Convolutional Inverse Graphics Network Tejas D. Kulkarni, William F. Whitney, Pushmeet Kohli, Josh Tenenbaum

Smooth and Strong: MAP Inference with Linear Convergence Ofer Meshi, Mehrdad Mahdavi, Alex Schwing

Efficient Compressive Phase Retrieval with Constrained Sensing Vectors Sohail Bahmani, Justin Romberg

Convolutional spike-triggered covariance analysis for neural subunit models Anqi Wu, Il Memming Park, Jonathan W. Pillow

Recovering Communities in the General Stochastic Block Model Without Knowing the Parameters Emmanuel Abbe, Colin Sandon

On Variance Reduction in Stochastic Gradient Descent and its Asynchronous Variants Sashank J. Reddi, Ahmed Hefny, Suvrit Sra, Barnabas Poczos, Alexander J. Smola

Galileo: Perceiving Physical Object Properties by Integrating a Physics Engine with Deep Learning Jiajun Wu, Ilker Yildirim, Joseph J. Lim, Bill Freeman, Josh Tenenbaum

Optimal Rates for Random Fourier Features Bharath Sriperumbudur, Zoltan Szabo

Deep learning with Elastic Averaging SGD Sixin Zhang, Anna E. Choromanska, Yann LeCun

Online F-Measure Optimization Róbert Busa-Fekete, Balázs Szörényi, Krzysztof Dembczynski, Eyke Hüllermeier

Bayesian Manifold Learning: The Locally Linear Latent Variable Model (LL-LVM) Mijung Park, Wittawat Jitkrittum, Ahmad Qamar, Zoltan Szabo, Lars Buesing, Maneesh Sahani

Smooth Interactive Submodular Set Cover Bryan D. He, Yisong Yue

Column Selection via Adaptive Sampling Saurabh Paul, Malik Magdon-Ismail, Petros Drineas

Parallel Multi-Dimensional LSTM, With Application to Fast Biomedical Volumetric Image Segmentation Marijn F. Stollenga, Wonmin Byeon, Marcus Liwicki, Jürgen Schmidhuber

Discriminative Robust Transformation Learning Jiaji Huang, Qiang Qiu, Guillermo Sapiro, Robert Calderbank

Deep Poisson Factor Modeling Ricardo Henao, Zhe Gan, James Lu, Lawrence Carin

Max-Margin Majority Voting for Learning from Crowds TIAN TIAN, Jun Zhu

Competitive Distribution Estimation: Why is Good-Turing Good Alon Orlitsky, Ananda Theertha Suresh

Embedding Inference for Structured Multilabel Prediction Farzaneh Mirzazadeh, Siamak Ravanbakhsh, Nan Ding, Dale Schuurmans

Spectral Learning of Large Structured HMMs for Comparative Epigenomics Chicheng Zhang, Jimin Song, Kamalika Chaudhuri, Kevin Chen

Deeply Learning the Messages in Message Passing Inference Guosheng Lin, Chunhua Shen, Ian Reid, Anton van den Hengel

Bayesian Active Model Selection with an Application to Automated Audiometry Jacob Gardner, Gustavo Malkomes, Roman Garnett, Kilian Q. Weinberger, Dennis Barbour, John P. Cunningham

Collaboratively Learning Preferences from Ordinal Data Sewoong Oh, Kiran K. Thekumparampil, Jiaming Xu

Shepard Convolutional Neural Networks Jimmy SJ Ren, Li Xu, Qiong Yan, Wenxiu Sun

Learning Wake-Sleep Recurrent Attention Models Jimmy Ba, Russ R. Salakhutdinov, Roger B. Grosse, Brendan J. Frey

Matrix Manifold Optimization for Gaussian Mixtures Reshad Hosseini, Suvrit Sra

Minimum Weight Perfect Matching via Blossom Belief Propagation Sung-Soo Ahn, Sejun Park, Michael Chertkov, Jinwoo Shin

Human Memory Search as Initial-Visit Emitting Random Walk Kwang-Sung Jun, Jerry Zhu, Timothy T. Rogers, Zhuoran Yang, ming yuan

Bounding the Cost of Search-Based Lifted Inference David B. Smith, Vibhav G. Gogate

Mixed Robust/Average Submodular Partitioning: Fast Algorithms, Guarantees, and Applications Kai Wei, Rishabh K. Iyer, Shengjie Wang, Wenruo Bai, Jeff A. Bilmes

Gradient Estimation Using Stochastic Computation Graphs John Schulman, Nicolas Heess, Theophane Weber, Pieter Abbeel

Rectified Factor Networks Djork-Arné Clevert, Andreas Mayr, Thomas Unterthiner, Sepp Hochreiter

Adaptive Stochastic Optimization: From Sets to Paths Zhan Wei Lim, David Hsu, Wee Sun Lee

A Universal Primal-Dual Convex Optimization Framework Alp Yurtsever, Quoc Tran Dinh, Volkan Cevher

Adversarial Prediction Games for Multivariate Losses Hong Wang, Wei Xing, Kaiser Asif, Brian Ziebart

Variational Information Maximisation for Intrinsically Motivated Reinforcement Learning Shakir Mohamed, Danilo Jimenez Rezende

Deep Visual Analogy-Making Scott E. Reed, Yi Zhang, Yuting Zhang, Honglak Lee

Rate-Agnostic (Causal) Structure Learning Sergey Plis, David Danks, Cynthia Freeman, Vince Calhoun

Structured Estimation with Atomic Norms: General Bounds and Applications Sheng Chen, Arindam Banerjee

Logarithmic Time Online Multiclass prediction Anna E. Choromanska, John Langford

Copula variational inference Dustin Tran, David Blei, Edo M. Airoldi

Attractor Network Dynamics Enable Preplay and Rapid Path Planning in Maze–like Environments Dane S. Corneil, Wulfram Gerstner

Exactness of Approximate MAP Inference in Continuous MRFs Nicholas Ruozzi

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

Subspace Clustering with Irrelevant Features via Robust Dantzig Selector Chao Qu, Huan Xu

Variational Consensus Monte Carlo Maxim Rabinovich, Elaine Angelino, Michael I. Jordan

Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks Samy Bengio, Oriol Vinyals, Navdeep Jaitly, Noam Shazeer

Path-SGD: Path-Normalized Optimization in Deep Neural Networks Behnam Neyshabur, Russ R. Salakhutdinov, Nati Srebro

Orthogonal NMF through Subspace Exploration Megasthenis Asteris, Dimitris Papailiopoulos, Alexandros G. Dimakis

M-Statistic for Kernel Change-Point Detection Shuang Li, Yao Xie, Hanjun Dai, Le Song

Active Learning from Weak and Strong Labelers Chicheng Zhang, Kamalika Chaudhuri

Sum-of-Squares Lower Bounds for Sparse PCA Tengyu Ma, Avi Wigderson

Where are they looking? Adria Recasens, Aditya Khosla, Carl Vondrick, Antonio Torralba

Softstar: Heuristic-Guided Probabilistic Inference Mathew Monfort, Brenden M. Lake, Brenden M. Lake, Brian Ziebart, Patrick Lucey, Josh Tenenbaum

Fast Bidirectional Probability Estimation in Markov Models Siddhartha Banerjee, Peter Lofgren

Learning to Linearize Under Uncertainty Ross Goroshin, Michael F. Mathieu, Yann LeCun

Variance Reduced Stochastic Gradient Descent with Neighbors Thomas Hofmann, Aurelien Lucchi, Simon Lacoste-Julien, Brian McWilliams

On Elicitation Complexity Rafael Frongillo, Ian Kash

Learning with Relaxed Supervision Jacob Steinhardt, Percy S. Liang

Matrix Completion Under Monotonic Single Index Models Ravi Sastry Ganti, Laura Balzano, Rebecca Willett

Principal Geodesic Analysis for Probability Measures under the Optimal Transport Metric Vivien Seguy, Marco Cuturi

HONOR: Hybrid Optimization for NOn-convex Regularized problems Pinghua Gong, Jieping Ye

The Poisson Gamma Belief Network Mingyuan Zhou, Yulai Cong, Bo Chen

Fixed-Length Poisson MRF: Adding Dependencies to the Multinomial David I. Inouye, Pradeep K. Ravikumar, Inderjit S. Dhillon

Stochastic Expectation Propagation Yingzhen Li, José Miguel Hernández-Lobato, Richard E. Turner

Beyond Sub-Gaussian Measurements: High-Dimensional Structured Estimation with Sub-Exponential Designs Vidyashankar Sivakumar, Arindam Banerjee, Pradeep K. Ravikumar

Fast Randomized Kernel Ridge Regression with Statistical Guarantees Ahmed Alaoui, Michael W. Mahoney

Skip-Thought Vectors Ryan Kiros, Yukun Zhu, Russ R. Salakhutdinov, Richard Zemel, Raquel Urtasun, Antonio Torralba, Sanja Fidler

Collaborative Filtering with Graph Information: Consistency and Scalable Methods Nikhil Rao, Hsiang-Fu Yu, Pradeep K. Ravikumar, Inderjit S. Dhillon

Gaussian Process Random Fields David Moore, Stuart J. Russell

Decoupled Deep Neural Network for Semi-supervised Semantic Segmentation Seunghoon Hong, Hyeonwoo Noh, Bohyung Han

Discrete Rényi Classifiers Meisam Razaviyayn, Farzan Farnia, David Tse

Preconditioned Spectral Descent for Deep Learning David E. Carlson, Edo Collins, Ya-Ping Hsieh, Lawrence Carin, Volkan Cevher

Accelerated Mirror Descent in Continuous and Discrete Time Walid Krichene, Alexandre Bayen, Peter L. Bartlett

Accelerated Proximal Gradient Methods for Nonconvex Programming Huan Li, Zhouchen Lin

Monotone k-Submodular Function Maximization with Size Constraints Naoto Ohsaka, Yuichi Yoshida

Spherical Random Features for Polynomial Kernels Jeffrey Pennington, Felix Xinnan X. Yu, Sanjiv Kumar

A Dual Augmented Block Minimization Framework for Learning with Limited Memory Ian En-Hsu Yen, Shan-Wei Lin, Shou-De Lin

Convolutional Networks on Graphs for Learning Molecular Fingerprints David K. Duvenaud, Dougal Maclaurin, Jorge Iparraguirre, Rafael Bombarell, Timothy Hirzel, Alan Aspuru-Guzik, Ryan P. Adams

Decomposition Bounds for Marginal MAP Wei Ping, Qiang Liu, Alexander T. Ihler

The Brain Uses Reliability of Stimulus Information when Making Perceptual Decisions Sebastian Bitzer, Stefan Kiebel

Are You Talking to a Machine? Dataset and Methods for Multilingual Image Question Haoyuan Gao, Junhua Mao, Jie Zhou, Zhiheng Huang, Lei Wang, Wei Xu

On the Pseudo-Dimension of Nearly Optimal Auctions Jamie H. Morgenstern, Tim Roughgarden

Fast Second Order Stochastic Backpropagation for Variational Inference Kai Fan, Ziteng Wang, Jeff Beck, James Kwok, Katherine A. Heller

Cross-Domain Matching for Bag-of-Words Data via Kernel Embeddings of Latent Distributions Yuya Yoshikawa, Tomoharu Iwata, Hiroshi Sawada, Takeshi Yamada

Learning Large-Scale Poisson DAG Models based on OverDispersion Scoring Gunwoong Park, Garvesh Raskutti

Local Causal Discovery of Direct Causes and Effects Tian Gao, Qiang Ji

Recognizing retinal ganglion cells in the dark Emile Richard, Georges A. Goetz, E.J. Chichilnisky

Advances in Neural Information Processing Systems 28 (NIPS 2015)

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