Book

Inferring neural population dynamics from multiple partial recordings of the same neural circuit Srini Turaga, Lars Buesing, Adam M. Packer, Henry Dalgleish, Noah Pettit, Michael Hausser, Jakob H. Macke

Approximate Gaussian process inference for the drift function in stochastic differential equations Andreas Ruttor, Philipp Batz, Manfred Opper

Third-Order Edge Statistics: Contour Continuation, Curvature, and Cortical Connections Matthew Lawlor, Steven W. Zucker

Transportability from Multiple Environments with Limited Experiments Elias Bareinboim, Sanghack Lee, Vasant Honavar, Judea Pearl

On model selection consistency of penalized M-estimators: a geometric theory Jason D. Lee, Yuekai Sun, Jonathan E. Taylor

Robust Bloom Filters for Large MultiLabel Classification Tasks Moustapha M. Cisse, Nicolas Usunier, Thierry Artières, Patrick Gallinari

On the Relationship Between Binary Classification, Bipartite Ranking, and Binary Class Probability Estimation Harikrishna Narasimhan, Shivani Agarwal

Sequential Transfer in Multi-armed Bandit with Finite Set of Models Mohammad Gheshlaghi azar, Alessandro Lazaric, Emma Brunskill

A Graphical Transformation for Belief Propagation: Maximum Weight Matchings and Odd-Sized Cycles Jinwoo Shin, Andrew E. Gelfand, Misha Chertkov

A Kernel Test for Three-Variable Interactions Dino Sejdinovic, Arthur Gretton, Wicher Bergsma

Accelerated Mini-Batch Stochastic Dual Coordinate Ascent Shai Shalev-Shwartz, Tong Zhang

A Scalable Approach to Probabilistic Latent Space Inference of Large-Scale Networks Junming Yin, Qirong Ho, Eric P. Xing

Multi-Prediction Deep Boltzmann Machines Ian Goodfellow, Mehdi Mirza, Aaron Courville, Yoshua Bengio

Learning and using language via recursive pragmatic reasoning about other agents Nathaniel J. Smith, Noah Goodman, Michael Frank

Reinforcement Learning in Robust Markov Decision Processes Shiau Hong Lim, Huan Xu, Shie Mannor

Regularized Spectral Clustering under the Degree-Corrected Stochastic Blockmodel Tai Qin, Karl Rohe

A Novel Two-Step Method for Cross Language Representation Learning Min Xiao, Yuhong Guo

Graphical Models for Inference with Missing Data Karthika Mohan, Judea Pearl, Jin Tian

Convex Tensor Decomposition via Structured Schatten Norm Regularization Ryota Tomioka, Taiji Suzuki

Variational Inference for Mahalanobis Distance Metrics in Gaussian Process Regression Michalis Titsias RC AUEB, Miguel Lazaro-Gredilla

Efficient Online Inference for Bayesian Nonparametric Relational Models Dae Il Kim, Prem K. Gopalan, David Blei, Erik Sudderth

Convergence of Monte Carlo Tree Search in Simultaneous Move Games Viliam Lisy, Vojta Kovarik, Marc Lanctot, Branislav Bosansky

Learning to Pass Expectation Propagation Messages Nicolas Heess, Daniel Tarlow, John Winn

Bayesian Inference and Online Experimental Design for Mapping Neural Microcircuits Ben Shababo, Brooks Paige, Ari Pakman, Liam Paninski

Action is in the Eye of the Beholder: Eye-gaze Driven Model for Spatio-Temporal Action Localization Nataliya Shapovalova, Michalis Raptis, Leonid Sigal, Greg Mori

Integrated Non-Factorized Variational Inference Shaobo Han, Xuejun Liao, Lawrence Carin

A Gang of Bandits Nicolò Cesa-Bianchi, Claudio Gentile, Giovanni Zappella

Multiclass Total Variation Clustering Xavier Bresson, Thomas Laurent, David Uminsky, James von Brecht

Simultaneous Rectification and Alignment via Robust Recovery of Low-rank Tensors Xiaoqin Zhang, Di Wang, Zhengyuan Zhou, Yi Ma

BIG & QUIC: Sparse Inverse Covariance Estimation for a Million Variables Cho-Jui Hsieh, Matyas A. Sustik, Inderjit S. Dhillon, Pradeep K. Ravikumar, Russell Poldrack

Robust Multimodal Graph Matching: Sparse Coding Meets Graph Matching Marcelo Fiori, Pablo Sprechmann, Joshua Vogelstein, Pablo Muse, Guillermo Sapiro

Optimal integration of visual speed across different spatiotemporal frequency channels Matjaz Jogan, Alan A. Stocker

Translating Embeddings for Modeling Multi-relational Data Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, Oksana Yakhnenko

Synthesizing Robust Plans under Incomplete Domain Models Tuan A. Nguyen, Subbarao Kambhampati, Minh Do

Learning Gaussian Graphical Models with Observed or Latent FVSs Ying Liu, Alan Willsky

Extracting regions of interest from biological images with convolutional sparse block coding Marius Pachitariu, Adam M. Packer, Noah Pettit, Henry Dalgleish, Michael Hausser, Maneesh Sahani

Training and Analysing Deep Recurrent Neural Networks Michiel Hermans, Benjamin Schrauwen

Low-Rank Matrix and Tensor Completion via Adaptive Sampling Akshay Krishnamurthy, Aarti Singh

Fast Determinantal Point Process Sampling with Application to Clustering Byungkon Kang

Matrix factorization with binary components Martin Slawski, Matthias Hein, Pavlo Lutsik

Reshaping Visual Datasets for Domain Adaptation Boqing Gong, Kristen Grauman, Fei Sha

Perfect Associative Learning with Spike-Timing-Dependent Plasticity Christian Albers, Maren Westkott, Klaus Pawelzik

Tracking Time-varying Graphical Structure Erich Kummerfeld, David Danks

Phase Retrieval using Alternating Minimization Praneeth Netrapalli, Prateek Jain, Sujay Sanghavi

Unsupervised Structure Learning of Stochastic And-Or Grammars Kewei Tu, Maria Pavlovskaia, Song-Chun Zhu

Learning Multi-level Sparse Representations Ferran Diego Andilla, Fred A. Hamprecht

Estimation, Optimization, and Parallelism when Data is Sparse John Duchi, Michael I. Jordan, Brendan McMahan

Predictive PAC Learning and Process Decompositions Cosma Shalizi, Aryeh Kontorovich

Scalable Inference for Logistic-Normal Topic Models Jianfei Chen, Jun Zhu, Zi Wang, Xun Zheng, Bo Zhang

A multi-agent control framework for co-adaptation in brain-computer interfaces Josh S. Merel, Roy Fox, Tony Jebara, Liam Paninski

Conditional Random Fields via Univariate Exponential Families Eunho Yang, Pradeep K. Ravikumar, Genevera I. Allen, Zhandong Liu

Adaptivity to Local Smoothness and Dimension in Kernel Regression Samory Kpotufe, Vikas Garg

Online Learning with Costly Features and Labels Navid Zolghadr, Gabor Bartok, Russell Greiner, András György, Csaba Szepesvari

An Approximate, Efficient LP Solver for LP Rounding Srikrishna Sridhar, Stephen Wright, Christopher Re, Ji Liu, Victor Bittorf, Ce Zhang

Regression-tree Tuning in a Streaming Setting Samory Kpotufe, Francesco Orabona

Estimating LASSO Risk and Noise Level Mohsen Bayati, Murat A. Erdogdu, Andrea Montanari

Demixing odors - fast inference in olfaction Agnieszka Grabska-Barwinska, Jeff Beck, Alexandre Pouget, Peter Latham

Zero-Shot Learning Through Cross-Modal Transfer Richard Socher, Milind Ganjoo, Christopher D. Manning, Andrew Ng

Optimistic policy iteration and natural actor-critic: A unifying view and a non-optimality result Paul Wagner

Bayesian Inference and Learning in Gaussian Process State-Space Models with Particle MCMC Roger Frigola, Fredrik Lindsten, Thomas B. Schön, Carl Edward Rasmussen

Stochastic Gradient Riemannian Langevin Dynamics on the Probability Simplex Sam Patterson, Yee Whye Teh

When are Overcomplete Topic Models Identifiable? Uniqueness of Tensor Tucker Decompositions with Structured Sparsity Anima Anandkumar, Daniel J. Hsu, Majid Janzamin, Sham M. Kakade

Sign Cauchy Projections and Chi-Square Kernel Ping Li, Gennady Samorodnitsk, John Hopcroft

Transfer Learning in a Transductive Setting Marcus Rohrbach, Sandra Ebert, Bernt Schiele

Solving inverse problem of Markov chain with partial observations Tetsuro Morimura, Takayuki Osogami, Tsuyoshi Ide

Wavelets on Graphs via Deep Learning Raif Rustamov, Leonidas J. Guibas

Stochastic Convex Optimization with Multiple Objectives Mehrdad Mahdavi, Tianbao Yang, Rong Jin

Bayesian Hierarchical Community Discovery Charles Blundell, Yee Whye Teh

Contrastive Learning Using Spectral Methods James Y. Zou, Daniel J. Hsu, David C. Parkes, Ryan P. Adams

Deep Fisher Networks for Large-Scale Image Classification Karen Simonyan, Andrea Vedaldi, Andrew Zisserman

Linear Convergence with Condition Number Independent Access of Full Gradients Lijun Zhang, Mehrdad Mahdavi, Rong Jin

Learning with Noisy Labels Nagarajan Natarajan, Inderjit S. Dhillon, Pradeep K. Ravikumar, Ambuj Tewari

Variational Policy Search via Trajectory Optimization Sergey Levine, Vladlen Koltun

Dropout Training as Adaptive Regularization Stefan Wager, Sida Wang, Percy S. Liang

Prior-free and prior-dependent regret bounds for Thompson Sampling Sebastien Bubeck, Che-Yu Liu

Geometric optimisation on positive definite matrices for elliptically contoured distributions Suvrit Sra, Reshad Hosseini

Capacity of strong attractor patterns to model behavioural and cognitive prototypes Abbas Edalat

Manifold-based Similarity Adaptation for Label Propagation Masayuki Karasuyama, Hiroshi Mamitsuka

New Subsampling Algorithms for Fast Least Squares Regression Paramveer Dhillon, Yichao Lu, Dean P. Foster, Lyle Ungar

A message-passing algorithm for multi-agent trajectory planning José Bento, Nate Derbinsky, Javier Alonso-Mora, Jonathan S. Yedidia

Solving the multi-way matching problem by permutation synchronization Deepti Pachauri, Risi Kondor, Vikas Singh

Auditing: Active Learning with Outcome-Dependent Query Costs Sivan Sabato, Anand D. Sarwate, Nati Srebro

Restricting exchangeable nonparametric distributions Sinead A. Williamson, Steve N. MacEachern, Eric P. Xing

On the Linear Convergence of the Proximal Gradient Method for Trace Norm Regularization Ke Hou, Zirui Zhou, Anthony Man-Cho So, Zhi-Quan Luo

Eluder Dimension and the Sample Complexity of Optimistic Exploration Daniel Russo, Benjamin Van Roy

Efficient Algorithm for Privately Releasing Smooth Queries Ziteng Wang, Kai Fan, Jiaqi Zhang, Liwei Wang

Buy-in-Bulk Active Learning Liu Yang, Jaime Carbonell

On Poisson Graphical Models Eunho Yang, Pradeep K. Ravikumar, Genevera I. Allen, Zhandong Liu

On Sampling from the Gibbs Distribution with Random Maximum A-Posteriori Perturbations Tamir Hazan, Subhransu Maji, Tommi Jaakkola

Factorized Asymptotic Bayesian Inference for Latent Feature Models Kohei Hayashi, Ryohei Fujimaki

Minimax Theory for High-dimensional Gaussian Mixtures with Sparse Mean Separation Martin Azizyan, Aarti Singh, Larry Wasserman

Efficient Optimization for Sparse Gaussian Process Regression Yanshuai Cao, Marcus A. Brubaker, David J. Fleet, Aaron Hertzmann

Robust learning of low-dimensional dynamics from large neural ensembles David Pfau, Eftychios A. Pnevmatikakis, Liam Paninski

Causal Inference on Time Series using Restricted Structural Equation Models Jonas Peters, Dominik Janzing, Bernhard Schölkopf

Better Approximation and Faster Algorithm Using the Proximal Average Yao-Liang Yu

Robust Low Rank Kernel Embeddings of Multivariate Distributions Le Song, Bo Dai

Learning the Local Statistics of Optical Flow Dan Rosenbaum, Daniel Zoran, Yair Weiss

Fast Algorithms for Gaussian Noise Invariant Independent Component Analysis James R. Voss, Luis Rademacher, Mikhail Belkin

Stochastic Majorization-Minimization Algorithms for Large-Scale Optimization Julien Mairal

Online Learning in Markov Decision Processes with Adversarially Chosen Transition Probability Distributions Yasin Abbasi Yadkori, Peter L. Bartlett, Varun Kanade, Yevgeny Seldin, Csaba Szepesvari

Improved and Generalized Upper Bounds on the Complexity of Policy Iteration Bruno Scherrer

Approximate Inference in Continuous Determinantal Processes Raja Hafiz Affandi, Emily Fox, Ben Taskar

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

One-shot learning by inverting a compositional causal process Brenden M. Lake, Russ R. Salakhutdinov, Josh Tenenbaum

Large Scale Distributed Sparse Precision Estimation Huahua Wang, Arindam Banerjee, Cho-Jui Hsieh, Pradeep K. Ravikumar, Inderjit S. Dhillon

Online Variational Approximations to non-Exponential Family Change Point Models: With Application to Radar Tracking Ryan D. Turner, Steven Bottone, Clay J. Stanek

RNADE: The real-valued neural autoregressive density-estimator Benigno Uria, Iain Murray, Hugo Larochelle

Estimating the Unseen: Improved Estimators for Entropy and other Properties Paul Valiant, Gregory Valiant

Dynamic Clustering via Asymptotics of the Dependent Dirichlet Process Mixture Trevor Campbell, Miao Liu, Brian Kulis, Jonathan P. How, Lawrence Carin

Parametric Task Learning Ichiro Takeuchi, Tatsuya Hongo, Masashi Sugiyama, Shinichi Nakajima

Generalized Denoising Auto-Encoders as Generative Models Yoshua Bengio, Li Yao, Guillaume Alain, Pascal Vincent

Local Privacy and Minimax Bounds: Sharp Rates for Probability Estimation John Duchi, Martin J. Wainwright, Michael I. Jordan

Reward Mapping for Transfer in Long-Lived Agents Xiaoxiao Guo, Satinder Singh, Richard L. Lewis

Distributed Exploration in Multi-Armed Bandits Eshcar Hillel, Zohar S. Karnin, Tomer Koren, Ronny Lempel, Oren Somekh

It is all in the noise: Efficient multi-task Gaussian process inference with structured residuals Barbara Rakitsch, Christoph Lippert, Karsten Borgwardt, Oliver Stegle

Projecting Ising Model Parameters for Fast Mixing Justin Domke, Xianghang Liu

Low-rank matrix reconstruction and clustering via approximate message passing Ryosuke Matsushita, Toshiyuki Tanaka

Inverse Density as an Inverse Problem: the Fredholm Equation Approach Qichao Que, Mikhail Belkin

Modeling Overlapping Communities with Node Popularities Prem K. Gopalan, Chong Wang, David Blei

Reflection methods for user-friendly submodular optimization Stefanie Jegelka, Francis Bach, Suvrit Sra

Compressive Feature Learning Hristo S. Paskov, Robert West, John C. Mitchell, Trevor Hastie

Sparse nonnegative deconvolution for compressive calcium imaging: algorithms and phase transitions Eftychios A. Pnevmatikakis, Liam Paninski

Probabilistic Low-Rank Matrix Completion with Adaptive Spectral Regularization Algorithms Adrien Todeschini, François Caron, Marie Chavent

Global Solver and Its Efficient Approximation for Variational Bayesian Low-rank Subspace Clustering Shinichi Nakajima, Akiko Takeda, S. Derin Babacan, Masashi Sugiyama, Ichiro Takeuchi

Reservoir Boosting : Between Online and Offline Ensemble Learning Leonidas Lefakis, François Fleuret

Faster Ridge Regression via the Subsampled Randomized Hadamard Transform Yichao Lu, Paramveer Dhillon, Dean P. Foster, Lyle Ungar

Convex Relaxations for Permutation Problems Fajwel Fogel, Rodolphe Jenatton, Francis Bach, Alexandre D'Aspremont

Online Learning of Dynamic Parameters in Social Networks Shahin Shahrampour, Sasha Rakhlin, Ali Jadbabaie

Discovering Hidden Variables in Noisy-Or Networks using Quartet Tests Yacine Jernite, Yonatan Halpern, David Sontag

Gaussian Process Conditional Copulas with Applications to Financial Time Series José Miguel Hernández-Lobato, James R. Lloyd, Daniel Hernández-Lobato

Non-Uniform Camera Shake Removal Using a Spatially-Adaptive Sparse Penalty Haichao Zhang, David Wipf

Online learning in episodic Markovian decision processes by relative entropy policy search Alexander Zimin, Gergely Neu

Bayesian inference for low rank spatiotemporal neural receptive fields Mijung Park, Jonathan W. Pillow

Global MAP-Optimality by Shrinking the Combinatorial Search Area with Convex Relaxation Bogdan Savchynskyy, Jörg Hendrik Kappes, Paul Swoboda, Christoph Schnörr

Error-Minimizing Estimates and Universal Entry-Wise Error Bounds for Low-Rank Matrix Completion Franz Kiraly, Louis Theran

Decision Jungles: Compact and Rich Models for Classification Jamie Shotton, Toby Sharp, Pushmeet Kohli, Sebastian Nowozin, John Winn, Antonio Criminisi

Bayesian Estimation of Latently-grouped Parameters in Undirected Graphical Models Jie Liu, David Page

(More) Efficient Reinforcement Learning via Posterior Sampling Ian Osband, Daniel Russo, Benjamin Van Roy

Forgetful Bayes and myopic planning: Human learning and decision-making in a bandit setting Shunan Zhang, Angela J. Yu

Stochastic Optimization of PCA with Capped MSG Raman Arora, Andy Cotter, Nati Srebro

Embed and Project: Discrete Sampling with Universal Hashing Stefano Ermon, Carla P. Gomes, Ashish Sabharwal, Bart Selman

Optimal Neural Population Codes for High-dimensional Stimulus Variables Zhuo Wang, Alan A. Stocker, Daniel D. Lee

Near-Optimal Entrywise Sampling for Data Matrices Dimitris Achlioptas, Zohar S. Karnin, Edo Liberty

A Comparative Framework for Preconditioned Lasso Algorithms Fabian L. Wauthier, Nebojsa Jojic, Michael I. Jordan

Universal models for binary spike patterns using centered Dirichlet processes Il Memming Park, Evan W. Archer, Kenneth Latimer, Jonathan W. Pillow

What Are the Invariant Occlusive Components of Image Patches? A Probabilistic Generative Approach Zhenwen Dai, Georgios Exarchakis, Jörg Lücke

Correlations strike back (again): the case of associative memory retrieval Cristina Savin, Peter Dayan, Mate Lengyel

Understanding Dropout Pierre Baldi, Peter J. Sadowski

Supervised Sparse Analysis and Synthesis Operators Pablo Sprechmann, Roee Litman, Tal Ben Yakar, Alexander M. Bronstein, Guillermo Sapiro

The Pareto Regret Frontier Wouter M. Koolen

Approximate Dynamic Programming Finally Performs Well in the Game of Tetris Victor Gabillon, Mohammad Ghavamzadeh, Bruno Scherrer

Learning Feature Selection Dependencies in Multi-task Learning Daniel Hernández-Lobato, José Miguel Hernández-Lobato

Dimension-Free Exponentiated Gradient Francesco Orabona

Memory Limited, Streaming PCA Ioannis Mitliagkas, Constantine Caramanis, Prateek Jain

Σ-Optimality for Active Learning on Gaussian Random Fields Yifei Ma, Roman Garnett, Jeff Schneider

Recurrent linear models of simultaneously-recorded neural populations Marius Pachitariu, Biljana Petreska, Maneesh Sahani

On the Complexity and Approximation of Binary Evidence in Lifted Inference Guy Van den Broeck, Adnan Darwiche

Pass-efficient unsupervised feature selection Crystal Maung, Haim Schweitzer

Adaptive dropout for training deep neural networks Jimmy Ba, Brendan Frey

On the Representational Efficiency of Restricted Boltzmann Machines James Martens, Arkadev Chattopadhya, Toni Pitassi, Richard Zemel

Robust Spatial Filtering with Beta Divergence Wojciech Samek, Duncan Blythe, Klaus-Robert Müller, Motoaki Kawanabe

DeViSE: A Deep Visual-Semantic Embedding Model Andrea Frome, Greg S. Corrado, Jon Shlens, Samy Bengio, Jeff Dean, Marc'Aurelio Ranzato, Tomas Mikolov

Symbolic Opportunistic Policy Iteration for Factored-Action MDPs Aswin Raghavan, Roni Khardon, Alan Fern, Prasad Tadepalli

Least Informative Dimensions Fabian Sinz, Anna Stockl, Jan Grewe, Jan Benda

A memory frontier for complex synapses Subhaneil Lahiri, Surya Ganguli

Data-driven Distributionally Robust Polynomial Optimization Martin Mevissen, Emanuele Ragnoli, Jia Yuan Yu

Learning Stochastic Inverses Andreas Stuhlmüller, Jacob Taylor, Noah Goodman

Stochastic Ratio Matching of RBMs for Sparse High-Dimensional Inputs Yann Dauphin, Yoshua Bengio

Distributed $k$-means and $k$-median Clustering on General Topologies Maria-Florina F. Balcan, Steven Ehrlich, Yingyu Liang

Non-strongly-convex smooth stochastic approximation with convergence rate O(1/n) Francis Bach, Eric Moulines

Predicting Parameters in Deep Learning Misha Denil, Babak Shakibi, Laurent Dinh, Marc'Aurelio Ranzato, Nando de Freitas

Estimation Bias in Multi-Armed Bandit Algorithms for Search Advertising Min Xu, Tao Qin, Tie-Yan Liu

Learning Efficient Random Maximum A-Posteriori Predictors with Non-Decomposable Loss Functions Tamir Hazan, Subhransu Maji, Joseph Keshet, Tommi Jaakkola

q-OCSVM: A q-Quantile Estimator for High-Dimensional Distributions Assaf Glazer, Michael Lindenbaum, Shaul Markovitch

Fantope Projection and Selection: A near-optimal convex relaxation of sparse PCA Vincent Q. Vu, Juhee Cho, Jing Lei, Karl Rohe

Fast Template Evaluation with Vector Quantization Mohammad Amin Sadeghi, David Forsyth

Sparse Additive Text Models with Low Rank Background Lei Shi

Correlated random features for fast semi-supervised learning Brian McWilliams, David Balduzzi, Joachim M. Buhmann

Variational Planning for Graph-based MDPs Qiang Cheng, Qiang Liu, Feng Chen, Alexander T. Ihler

Adaptive Anonymity via $b$-Matching Krzysztof M. Choromanski, Tony Jebara, Kui Tang

Statistical Active Learning Algorithms Maria-Florina F. Balcan, Vitaly Feldman

The Power of Asymmetry in Binary Hashing Behnam Neyshabur, Nati Srebro, Russ R. Salakhutdinov, Yury Makarychev, Payman Yadollahpour

Bayesian Mixture Modelling and Inference based Thompson Sampling in Monte-Carlo Tree Search Aijun Bai, Feng Wu, Xiaoping Chen

Distributed Submodular Maximization: Identifying Representative Elements in Massive Data Baharan Mirzasoleiman, Amin Karbasi, Rik Sarkar, Andreas Krause

Analyzing the Harmonic Structure in Graph-Based Learning Xiao-Ming Wu, Zhenguo Li, Shih-Fu Chang

Near-optimal Anomaly Detection in Graphs using Lovasz Extended Scan Statistic James L. Sharpnack, Akshay Krishnamurthy, Aarti Singh

Bellman Error Based Feature Generation using Random Projections on Sparse Spaces Mahdi Milani Fard, Yuri Grinberg, Amir-massoud Farahmand, Joelle Pineau, Doina Precup

Similarity Component Analysis Soravit Changpinyo, Kuan Liu, Fei Sha

Matrix Completion From any Given Set of Observations Troy Lee, Adi Shraibman

A Deep Architecture for Matching Short Texts Zhengdong Lu, Hang Li

Sensor Selection in High-Dimensional Gaussian Trees with Nuisances Daniel S. Levine, Jonathan P. How

The Total Variation on Hypergraphs - Learning on Hypergraphs Revisited Matthias Hein, Simon Setzer, Leonardo Jost, Syama Sundar Rangapuram

Lasso Screening Rules via Dual Polytope Projection Jie Wang, Jiayu Zhou, Peter Wonka, Jieping Ye

Multiscale Dictionary Learning for Estimating Conditional Distributions Francesca Petralia, Joshua T. Vogelstein, David B. Dunson

Dirty Statistical Models Eunho Yang, Pradeep K. Ravikumar

Online Learning of Nonparametric Mixture Models via Sequential Variational Approximation Dahua Lin

Exact and Stable Recovery of Pairwise Interaction Tensors Shouyuan Chen, Michael R. Lyu, Irwin King, Zenglin Xu

A* Lasso for Learning a Sparse Bayesian Network Structure for Continuous Variables Jing Xiang, Seyoung Kim

High-Dimensional Gaussian Process Bandits Josip Djolonga, Andreas Krause, Volkan Cevher

Generalizing Analytic Shrinkage for Arbitrary Covariance Structures Daniel Bartz, Klaus-Robert Müller

Higher Order Priors for Joint Intrinsic Image, Objects, and Attributes Estimation Vibhav Vineet, Carsten Rother, Philip Torr

Online Robust PCA via Stochastic Optimization Jiashi Feng, Huan Xu, Shuicheng Yan

Compete to Compute Rupesh K. Srivastava, Jonathan Masci, Sohrob Kazerounian, Faustino Gomez, Jürgen Schmidhuber

Heterogeneous-Neighborhood-based Multi-Task Local Learning Algorithms Yu Zhang

Scalable Influence Estimation in Continuous-Time Diffusion Networks Nan Du, Le Song, Manuel Gomez Rodriguez, Hongyuan Zha

More data speeds up training time in learning halfspaces over sparse vectors Amit Daniely, Nati Linial, Shai Shalev-Shwartz

Top-Down Regularization of Deep Belief Networks Hanlin Goh, Nicolas Thome, Matthieu Cord, Joo-Hwee Lim

Polar Operators for Structured Sparse Estimation Xinhua Zhang, Yao-Liang Yu, Dale Schuurmans

Learning with Invariance via Linear Functionals on Reproducing Kernel Hilbert Space Xinhua Zhang, Wee Sun Lee, Yee Whye Teh

Real-Time Inference for a Gamma Process Model of Neural Spiking David E. Carlson, Vinayak Rao, Joshua T. Vogelstein, Lawrence Carin

Direct 0-1 Loss Minimization and Margin Maximization with Boosting Shaodan Zhai, Tian Xia, Ming Tan, Shaojun Wang

Marginals-to-Models Reducibility Tim Roughgarden, Michael Kearns

Sketching Structured Matrices for Faster Nonlinear Regression Haim Avron, Vikas Sindhwani, David Woodruff

Variance Reduction for Stochastic Gradient Optimization Chong Wang, Xi Chen, Alexander J. Smola, Eric P. Xing

On Decomposing the Proximal Map Yao-Liang Yu

Documents as multiple overlapping windows into grids of counts Alessandro Perina, Nebojsa Jojic, Manuele Bicego, Andrzej Truski

Robust Sparse Principal Component Regression under the High Dimensional Elliptical Model Fang Han, Han Liu

Optimizing Instructional Policies Robert V. Lindsey, Michael C. Mozer, William J. Huggins, Harold Pashler

Adaptive Market Making via Online Learning Jacob Abernethy, Satyen Kale

Learning Prices for Repeated Auctions with Strategic Buyers Kareem Amin, Afshin Rostamizadeh, Umar Syed

Multilinear Dynamical Systems for Tensor Time Series Mark Rogers, Lei Li, Stuart J. Russell

Computing the Stationary Distribution Locally Christina E. Lee, Asuman Ozdaglar, Devavrat Shah

Latent Maximum Margin Clustering Guang-Tong Zhou, Tian Lan, Arash Vahdat, Greg Mori

Hierarchical Modular Optimization of Convolutional Networks Achieves Representations Similar to Macaque IT and Human Ventral Stream Daniel L. Yamins, Ha Hong, Charles Cadieu, James J. DiCarlo

Online PCA for Contaminated Data Jiashi Feng, Huan Xu, Shie Mannor, Shuicheng Yan

Distributed Representations of Words and Phrases and their Compositionality Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S. Corrado, Jeff Dean

Learning Multiple Models via Regularized Weighting Daniel Vainsencher, Shie Mannor, Huan Xu

Discriminative Transfer Learning with Tree-based Priors Nitish Srivastava, Russ R. Salakhutdinov

Machine Teaching for Bayesian Learners in the Exponential Family Jerry Zhu

Online Learning with Switching Costs and Other Adaptive Adversaries Nicolò Cesa-Bianchi, Ofer Dekel, Ohad Shamir

From Bandits to Experts: A Tale of Domination and Independence Noga Alon, Nicolò Cesa-Bianchi, Claudio Gentile, Yishay Mansour

Which Space Partitioning Tree to Use for Search? Parikshit Ram, Alexander Gray

Small-Variance Asymptotics for Hidden Markov Models Anirban Roychowdhury, Ke Jiang, Brian Kulis

Sparse Overlapping Sets Lasso for Multitask Learning and its Application to fMRI Analysis Nikhil Rao, Christopher Cox, Rob Nowak, Timothy T. Rogers

Submodular Optimization with Submodular Cover and Submodular Knapsack Constraints Rishabh K. Iyer, Jeff A. Bilmes

Model Selection for High-Dimensional Regression under the Generalized Irrepresentability Condition Adel Javanmard, Andrea Montanari

Scalable kernels for graphs with continuous attributes Aasa Feragen, Niklas Kasenburg, Jens Petersen, Marleen de Bruijne, Karsten Borgwardt

Bayesian optimization explains human active search Ali Borji, Laurent Itti

B-test: A Non-parametric, Low Variance Kernel Two-sample Test Wojciech Zaremba, Arthur Gretton, Matthew Blaschko

Moment-based Uniform Deviation Bounds for $k$-means and Friends Matus J. Telgarsky, Sanjoy Dasgupta

Convex Calibrated Surrogates for Low-Rank Loss Matrices with Applications to Subset Ranking Losses Harish G. Ramaswamy, Shivani Agarwal, Ambuj Tewari

Auxiliary-variable Exact Hamiltonian Monte Carlo Samplers for Binary Distributions Ari Pakman, Liam Paninski

Spectral methods for neural characterization using generalized quadratic models Il Memming Park, Evan W. Archer, Nicholas Priebe, Jonathan W. Pillow

A Latent Source Model for Nonparametric Time Series Classification George H. Chen, Stanislav Nikolov, Devavrat Shah

PAC-Bayes-Empirical-Bernstein Inequality Ilya O. Tolstikhin, Yevgeny Seldin

Convex Two-Layer Modeling Özlem Aslan, Hao Cheng, Xinhua Zhang, Dale Schuurmans

The Randomized Dependence Coefficient David Lopez-Paz, Philipp Hennig, Bernhard Schölkopf

Sparse Inverse Covariance Estimation with Calibration Tuo Zhao, Han Liu

Thompson Sampling for 1-Dimensional Exponential Family Bandits Nathaniel Korda, Emilie Kaufmann, Remi Munos

Accelerating Stochastic Gradient Descent using Predictive Variance Reduction Rie Johnson, Tong Zhang

Multisensory Encoding, Decoding, and Identification Aurel A. Lazar, Yevgeniy Slutskiy

Learning invariant representations and applications to face verification Qianli Liao, Joel Z. Leibo, Tomaso Poggio

Optimistic Concurrency Control for Distributed Unsupervised Learning Xinghao Pan, Joseph E. Gonzalez, Stefanie Jegelka, Tamara Broderick, Michael I. Jordan

Statistical analysis of coupled time series with Kernel Cross-Spectral Density operators. Michel Besserve, Nikos K. Logothetis, Bernhard Schölkopf

Sinkhorn Distances: Lightspeed Computation of Optimal Transport Marco Cuturi

Nonparametric Multi-group Membership Model for Dynamic Networks Myunghwan Kim, Jure Leskovec

EDML for Learning Parameters in Directed and Undirected Graphical Models Khaled S. Refaat, Arthur Choi, Adnan Darwiche

Flexible sampling of discrete data correlations without the marginal distributions Alfredo Kalaitzis, Ricardo Silva

Designed Measurements for Vector Count Data Liming Wang, David E. Carlson, Miguel Rodrigues, David Wilcox, Robert Calderbank, Lawrence Carin

Reasoning With Neural Tensor Networks for Knowledge Base Completion Richard Socher, Danqi Chen, Christopher D. Manning, Andrew Ng

Deep content-based music recommendation Aaron van den Oord, Sander Dieleman, Benjamin Schrauwen

What do row and column marginals reveal about your dataset? Behzad Golshan, John Byers, Evimaria Terzi

Analyzing Hogwild Parallel Gaussian Gibbs Sampling Matthew J. Johnson, James Saunderson, Alan Willsky

Latent Structured Active Learning Wenjie Luo, Alex Schwing, Raquel Urtasun

Confidence Intervals and Hypothesis Testing for High-Dimensional Statistical Models Adel Javanmard, Andrea Montanari

Stochastic blockmodel approximation of a graphon: Theory and consistent estimation Edo M. Airoldi, Thiago B. Costa, Stanley H. Chan

More Effective Distributed ML via a Stale Synchronous Parallel Parameter Server Qirong Ho, James Cipar, Henggang Cui, Seunghak Lee, Jin Kyu Kim, Phillip B. Gibbons, Garth A. Gibson, Greg Ganger, Eric P. Xing

On Algorithms for Sparse Multi-factor NMF Siwei Lyu, Xin Wang

Efficient Exploration and Value Function Generalization in Deterministic Systems Zheng Wen, Benjamin Van Roy

Parallel Sampling of DP Mixture Models using Sub-Cluster Splits Jason Chang, John W. Fisher III

Context-sensitive active sensing in humans Sheeraz Ahmad, He Huang, Angela J. Yu

On the Sample Complexity of Subspace Learning Alessandro Rudi, Guillermo D. Canas, Lorenzo Rosasco

Non-Linear Domain Adaptation with Boosting Carlos J. Becker, Christos M. Christoudias, Pascal Fua

Learning Trajectory Preferences for Manipulators via Iterative Improvement Ashesh Jain, Brian Wojcik, Thorsten Joachims, Ashutosh Saxena

Learning Chordal Markov Networks by Constraint Satisfaction Jukka Corander, Tomi Janhunen, Jussi Rintanen, Henrik Nyman, Johan Pensar

Curvature and Optimal Algorithms for Learning and Minimizing Submodular Functions Rishabh K. Iyer, Stefanie Jegelka, Jeff A. Bilmes

A New Convex Relaxation for Tensor Completion Bernardino Romera-Paredes, Massimiliano Pontil

DESPOT: Online POMDP Planning with Regularization Adhiraj Somani, Nan Ye, David Hsu, Wee Sun Lee

Speeding up Permutation Testing in Neuroimaging Chris Hinrichs, Vamsi K. Ithapu, Qinyuan Sun, Sterling C. Johnson, Vikas Singh

Neural representation of action sequences: how far can a simple snippet-matching model take us? Cheston Tan, Jedediah M. Singer, Thomas Serre, David Sheinberg, Tomaso Poggio

Modeling Clutter Perception using Parametric Proto-object Partitioning Chen-Ping Yu, Wen-Yu Hua, Dimitris Samaras, Greg Zelinsky

Relevance Topic Model for Unstructured Social Group Activity Recognition Fang Zhao, Yongzhen Huang, Liang Wang, Tieniu Tan

Generalized Random Utility Models with Multiple Types Hossein Azari Soufiani, Hansheng Diao, Zhenyu Lai, David C. Parkes

(Nearly) Optimal Algorithms for Private Online Learning in Full-information and Bandit Settings Abhradeep Guha Thakurta, Adam Smith

The Fast Convergence of Incremental PCA Akshay Balsubramani, Sanjoy Dasgupta, Yoav Freund

Point Based Value Iteration with Optimal Belief Compression for Dec-POMDPs Liam C. MacDermed, Charles L. Isbell

Summary Statistics for Partitionings and Feature Allocations Isik B. Fidaner, Taylan Cemgil

Learning Hidden Markov Models from Non-sequence Data via Tensor Decomposition Tzu-Kuo Huang, Jeff Schneider

On Flat versus Hierarchical Classification in Large-Scale Taxonomies Rohit Babbar, Ioannis Partalas, Eric Gaussier, Massih R. Amini

Scoring Workers in Crowdsourcing: How Many Control Questions are Enough? Qiang Liu, Alexander T. Ihler, Mark Steyvers

Cluster Trees on Manifolds Sivaraman Balakrishnan, Srivatsan Narayanan, Alessandro Rinaldo, Aarti Singh, Larry Wasserman

Bayesian inference as iterated random functions with applications to sequential inference in graphical models Arash Amini, XuanLong Nguyen

Recurrent networks of coupled Winner-Take-All oscillators for solving constraint satisfaction problems Hesham Mostafa, Lorenz. K. Mueller, Giacomo Indiveri

Rapid Distance-Based Outlier Detection via Sampling Mahito Sugiyama, Karsten Borgwardt

Visual Concept Learning: Combining Machine Vision and Bayesian Generalization on Concept Hierarchies Yangqing Jia, Joshua T. Abbott, Joseph L. Austerweil, Tom Griffiths, Trevor Darrell

Memoized Online Variational Inference for Dirichlet Process Mixture Models Michael C. Hughes, Erik Sudderth

Locally Adaptive Bayesian Multivariate Time Series Daniele Durante, Bruno Scarpa, David B. Dunson

When in Doubt, SWAP: High-Dimensional Sparse Recovery from Correlated Measurements Divyanshu Vats, Richard Baraniuk

Information-theoretic lower bounds for distributed statistical estimation with communication constraints Yuchen Zhang, John Duchi, Michael I. Jordan, Martin J. Wainwright

Learning Stochastic Feedforward Neural Networks Charlie Tang, Russ R. Salakhutdinov

Robust Transfer Principal Component Analysis with Rank Constraints Yuhong Guo

A Determinantal Point Process Latent Variable Model for Inhibition in Neural Spiking Data Jasper Snoek, Richard Zemel, Ryan P. Adams

Reciprocally Coupled Local Estimators Implement Bayesian Information Integration Distributively Wen-Hao Zhang, Si Wu

Structured Learning via Logistic Regression Justin Domke

Learning word embeddings efficiently with noise-contrastive estimation Andriy Mnih, Koray Kavukcuoglu

Generalized Method-of-Moments for Rank Aggregation Hossein Azari Soufiani, William Chen, David C. Parkes, Lirong Xia

Reconciling "priors" & "priors" without prejudice? Remi Gribonval, Pierre Machart

Learning a Deep Compact Image Representation for Visual Tracking Naiyan Wang, Dit-Yan Yeung

Trading Computation for Communication: Distributed Stochastic Dual Coordinate Ascent Tianbao Yang

Projected Natural Actor-Critic Philip S. Thomas, William C. Dabney, Stephen Giguere, Sridhar Mahadevan

Minimax Optimal Algorithms for Unconstrained Linear Optimization Brendan McMahan, Jacob Abernethy

Policy Shaping: Integrating Human Feedback with Reinforcement Learning Shane Griffith, Kaushik Subramanian, Jonathan Scholz, Charles L. Isbell, Andrea L. Thomaz

Regret based Robust Solutions for Uncertain Markov Decision Processes Asrar Ahmed, Pradeep Varakantham, Yossiri Adulyasak, Patrick Jaillet

Understanding variable importances in forests of randomized trees Gilles Louppe, Louis Wehenkel, Antonio Sutera, Pierre Geurts

Linear decision rule as aspiration for simple decision heuristics Özgür Şimşek

Adaptive Multi-Column Deep Neural Networks with Application to Robust Image Denoising Forest Agostinelli, Michael R. Anderson, Honglak Lee

Probabilistic Movement Primitives Alexandros Paraschos, Christian Daniel, Jan R. Peters, Gerhard Neumann

Speedup Matrix Completion with Side Information: Application to Multi-Label Learning Miao Xu, Rong Jin, Zhi-Hua Zhou

Message Passing Inference with Chemical Reaction Networks Nils E. Napp, Ryan P. Adams

Binary to Bushy: Bayesian Hierarchical Clustering with the Beta Coalescent Yuening Hu, Jordan L. Ying, Hal Daume III, Z. Irene Ying

A Stability-based Validation Procedure for Differentially Private Machine Learning Kamalika Chaudhuri, Staal A. Vinterbo

Unsupervised Spectral Learning of Finite State Transducers Raphael Bailly, Xavier Carreras, Ariadna Quattoni

One-shot learning and big data with n=2 Lee H. Dicker, Dean P. Foster

Mapping paradigm ontologies to and from the brain Yannick Schwartz, Bertrand Thirion, Gael Varoquaux

Density estimation from unweighted k-nearest neighbor graphs: a roadmap Ulrike Von Luxburg, Morteza Alamgir

Actor-Critic Algorithms for Risk-Sensitive MDPs Prashanth L.A., Mohammad Ghavamzadeh

Probabilistic Principal Geodesic Analysis Miaomiao Zhang, Tom Fletcher

k-Prototype Learning for 3D Rigid Structures Hu Ding, Ronald Berezney, Jinhui Xu

Learning Adaptive Value of Information for Structured Prediction David J. Weiss, Ben Taskar

Spike train entropy-rate estimation using hierarchical Dirichlet process priors Karin C. Knudson, Jonathan W. Pillow

Regularized M-estimators with nonconvexity: Statistical and algorithmic theory for local optima Po-Ling Loh, Martin J. Wainwright

Robust Data-Driven Dynamic Programming Grani Adiwena Hanasusanto, Daniel Kuhn

Provable Subspace Clustering: When LRR meets SSC Yu-Xiang Wang, Huan Xu, Chenlei Leng

Optimization, Learning, and Games with Predictable Sequences Sasha Rakhlin, Karthik Sridharan

Beyond Pairwise: Provably Fast Algorithms for Approximate $k$-Way Similarity Search Anshumali Shrivastava, Ping Li

Firing rate predictions in optimal balanced networks David G. Barrett, Sophie Denève, Christian K. Machens

Multi-Task Bayesian Optimization Kevin Swersky, Jasper Snoek, Ryan P. Adams

Using multiple samples to learn mixture models Jason D. Lee, Ran Gilad-Bachrach, Rich Caruana

First-order Decomposition Trees Nima Taghipour, Jesse Davis, Hendrik Blockeel

Noise-Enhanced Associative Memories Amin Karbasi, Amir Hesam Salavati, Amin Shokrollahi, Lav R. Varshney

Adaptive Submodular Maximization in Bandit Setting Victor Gabillon, Branislav Kveton, Zheng Wen, Brian Eriksson, S. Muthukrishnan

Approximate inference in latent Gaussian-Markov models from continuous time observations Botond Cseke, Manfred Opper, Guido Sanguinetti

Lexical and Hierarchical Topic Regression Viet-An Nguyen, Jordan L. Ying, Philip Resnik

Adaptive Step-Size for Policy Gradient Methods Matteo Pirotta, Marcello Restelli, Luca Bascetta

Mixed Optimization for Smooth Functions Mehrdad Mahdavi, Lijun Zhang, Rong Jin

Deep Neural Networks for Object Detection Christian Szegedy, Alexander Toshev, Dumitru Erhan

A simple example of Dirichlet process mixture inconsistency for the number of components Jeffrey W. Miller, Matthew T. Harrison

Learning Kernels Using Local Rademacher Complexity Corinna Cortes, Marius Kloft, Mehryar Mohri

Bayesian entropy estimation for binary spike train data using parametric prior knowledge Evan W. Archer, Il Memming Park, Jonathan W. Pillow

Mid-level Visual Element Discovery as Discriminative Mode Seeking Carl Doersch, Abhinav Gupta, Alexei A. Efros

Approximate Bayesian Image Interpretation using Generative Probabilistic Graphics Programs Vikash K. Mansinghka, Tejas D. Kulkarni, Yura N. Perov, Josh Tenenbaum

Annealing between distributions by averaging moments Roger B. Grosse, Chris J. Maddison, Russ R. Salakhutdinov

Blind Calibration in Compressed Sensing using Message Passing Algorithms Christophe Schulke, Francesco Caltagirone, Florent Krzakala, Lenka Zdeborová

Active Learning for Probabilistic Hypotheses Using the Maximum Gibbs Error Criterion Nguyen Viet Cuong, Wee Sun Lee, Nan Ye, Kian Ming A. Chai, Hai Leong Chieu

Two-Target Algorithms for Infinite-Armed Bandits with Bernoulli Rewards Thomas Bonald, Alexandre Proutiere

Learning to Prune in Metric and Non-Metric Spaces Leonid Boytsov, Bilegsaikhan Naidan

Learning from Limited Demonstrations Beomjoon Kim, Amir-massoud Farahmand, Joelle Pineau, Doina Precup

Aggregating Optimistic Planning Trees for Solving Markov Decision Processes Gunnar Kedenburg, Raphael Fonteneau, Remi Munos

Action from Still Image Dataset and Inverse Optimal Control to Learn Task Specific Visual Scanpaths Stefan Mathe, Cristian Sminchisescu

How to Hedge an Option Against an Adversary: Black-Scholes Pricing is Minimax Optimal Jacob Abernethy, Peter L. Bartlett, Rafael Frongillo, Andre Wibisono

Advances in Neural Information Processing Systems 26 (NIPS 2013)

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