Book

Topology Constraints in Graphical Models Marcelo Fiori, Pablo Musé, Guillermo Sapiro

Clustering Aggregation as Maximum-Weight Independent Set Nan Li, Longin Latecki

FastEx: Hash Clustering with Exponential Families Amr Ahmed, Sujith Ravi, Alex Smola, Shravan Narayanamurthy

The Bethe Partition Function of Log-supermodular Graphical Models Nicholas Ruozzi

Selective Labeling via Error Bound Minimization Quanquan Gu, Tong Zhang, Jiawei Han, Chris Ding

Practical Bayesian Optimization of Machine Learning Algorithms Jasper Snoek, Hugo Larochelle, Ryan P. Adams

Optimal Neural Tuning Curves for Arbitrary Stimulus Distributions: Discrimax, Infomax and Minimum $L_p$ Loss Zhuo Wang, Alan A. Stocker, Daniel D. Lee

Probabilistic Event Cascades for Alzheimer's disease Jonathan Huang, Daniel Alexander

Super-Bit Locality-Sensitive Hashing Jianqiu Ji, Jianmin Li, Shuicheng Yan, Bo Zhang, Qi Tian

Bayesian nonparametric models for bipartite graphs Francois Caron

Learning Label Trees for Probabilistic Modelling of Implicit Feedback Andriy Mnih, Yee Teh

Factoring nonnegative matrices with linear programs Ben Recht, Christopher Re, Joel Tropp, Victor Bittorf

Privacy Aware Learning Martin J. Wainwright, Michael Jordan, John C. Duchi

Truncation-free Online Variational Inference for Bayesian Nonparametric Models Chong Wang, David Blei

Provable ICA with Unknown Gaussian Noise, with Implications for Gaussian Mixtures and Autoencoders Sanjeev Arora, Rong Ge, Ankur Moitra, Sushant Sachdeva

Learning Image Descriptors with the Boosting-Trick Tomasz Trzcinski, Mario Christoudias, Vincent Lepetit, Pascal Fua

A latent factor model for highly multi-relational data Rodolphe Jenatton, Nicolas Roux, Antoine Bordes, Guillaume R. Obozinski

Bayesian estimation of discrete entropy with mixtures of stick-breaking priors Evan Archer, Il Memming Park, Jonathan Pillow

Timely Object Recognition Sergey Karayev, Tobias Baumgartner, Mario Fritz, Trevor Darrell

Efficient high dimensional maximum entropy modeling via symmetric partition functions Paul Vernaza, Drew Bagnell

Topic-Partitioned Multinetwork Embeddings Peter Krafft, Juston Moore, Bruce Desmarais, Hanna Wallach

Recovery of Sparse Probability Measures via Convex Programming Mert Pilanci, Laurent Ghaoui, Venkat Chandrasekaran

Proximal Newton-type methods for convex optimization Jason D. Lee, Yuekai Sun, Michael Saunders

Learning visual motion in recurrent neural networks Marius Pachitariu, Maneesh Sahani

Graphical Models via Generalized Linear Models Eunho Yang, Genevera Allen, Zhandong Liu, Pradeep Ravikumar

Searching for objects driven by context Bogdan Alexe, Nicolas Heess, Yee Teh, Vittorio Ferrari

Learning Mixtures of Tree Graphical Models Anima Anandkumar, Daniel J. Hsu, Furong Huang, Sham M. Kakade

Convex Multi-view Subspace Learning Martha White, Xinhua Zhang, Dale Schuurmans, Yao-liang Yu

Learning Invariant Representations of Molecules for Atomization Energy Prediction Grégoire Montavon, Katja Hansen, Siamac Fazli, Matthias Rupp, Franziska Biegler, Andreas Ziehe, Alexandre Tkatchenko, Anatole Lilienfeld, Klaus-Robert Müller

On Multilabel Classification and Ranking with Partial Feedback Claudio Gentile, Francesco Orabona

A dynamic excitatory-inhibitory network in a VLSI chip for spiking information reregistrations Juan Huo

Distributed Non-Stochastic Experts Varun Kanade, Zhenming Liu, Bozidar Radunovic

Deep Learning of Invariant Features via Simulated Fixations in Video Will Zou, Shenghuo Zhu, Kai Yu, Andrew Ng

Homeostatic plasticity in Bayesian spiking networks as Expectation Maximization with posterior constraints Stefan Habenschuss, Johannes Bill, Bernhard Nessler

Nonparametric Bayesian Inverse Reinforcement Learning for Multiple Reward Functions Jaedeug Choi, Kee-eung Kim

Human memory search as a random walk in a semantic network Joseph Austerweil, Joshua T. Abbott, Thomas Griffiths

Multi-criteria Anomaly Detection using Pareto Depth Analysis Ko-jen Hsiao, Kevin Xu, Jeff Calder, Alfred Hero

A Spectral Algorithm for Latent Dirichlet Allocation Anima Anandkumar, Dean P. Foster, Daniel J. Hsu, Sham M. Kakade, Yi-kai Liu

The Perturbed Variation Maayan Harel, Shie Mannor

Discriminatively Trained Sparse Code Gradients for Contour Detection Ren Xiaofeng, Liefeng Bo

A Bayesian Approach for Policy Learning from Trajectory Preference Queries Aaron Wilson, Alan Fern, Prasad Tadepalli

Kernel Hyperalignment Alexander Lorbert, Peter J. Ramadge

Multi-Task Averaging Sergey Feldman, Maya Gupta, Bela Frigyik

A Unifying Perspective of Parametric Policy Search Methods for Markov Decision Processes Thomas Furmston, David Barber

Convergence and Energy Landscape for Cheeger Cut Clustering Xavier Bresson, Thomas Laurent, David Uminsky, James Brecht

A Divide-and-Conquer Method for Sparse Inverse Covariance Estimation Cho-jui Hsieh, Arindam Banerjee, Inderjit Dhillon, Pradeep Ravikumar

Nonconvex Penalization Using Laplace Exponents and Concave Conjugates Zhihua Zhang, Bojun Tu

How They Vote: Issue-Adjusted Models of Legislative Behavior Sean Gerrish, David Blei

Multiclass Learning Approaches: A Theoretical Comparison with Implications Amit Daniely, Sivan Sabato, Shai Shwartz

Dynamical And-Or Graph Learning for Object Shape Modeling and Detection Xiaolong Wang, Liang Lin

Augmented-SVM: Automatic space partitioning for combining multiple non-linear dynamics Ashwini Shukla, Aude Billard

3D Social Saliency from Head-mounted Cameras Hyun Park, Eakta Jain, Yaser Sheikh

Coupling Nonparametric Mixtures via Latent Dirichlet Processes Dahua Lin, John Fisher

Multiclass Learning with Simplex Coding Youssef Mroueh, Tomaso Poggio, Lorenzo Rosasco, Jean-jeacques Slotine

Clustering Sparse Graphs Yudong Chen, Sujay Sanghavi, Huan Xu

On-line Reinforcement Learning Using Incremental Kernel-Based Stochastic Factorization Andre Barreto, Doina Precup, Joelle Pineau

Accelerated Training for Matrix-norm Regularization: A Boosting Approach Xinhua Zhang, Dale Schuurmans, Yao-liang Yu

Supervised Learning with Similarity Functions Purushottam Kar, Prateek Jain

A Simple and Practical Algorithm for Differentially Private Data Release Moritz Hardt, Katrina Ligett, Frank Mcsherry

Persistent Homology for Learning Densities with Bounded Support Florian Pokorny, Hedvig Kjellström, Danica Kragic, Carl Ek

Proper losses for learning from partial labels Jesús Cid-sueiro

Predicting Action Content On-Line and in Real Time before Action Onset – an Intracranial Human Study Uri Maoz, Shengxuan Ye, Ian Ross, Adam Mamelak, Christof Koch

Classification Calibration Dimension for General Multiclass Losses Harish G. Ramaswamy, Shivani Agarwal

Analog readout for optical reservoir computers Anteo Smerieri, François Duport, Yvon Paquot, Benjamin Schrauwen, Marc Haelterman, Serge Massar

Perfect Dimensionality Recovery by Variational Bayesian PCA Shinichi Nakajima, Ryota Tomioka, Masashi Sugiyama, S. Babacan

Compressive neural representation of sparse, high-dimensional probabilities Zachary Pitkow

Shifting Weights: Adapting Object Detectors from Image to Video Kevin Tang, Vignesh Ramanathan, Li Fei-fei, Daphne Koller

Minimization of Continuous Bethe Approximations: A Positive Variation Jason Pacheco, Erik Sudderth

Optimal Regularized Dual Averaging Methods for Stochastic Optimization Xi Chen, Qihang Lin, Javier Pena

Gradient Weights help Nonparametric Regressors Samory Kpotufe, Abdeslam Boularias

Regularized Off-Policy TD-Learning Bo Liu, Sridhar Mahadevan, Ji Liu

Locating Changes in Highly Dependent Data with Unknown Number of Change Points Azadeh Khaleghi, Daniil Ryabko

Controlled Recognition Bounds for Visual Learning and Exploration Vasiliy Karasev, Alessandro Chiuso, Stefano Soatto

Multi-Stage Multi-Task Feature Learning Pinghua Gong, Jieping Ye, Chang-shui Zhang

Fast Resampling Weighted v-Statistics Chunxiao Zhou, Jiseong Park, Yun Fu

Rational inference of relative preferences Nisheeth Srivastava, Paul R. Schrater

Latent Graphical Model Selection: Efficient Methods for Locally Tree-like Graphs Anima Anandkumar, Ragupathyraj Valluvan

On the connections between saliency and tracking Vijay Mahadevan, Nuno Vasconcelos

Symbolic Dynamic Programming for Continuous State and Observation POMDPs Zahra Zamani, Scott Sanner, Pascal Poupart, Kristian Kersting

Imitation Learning by Coaching He He, Jason Eisner, Hal Daume

Learning about Canonical Views from Internet Image Collections Elad Mezuman, Yair Weiss

A lattice filter model of the visual pathway Karol Gregor, Dmitri Chklovskii

Multi-scale Hyper-time Hardware Emulation of Human Motor Nervous System Based on Spiking Neurons using FPGA C. Niu, Sirish Nandyala, Won Sohn, Terence Sanger

Recognizing Activities by Attribute Dynamics Weixin Li, Nuno Vasconcelos

Statistical Consistency of Ranking Methods in A Rank-Differentiable Probability Space Yanyan Lan, Jiafeng Guo, Xueqi Cheng, Tie-yan Liu

A Scalable CUR Matrix Decomposition Algorithm: Lower Time Complexity and Tighter Bound Shusen Wang, Zhihua Zhang

Efficient Bayes-Adaptive Reinforcement Learning using Sample-Based Search Arthur Guez, David Silver, Peter Dayan

Dual-Space Analysis of the Sparse Linear Model Yi Wu, David Wipf

Neuronal Spike Generation Mechanism as an Oversampling, Noise-shaping A-to-D converter Dmitri Chklovskii, Daniel Soudry

Multiple Operator-valued Kernel Learning Hachem Kadri, Alain Rakotomamonjy, Philippe Preux, Francis Bach

No-Regret Algorithms for Unconstrained Online Convex Optimization Brendan Mcmahan, Matthew Streeter

Learning Partially Observable Models Using Temporally Abstract Decision Trees Erik Talvitie

Emergence of Object-Selective Features in Unsupervised Feature Learning Adam Coates, Andrej Karpathy, Andrew Ng

CPRL -- An Extension of Compressive Sensing to the Phase Retrieval Problem Henrik Ohlsson, Allen Yang, Roy Dong, Shankar Sastry

Learning optimal spike-based representations Ralph Bourdoukan, David Barrett, Sophie Deneve, Christian K. Machens

Collaborative Ranking With 17 Parameters Maksims Volkovs, Richard Zemel

Small-Variance Asymptotics for Exponential Family Dirichlet Process Mixture Models Ke Jiang, Brian Kulis, Michael Jordan

Deep Representations and Codes for Image Auto-Annotation Ryan Kiros, Csaba Szepesvári

Patient Risk Stratification for Hospital-Associated C. diff as a Time-Series Classification Task Jenna Wiens, Eric Horvitz, John Guttag

Meta-Gaussian Information Bottleneck Melanie Rey, Volker Roth

Efficient Monte Carlo Counterfactual Regret Minimization in Games with Many Player Actions Neil Burch, Marc Lanctot, Duane Szafron, Richard Gibson

Fusion with Diffusion for Robust Visual Tracking Yu Zhou, Xiang Bai, Wenyu Liu, Longin Latecki

Convolutional-Recursive Deep Learning for 3D Object Classification Richard Socher, Brody Huval, Bharath Bath, Christopher D. Manning, Andrew Ng

Transferring Expectations in Model-based Reinforcement Learning Trung Nguyen, Tomi Silander, Tze Leong

Modelling Reciprocating Relationships with Hawkes Processes Charles Blundell, Jeff Beck, Katherine A. Heller

Ancestor Sampling for Particle Gibbs Fredrik Lindsten, Thomas Schön, Michael Jordan

Interpreting prediction markets: a stochastic approach Rafael Frongillo, Nicholás Della Penna, Mark D. Reid

Nonparanormal Belief Propagation (NPNBP) Gal Elidan, Cobi Cario

Adaptive Stratified Sampling for Monte-Carlo integration of Differentiable functions Alexandra Carpentier, Rémi Munos

Bayesian Nonparametric Modeling of Suicide Attempts Francisco Ruiz, Isabel Valera, Carlos Blanco, Fernando Pérez-Cruz

Non-linear Metric Learning Dor Kedem, Stephen Tyree, Fei Sha, Gert Lanckriet, Kilian Q. Weinberger

Putting Bayes to sleep Dmitry Adamskiy, Manfred K. K. Warmuth, Wouter M. Koolen

Sparse Approximate Manifolds for Differential Geometric MCMC Ben Calderhead, Mátyás Sustik

Bayesian active learning with localized priors for fast receptive field characterization Mijung Park, Jonathan Pillow

Deep Neural Networks Segment Neuronal Membranes in Electron Microscopy Images Dan Ciresan, Alessandro Giusti, Luca Gambardella, Jürgen Schmidhuber

Learning from the Wisdom of Crowds by Minimax Entropy Dengyong Zhou, Sumit Basu, Yi Mao, John Platt

Parametric Local Metric Learning for Nearest Neighbor Classification Jun Wang, Alexandros Kalousis, Adam Woznica

The topographic unsupervised learning of natural sounds in the auditory cortex Hiroki Terashima, Masato Okada

Efficient Sampling for Bipartite Matching Problems Maksims Volkovs, Richard Zemel

Volume Regularization for Binary Classification Koby Crammer, Tal Wagner

Causal discovery with scale-mixture model for spatiotemporal variance dependencies Zhitang Chen, Kun Zhang, Laiwan Chan

Neurally Plausible Reinforcement Learning of Working Memory Tasks Jaldert Rombouts, Pieter Roelfsema, Sander Bohte

Simultaneously Leveraging Output and Task Structures for Multiple-Output Regression Piyush Rai, Abhishek Kumar, Hal Daume

Feature Clustering for Accelerating Parallel Coordinate Descent Chad Scherrer, Ambuj Tewari, Mahantesh Halappanavar, David Haglin

Phoneme Classification using Constrained Variational Gaussian Process Dynamical System Hyunsin Park, Sungrack Yun, Sanghyuk Park, Jongmin Kim, Chang Yoo

Fast Variational Inference in the Conjugate Exponential Family James Hensman, Magnus Rattray, Neil Lawrence

Identifiability and Unmixing of Latent Parse Trees Daniel J. Hsu, Sham M. Kakade, Percy S. Liang

On the (Non-)existence of Convex, Calibrated Surrogate Losses for Ranking Clément Calauzènes, Nicolas Usunier, Patrick Gallinari

Learning with Partially Absorbing Random Walks Xiao-ming Wu, Zhenguo Li, Anthony So, John Wright, Shih-fu Chang

Relax and Randomize : From Value to Algorithms Sasha Rakhlin, Ohad Shamir, Karthik Sridharan

Inverse Reinforcement Learning through Structured Classification Edouard Klein, Matthieu Geist, Bilal Piot, Olivier Pietquin

Efficient and direct estimation of a neural subunit model for sensory coding Brett Vintch, Andrew Zaharia, J Movshon, Eero Simoncelli

Discriminative Learning of Sum-Product Networks Robert Gens, Pedro Domingos

Stochastic optimization and sparse statistical recovery: Optimal algorithms for high dimensions Alekh Agarwal, Sahand Negahban, Martin J. Wainwright

Bandit Algorithms boost Brain Computer Interfaces for motor-task selection of a brain-controlled button Joan Fruitet, Alexandra Carpentier, Maureen Clerc, Rémi Munos

Localizing 3D cuboids in single-view images Jianxiong Xiao, Bryan Russell, Antonio Torralba

A Convex Formulation for Learning Scale-Free Networks via Submodular Relaxation Aaron Defazio, Tibério Caetano

Hamming Distance Metric Learning Mohammad Norouzi, David J. Fleet, Russ R. Salakhutdinov

Co-Regularized Hashing for Multimodal Data Yi Zhen, Dit-Yan Yeung

The Coloured Noise Expansion and Parameter Estimation of Diffusion Processes Simon Lyons, Amos J. Storkey, Simo Särkkä

How Prior Probability Influences Decision Making: A Unifying Probabilistic Model Yanping Huang, Timothy Hanks, Mike Shadlen, Abram L. Friesen, Rajesh PN Rao

Structured Learning of Gaussian Graphical Models Karthik Mohan, Mike Chung, Seungyeop Han, Daniela Witten, Su-in Lee, Maryam Fazel

Entropy Estimations Using Correlated Symmetric Stable Random Projections Ping Li, Cun-hui Zhang

Coding efficiency and detectability of rate fluctuations with non-Poisson neuronal firing Shinsuke Koyama

Nyström Method vs Random Fourier Features: A Theoretical and Empirical Comparison Tianbao Yang, Yu-feng Li, Mehrdad Mahdavi, Rong Jin, Zhi-Hua Zhou

Spiking and saturating dendrites differentially expand single neuron computation capacity Romain Cazé, Mark Humphries, Boris Gutkin

Active Learning of Model Evidence Using Bayesian Quadrature Michael Osborne, Roman Garnett, Zoubin Ghahramani, David K. Duvenaud, Stephen J. Roberts, Carl Rasmussen

Diffusion Decision Making for Adaptive k-Nearest Neighbor Classification Yung-kyun Noh, Frank Park, Daniel Lee

Bayesian Hierarchical Reinforcement Learning Feng Cao, Soumya Ray

Compressive Sensing MRI with Wavelet Tree Sparsity Chen Chen, Junzhou Huang

Mixability in Statistical Learning Tim Erven, Peter Grünwald, Mark D. Reid, Robert C. Williamson

Symmetric Correspondence Topic Models for Multilingual Text Analysis Kosuke Fukumasu, Koji Eguchi, Eric Xing

Learning Halfspaces with the Zero-One Loss: Time-Accuracy Tradeoffs Aharon Birnbaum, Shai Shwartz

Learning High-Density Regions for a Generalized Kolmogorov-Smirnov Test in High-Dimensional Data Assaf Glazer, Michael Lindenbaum, Shaul Markovitch

Factorial LDA: Sparse Multi-Dimensional Text Models Michael Paul, Mark Dredze

Augment-and-Conquer Negative Binomial Processes Mingyuan Zhou, Lawrence Carin

Large Scale Distributed Deep Networks Jeffrey Dean, Greg Corrado, Rajat Monga, Kai Chen, Matthieu Devin, Mark Mao, Marc'aurelio Ranzato, Andrew Senior, Paul Tucker, Ke Yang, Quoc Le, Andrew Ng

Structure estimation for discrete graphical models: Generalized covariance matrices and their inverses Po-ling Loh, Martin J. Wainwright

Joint Modeling of a Matrix with Associated Text via Latent Binary Features Xianxing Zhang, Lawrence Carin

Near-Optimal MAP Inference for Determinantal Point Processes Jennifer Gillenwater, Alex Kulesza, Ben Taskar

Image Denoising and Inpainting with Deep Neural Networks Junyuan Xie, Linli Xu, Enhong Chen

Strategic Impatience in Go/NoGo versus Forced-Choice Decision-Making Pradeep Shenoy, Angela J. Yu

Cost-Sensitive Exploration in Bayesian Reinforcement Learning Dongho Kim, Kee-eung Kim, Pascal Poupart

MCMC for continuous-time discrete-state systems Vinayak Rao, Yee Teh

Spectral Learning of General Weighted Automata via Constrained Matrix Completion Borja Balle, Mehryar Mohri

Learning with Recursive Perceptual Representations Oriol Vinyals, Yangqing Jia, Li Deng, Trevor Darrell

Scaled Gradients on Grassmann Manifolds for Matrix Completion Thanh Ngo, Yousef Saad

Link Prediction in Graphs with Autoregressive Features Emile Richard, Stephane Gaiffas, Nicolas Vayatis

A Generative Model for Parts-based Object Segmentation S. Eslami, Christopher Williams

Dimensionality Dependent PAC-Bayes Margin Bound Chi Jin, Liwei Wang

MAP Inference in Chains using Column Generation David Belanger, Alexandre Passos, Sebastian Riedel, Andrew McCallum

Cocktail Party Processing via Structured Prediction Yuxuan Wang, Deliang Wang

Fused sparsity and robust estimation for linear models with unknown variance Arnak Dalalyan, Yin Chen

On the Sample Complexity of Robust PCA Matthew Coudron, Gilad Lerman

Learning to Discover Social Circles in Ego Networks Jure Leskovec, Julian Mcauley

Exact and Stable Recovery of Sequences of Signals with Sparse Increments via Differential _1-Minimization Demba Ba, Behtash Babadi, Patrick Purdon, Emery Brown

Tight Bounds on Profile Redundancy and Distinguishability Jayadev Acharya, Hirakendu Das, Alon Orlitsky

On Triangular versus Edge Representations --- Towards Scalable Modeling of Networks Qirong Ho, Junming Yin, Eric Xing

A Better Way to Pretrain Deep Boltzmann Machines Geoffrey E. Hinton, Russ R. Salakhutdinov

Semi-supervised Eigenvectors for Locally-biased Learning Toke Hansen, Michael W. Mahoney

The variational hierarchical EM algorithm for clustering hidden Markov models Emanuele Coviello, Gert Lanckriet, Antoni Chan

Scalable nonconvex inexact proximal splitting Suvrit Sra

Bayesian nonparametric models for ranked data Francois Caron, Yee Teh

GenDeR: A Generic Diversified Ranking Algorithm Jingrui He, Hanghang Tong, Qiaozhu Mei, Boleslaw Szymanski

Accuracy at the Top Stephen Boyd, Corinna Cortes, Mehryar Mohri, Ana Radovanovic

Approximating Equilibria in Sequential Auctions with Incomplete Information and Multi-Unit Demand Amy Greenwald, Jiacui Li, Eric Sodomka

Multiresolution Gaussian Processes Emily Fox, David Dunson

Burn-in, bias, and the rationality of anchoring Falk Lieder, Tom Griffiths, Noah Goodman

Truly Nonparametric Online Variational Inference for Hierarchical Dirichlet Processes Michael Bryant, Erik Sudderth

3D Object Detection and Viewpoint Estimation with a Deformable 3D Cuboid Model Sanja Fidler, Sven Dickinson, Raquel Urtasun

Risk-Aversion in Multi-armed Bandits Amir Sani, Alessandro Lazaric, Rémi Munos

Approximate Message Passing with Consistent Parameter Estimation and Applications to Sparse Learning Ulugbek Kamilov, Sundeep Rangan, Michael Unser, Alyson K. Fletcher

Gradient-based kernel method for feature extraction and variable selection Kenji Fukumizu, Chenlei Leng

Scalable imputation of genetic data with a discrete fragmentation-coagulation process Lloyd Elliott, Yee Teh

Non-parametric Approximate Dynamic Programming via the Kernel Method Nikhil Bhat, Vivek Farias, Ciamac C. Moallemi

Probabilistic n-Choose-k Models for Classification and Ranking Kevin Swersky, Brendan J. Frey, Daniel Tarlow, Richard Zemel, Ryan P. Adams

Delay Compensation with Dynamical Synapses Chi Fung, K. Wong, Si Wu

Visual Recognition using Embedded Feature Selection for Curvature Self-Similarity Angela Eigenstetter, Bjorn Ommer

High-Order Multi-Task Feature Learning to Identify Longitudinal Phenotypic Markers for Alzheimer's Disease Progression Prediction Hua Wang, Feiping Nie, Heng Huang, Jingwen Yan, Sungeun Kim, Shannon Risacher, Andrew Saykin, Li Shen

Multiresolution analysis on the symmetric group Risi Kondor, Walter Dempsey

Learned Prioritization for Trading Off Accuracy and Speed Jiarong Jiang, Adam Teichert, Jason Eisner, Hal Daume

Learning as MAP Inference in Discrete Graphical Models Xianghang Liu, James Petterson, Tibério Caetano

Hierarchical Optimistic Region Selection driven by Curiosity Odalric-ambrym Maillard

Trajectory-Based Short-Sighted Probabilistic Planning Felipe Trevizan, Manuela Veloso

Best Arm Identification: A Unified Approach to Fixed Budget and Fixed Confidence Victor Gabillon, Mohammad Ghavamzadeh, Alessandro Lazaric

On the Use of Non-Stationary Policies for Stationary Infinite-Horizon Markov Decision Processes Bruno Scherrer, Boris Lesner

Deep Spatio-Temporal Architectures and Learning for Protein Structure Prediction Pietro Lena, Ken Nagata, Pierre Baldi

Isotropic Hashing Weihao Kong, Wu-jun Li

Repulsive Mixtures Francesca Petralia, Vinayak Rao, David Dunson

Forward-Backward Activation Algorithm for Hierarchical Hidden Markov Models Kei Wakabayashi, Takao Miura

Finding Exemplars from Pairwise Dissimilarities via Simultaneous Sparse Recovery Ehsan Elhamifar, Guillermo Sapiro, René Vidal

Mirror Descent Meets Fixed Share (and feels no regret) Nicolò Cesa-bianchi, Pierre Gaillard, Gabor Lugosi, Gilles Stoltz

Semi-Supervised Domain Adaptation with Non-Parametric Copulas David Lopez-paz, Jose Hernández-lobato, Bernhard Schölkopf

The Lovász ϑ function, SVMs and finding large dense subgraphs Vinay Jethava, Anders Martinsson, Chiranjib Bhattacharyya, Devdatt Dubhashi

Slice sampling normalized kernel-weighted completely random measure mixture models Nick Foti, Sinead Williamson

Automatic Feature Induction for Stagewise Collaborative Filtering Joonseok Lee, Mingxuan Sun, Seungyeon Kim, Guy Lebanon

A Stochastic Gradient Method with an Exponential Convergence _Rate for Finite Training Sets Nicolas Roux, Mark Schmidt, Francis Bach

Monte Carlo Methods for Maximum Margin Supervised Topic Models Qixia Jiang, Jun Zhu, Maosong Sun, Eric Xing

Analyzing 3D Objects in Cluttered Images Mohsen Hejrati, Deva Ramanan

A mechanistic model of early sensory processing based on subtracting sparse representations Shaul Druckmann, Tao Hu, Dmitri Chklovskii

Ensemble weighted kernel estimators for multivariate entropy estimation Kumar Sricharan, Alfred Hero

Active Comparison of Prediction Models Christoph Sawade, Niels Landwehr, Tobias Scheffer

Reducing statistical time-series problems to binary classification Daniil Ryabko, Jeremie Mary

Max-Margin Structured Output Regression for Spatio-Temporal Action Localization Du Tran, Junsong Yuan

Minimizing Sparse High-Order Energies by Submodular Vertex-Cover Andrew Delong, Olga Veksler, Anton Osokin, Yuri Boykov

A new metric on the manifold of kernel matrices with application to matrix geometric means Suvrit Sra

Wavelet based multi-scale shape features on arbitrary surfaces for cortical thickness discrimination Won Kim, Deepti Pachauri, Charles Hatt, Moo. Chung, Sterling Johnson, Vikas Singh

Cardinality Restricted Boltzmann Machines Kevin Swersky, Ilya Sutskever, Daniel Tarlow, Richard Zemel, Russ R. Salakhutdinov, Ryan P. Adams

Sparse Prediction with the $k$-Support Norm Andreas Argyriou, Rina Foygel, Nathan Srebro

A Marginalized Particle Gaussian Process Regression Yali Wang, Brahim Chaib-draa

Iterative ranking from pair-wise comparisons Sahand Negahban, Sewoong Oh, Devavrat Shah

Training sparse natural image models with a fast Gibbs sampler of an extended state space Lucas Theis, Jascha Sohl-dickstein, Matthias Bethge

Learning from Distributions via Support Measure Machines Krikamol Muandet, Kenji Fukumizu, Francesco Dinuzzo, Bernhard Schölkopf

Learning Multiple Tasks using Shared Hypotheses Koby Crammer, Yishay Mansour

Minimizing Uncertainty in Pipelines Nilesh Dalvi, Aditya Parameswaran, Vibhor Rastogi

An Integer Optimization Approach to Associative Classification Dimitris Bertsimas, Allison Chang, Cynthia Rudin

Algorithms for Learning Markov Field Policies Abdeslam Boularias, Jan Peters, Oliver Kroemer

Bayesian Probabilistic Co-Subspace Addition Lei Shi

Exploration in Model-based Reinforcement Learning by Empirically Estimating Learning Progress Manuel Lopes, Tobias Lang, Marc Toussaint, Pierre-yves Oudeyer

From Deformations to Parts: Motion-based Segmentation of 3D Objects Soumya Ghosh, Matthew Loper, Erik Sudderth, Michael Black

Bayesian models for Large-scale Hierarchical Classification Siddharth Gopal, Yiming Yang, Bing Bai, Alexandru Niculescu-mizil

Efficient Spike-Coding with Multiplicative Adaptation in a Spike Response Model Sander Bohte

Forging The Graphs: A Low Rank and Positive Semidefinite Graph Learning Approach Dijun Luo, Heng Huang, Feiping Nie, Chris Ding

Random Utility Theory for Social Choice Hossein Azari, David Parks, Lirong Xia

Tensor Decomposition for Fast Parsing with Latent-Variable PCFGs Michael Collins, Shay Cohen

Action-Model Based Multi-agent Plan Recognition Hankz Zhuo, Qiang Yang, Subbarao Kambhampati

Semiparametric Principal Component Analysis Fang Han, Han Liu

Multi-task Vector Field Learning Binbin Lin, Sen Yang, Chiyuan Zhang, Jieping Ye, Xiaofei He

Local Supervised Learning through Space Partitioning Joseph Wang, Venkatesh Saligrama

Dip-means: an incremental clustering method for estimating the number of clusters Argyris Kalogeratos, Aristidis Likas

Unsupervised Template Learning for Fine-Grained Object Recognition Shulin Yang, Liefeng Bo, Jue Wang, Linda Shapiro

A Nonparametric Conjugate Prior Distribution for the Maximizing Argument of a Noisy Function Pedro Ortega, Jordi Grau-moya, Tim Genewein, David Balduzzi, Daniel Braun

Efficient Reinforcement Learning for High Dimensional Linear Quadratic Systems Morteza Ibrahimi, Adel Javanmard, Benjamin Roy

A Conditional Multinomial Mixture Model for Superset Label Learning Liping Liu, Thomas Dietterich

Value Pursuit Iteration Amir Farahmand, Doina Precup

Latent Coincidence Analysis: A Hidden Variable Model for Distance Metric Learning Matthew Der, Lawrence Saul

Identification of Recurrent Patterns in the Activation of Brain Networks Firdaus Janoos, Weichang Li, Niranjan Subrahmanya, Istvan Morocz, William Wells

Hierarchical spike coding of sound Yan Karklin, Chaitanya Ekanadham, Eero Simoncelli

A Polynomial-time Form of Robust Regression Yao-liang Yu, Özlem Aslan, Dale Schuurmans

Multimodal Learning with Deep Boltzmann Machines Nitish Srivastava, Russ R. Salakhutdinov

A nonparametric variable clustering model Konstantina Palla, Zoubin Ghahramani, David Knowles

Transelliptical Graphical Models Han Liu, Fang Han, Cun-hui Zhang

Collaborative Gaussian Processes for Preference Learning Neil Houlsby, Ferenc Huszar, Zoubin Ghahramani, Jose Hernández-lobato

Smooth-projected Neighborhood Pursuit for High-dimensional Nonparanormal Graph Estimation Tuo Zhao, Kathryn Roeder, Han Liu

Learning Manifolds with K-Means and K-Flats Guillermo Canas, Tomaso Poggio, Lorenzo Rosasco

Newton-Like Methods for Sparse Inverse Covariance Estimation Figen Oztoprak, Jorge Nocedal, Steven Rennie, Peder A. Olsen

A Neural Autoregressive Topic Model Hugo Larochelle, Stanislas Lauly

Active Learning of Multi-Index Function Models Tyagi Hemant, Volkan Cevher

Probabilistic Low-Rank Subspace Clustering S. Babacan, Shinichi Nakajima, Minh Do

Fully Bayesian inference for neural models with negative-binomial spiking Jonathan Pillow, James Scott

Adaptive Learning of Smoothing Functions: Application to Electricity Load Forecasting Amadou Ba, Mathieu Sinn, Yannig Goude, Pascal Pompey

Complex Inference in Neural Circuits with Probabilistic Population Codes and Topic Models Jeff Beck, Alexandre Pouget, Katherine A. Heller

Clustering by Nonnegative Matrix Factorization Using Graph Random Walk Zhirong Yang, Tele Hao, Onur Dikmen, Xi Chen, Erkki Oja

Graphical Gaussian Vector for Image Categorization Tatsuya Harada, Yasuo Kuniyoshi

Convergence Rate Analysis of MAP Coordinate Minimization Algorithms Ofer Meshi, Amir Globerson, Tommi Jaakkola

Distributed Probabilistic Learning for Camera Networks with Missing Data Sejong Yoon, Vladimir Pavlovic

Confusion-Based Online Learning and a Passive-Aggressive Scheme Liva Ralaivola

Context-Sensitive Decision Forests for Object Detection Peter Kontschieder, Samuel Bulò, Antonio Criminisi, Pushmeet Kohli, Marcello Pelillo, Horst Bischof

Approximating Concavely Parameterized Optimization Problems Joachim Giesen, Jens Mueller, Soeren Laue, Sascha Swiercy

Kernel Latent SVM for Visual Recognition Weilong Yang, Yang Wang, Arash Vahdat, Greg Mori

A Linear Time Active Learning Algorithm for Link Classification Nicolò Cesa-bianchi, Claudio Gentile, Fabio Vitale, Giovanni Zappella

A P300 BCI for the Masses: Prior Information Enables Instant Unsupervised Spelling Pieter-jan Kindermans, Hannes Verschore, David Verstraeten, Benjamin Schrauwen

Dynamic Pruning of Factor Graphs for Maximum Marginal Prediction Christoph H. Lampert

Locally Uniform Comparison Image Descriptor Andrew Ziegler, Eric Christiansen, David Kriegman, Serge Belongie

Priors for Diversity in Generative Latent Variable Models James Kwok, Ryan P. Adams

Mixing Properties of Conditional Markov Chains with Unbounded Feature Functions Mathieu Sinn, Bei Chen

ImageNet Classification with Deep Convolutional Neural Networks Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton

Stochastic Gradient Descent with Only One Projection Mehrdad Mahdavi, Tianbao Yang, Rong Jin, Shenghuo Zhu, Jinfeng Yi

Learning Probability Measures with respect to Optimal Transport Metrics Guillermo Canas, Lorenzo Rosasco

Effective Split-Merge Monte Carlo Methods for Nonparametric Models of Sequential Data Michael C. Hughes, Emily Fox, Erik Sudderth

Scaling MPE Inference for Constrained Continuous Markov Random Fields with Consensus Optimization Stephen Bach, Matthias Broecheler, Lise Getoor, Dianne O'leary

A systematic approach to extracting semantic information from functional MRI data Francisco Pereira, Matthew Botvinick

Sketch-Based Linear Value Function Approximation Marc Bellemare, Joel Veness, Michael Bowling

Unsupervised Structure Discovery for Semantic Analysis of Audio Sourish Chaudhuri, Bhiksha Raj

The Time-Marginalized Coalescent Prior for Hierarchical Clustering Levi Boyles, Max Welling

Nonparametric Max-Margin Matrix Factorization for Collaborative Prediction Minjie Xu, Jun Zhu, Bo Zhang

Exponential Concentration for Mutual Information Estimation with Application to Forests Han Liu, Larry Wasserman, John Lafferty

Slice Normalized Dynamic Markov Logic Networks Tivadar Papai, Henry Kautz, Daniel Stefankovic

Continuous Relaxations for Discrete Hamiltonian Monte Carlo Yichuan Zhang, Zoubin Ghahramani, Amos J. Storkey, Charles Sutton

Learning with Target Prior Zuoguan Wang, Siwei Lyu, Gerwin Schalk, Qiang Ji

Generalization Bounds for Domain Adaptation Chao Zhang, Lei Zhang, Jieping Ye

Multiplicative Forests for Continuous-Time Processes Jeremy Weiss, Sriraam Natarajan, David Page

Variational Inference for Crowdsourcing Qiang Liu, Jian Peng, Alexander T. Ihler

Why MCA? Nonlinear sparse coding with spike-and-slab prior for neurally plausible image encoding Philip Sterne, Joerg Bornschein, Abdul-saboor Sheikh, Jörg Lücke, Jacquelyn Shelton

Tractable Objectives for Robust Policy Optimization Katherine Chen, Michael Bowling

Multiple Choice Learning: Learning to Produce Multiple Structured Outputs Abner Guzmán-rivera, Dhruv Batra, Pushmeet Kohli

Robustness and risk-sensitivity in Markov decision processes Takayuki Osogami

Communication/Computation Tradeoffs in Consensus-Based Distributed Optimization Konstantinos Tsianos, Sean Lawlor, Michael Rabbat

A Polylog Pivot Steps Simplex Algorithm for Classification Elad Hazan, Zohar Karnin

Fiedler Random Fields: A Large-Scale Spectral Approach to Statistical Network Modeling Antonino Freno, Mikaela Keller, Marc Tommasi

Majorization for CRFs and Latent Likelihoods Tony Jebara, Anna Choromanska

Feature-aware Label Space Dimension Reduction for Multi-label Classification Yao-nan Chen, Hsuan-tien Lin

Semantic Kernel Forests from Multiple Taxonomies Sung Hwang, Kristen Grauman, Fei Sha

Spectral learning of linear dynamics from generalised-linear observations with application to neural population data Lars Buesing, Jakob H. Macke, Maneesh Sahani

Assessing Blinding in Clinical Trials Ognjen Arandjelovic

Scalable Inference of Overlapping Communities Prem K. Gopalan, Sean Gerrish, Michael Freedman, David Blei, David Mimno

Learning Networks of Heterogeneous Influence Nan Du, Le Song, Ming Yuan, Alex Smola

Learning to Align from Scratch Gary Huang, Marwan Mattar, Honglak Lee, Erik Learned-miller

Bayesian Warped Gaussian Processes Miguel Lázaro-Gredilla

Affine Independent Variational Inference Edward Challis, David Barber

Submodular-Bregman and the Lovász-Bregman Divergences with Applications Rishabh Iyer, Jeff A. Bilmes

Optimal kernel choice for large-scale two-sample tests Arthur Gretton, Dino Sejdinovic, Heiko Strathmann, Sivaraman Balakrishnan, Massimiliano Pontil, Kenji Fukumizu, Bharath K. Sriperumbudur

Entangled Monte Carlo Seong-hwan Jun, Liangliang Wang, Alexandre Bouchard-côté

Minimax Multi-Task Learning and a Generalized Loss-Compositional Paradigm for MTL Nishant Mehta, Dongryeol Lee, Alexander Gray

Semi-Crowdsourced Clustering: Generalizing Crowd Labeling by Robust Distance Metric Learning Jinfeng Yi, Rong Jin, Shaili Jain, Tianbao Yang, Anil Jain

Online L1-Dictionary Learning with Application to Novel Document Detection Shiva Kasiviswanathan, Huahua Wang, Arindam Banerjee, Prem Melville

Learning curves for multi-task Gaussian process regression Peter Sollich, Simon Ashton

Transelliptical Component Analysis Fang Han, Han Liu

Learning the Dependency Structure of Latent Factors Yunlong He, Yanjun Qi, Koray Kavukcuoglu, Haesun Park

Random function priors for exchangeable arrays with applications to graphs and relational data James Lloyd, Peter Orbanz, Zoubin Ghahramani, Daniel M. Roy

Bayesian Pedigree Analysis using Measure Factorization Bonnie Kirkpatrick, Alexandre Bouchard-côté

Density Propagation and Improved Bounds on the Partition Function Stefano Ermon, Ashish Sabharwal, Bart Selman, Carla P. Gomes

A quasi-Newton proximal splitting method Stephen Becker, Jalal Fadili

Waveform Driven Plasticity in BiFeO3 Memristive Devices: Model and Implementation Christian Mayr, Paul Stärke, Johannes Partzsch, Love Cederstroem, Rene Schüffny, Yao Shuai, Nan Du, Heidemarie Schmidt

Multilabel Classification using Bayesian Compressed Sensing Ashish Kapoor, Raajay Viswanathan, Prateek Jain

Online Sum-Product Computation Over Trees Mark Herbster, Stephen Pasteris, Fabio Vitale

Risk Aversion in Markov Decision Processes via Near Optimal Chernoff Bounds Teodor Moldovan, Pieter Abbeel

Calibrated Elastic Regularization in Matrix Completion Tingni Sun, Cun-hui Zhang

Expectation Propagation in Gaussian Process Dynamical Systems Marc Deisenroth, Shakir Mohamed

Finite Sample Convergence Rates of Zero-Order Stochastic Optimization Methods Andre Wibisono, Martin J. Wainwright, Michael Jordan, John C. Duchi

Query Complexity of Derivative-Free Optimization Kevin G. Jamieson, Robert Nowak, Ben Recht

Communication-Efficient Algorithms for Statistical Optimization Yuchen Zhang, Martin J. Wainwright, John C. Duchi

Selecting Diverse Features via Spectral Regularization Abhimanyu Das, Anirban Dasgupta, Ravi Kumar

Fast Bayesian Inference for Non-Conjugate Gaussian Process Regression Emtiyaz Khan, Shakir Mohamed, Kevin P. Murphy

Natural Images, Gaussian Mixtures and Dead Leaves Daniel Zoran, Yair Weiss

Memorability of Image Regions Aditya Khosla, Jianxiong Xiao, Antonio Torralba, Aude Oliva

Projection Retrieval for Classification Madalina Fiterau, Artur Dubrawski

One Permutation Hashing Ping Li, Art Owen, Cun-hui Zhang

The representer theorem for Hilbert spaces: a necessary and sufficient condition Francesco Dinuzzo, Bernhard Schölkopf

A Geometric take on Metric Learning Søren Hauberg, Oren Freifeld, Michael Black

Iterative Thresholding Algorithm for Sparse Inverse Covariance Estimation Benjamin Rolfs, Bala Rajaratnam, Dominique Guillot, Ian Wong, Arian Maleki

Online allocation and homogeneous partitioning for piecewise constant mean-approximation Alexandra Carpentier, Odalric-ambrym Maillard

Efficient coding provides a direct link between prior and likelihood in perceptual Bayesian inference Xue-xin Wei, Alan A. Stocker

Pointwise Tracking the Optimal Regression Function Yair Wiener, Ran El-Yaniv

Globally Convergent Dual MAP LP Relaxation Solvers using Fenchel-Young Margins Alex Schwing, Tamir Hazan, Marc Pollefeys, Raquel Urtasun

Nonparametric Reduced Rank Regression Rina Foygel, Michael Horrell, Mathias Drton, John Lafferty

Density-Difference Estimation Masashi Sugiyama, Takafumi Kanamori, Taiji Suzuki, Marthinus Plessis, Song Liu, Ichiro Takeuchi

Learning the Architecture of Sum-Product Networks Using Clustering on Variables Aaron Dennis, Dan Ventura

Towards a learning-theoretic analysis of spike-timing dependent plasticity David Balduzzi, Michel Besserve

Angular Quantization-based Binary Codes for Fast Similarity Search Yunchao Gong, Sanjiv Kumar, Vishal Verma, Svetlana Lazebnik

Matrix reconstruction with the local max norm Rina Foygel, Nathan Srebro, Russ R. Salakhutdinov

Near-optimal Differentially Private Principal Components Kamalika Chaudhuri, Anand Sarwate, Kaushik Sinha

Mandatory Leaf Node Prediction in Hierarchical Multilabel Classification Wei Bi, James Kwok

Synchronization can Control Regularization in Neural Systems via Correlated Noise Processes Jake Bouvrie, Jean-jeacques Slotine

Online Regret Bounds for Undiscounted Continuous Reinforcement Learning Ronald Ortner, Daniil Ryabko

Perceptron Learning of SAT Alex Flint, Matthew Blaschko

On Lifting the Gibbs Sampling Algorithm Deepak Venugopal, Vibhav Gogate

Q-MKL: Matrix-induced Regularization in Multi-Kernel Learning with Applications to Neuroimaging Chris Hinrichs, Vikas Singh, Jiming Peng, Sterling Johnson

Label Ranking with Partial Abstention based on Thresholded Probabilistic Models Weiwei Cheng, Eyke Hüllermeier, Willem Waegeman, Volkmar Welker

Weighted Likelihood Policy Search with Model Selection Tsuyoshi Ueno, Kohei Hayashi, Takashi Washio, Yoshinobu Kawahara

Advances in Neural Information Processing Systems 25 (NIPS 2012)

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