Advances in Neural Information Processing Systems 26

NIPS 2013

Edited by C.J. Burges and L. Bottou and M. Welling and Z. Ghahramani and K.Q. Weinberger

ISBN: 9781632660244

360 papers

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 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 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 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 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 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 B. 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, Ruslan 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 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 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 Ill 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, Alex 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, Ruslan 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 Lindsey, Michael 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 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, Ruslan Salakhutdinov
Machine Teaching for Bayesian Learners in the Exponential Family Xiaojin 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 Ill 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 Edoardo 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, Alex 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, Ruslan 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 Ozgur Simsek
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 Huhn
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 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, Ill 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, Ruslan 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

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