Yang Cheng

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NIPS2013

  • (More) Efficient Reinforcement Learning via Posterior Sampling Ian Osband, Dan Russo, Benjamin Van Roy
  • (Nearly) Optimal Algorithms for Private Online Learning in Full-nformation and Bandit Settings Abhradeep Guha Thakurta, Adam Smith
  • A Comparative Framework for Preconditioned Lasso Algorithms Fabian L. Wauthier, Nebojsa Jojic, Michael Jordan
  • A Deep Architecture for Matching Short Texts Zhengdong Lu, Hang Li
  • A Determinantal Point Process Latent Variable Model for Inhibition in Neural Spiking Data Jasper Snoek, Richard Zemel, Ryan P. Adams
  • A Gang of Bandits Nicolò Cesa-ianchi, Claudio Gentile, Giovanni Zappella
  • A Graphical Transformation for Belief Propagation: Maximum Weight Matchings and Odd-ized Cycles Jinwoo Shin, Andrew E. Gelfand, Misha Chertkov
  • A Kernel Test for Three-ariable Interactions Dino Sejdinovic, Arthur Gretton, Wicher Bergsma
  • A Latent Source Model for Nonparametric Time Series Classification George H. Chen, Stanislav Nikolov, Devavrat Shah
  • A New Convex Relaxation for Tensor Completion Bernardino Romera-aredes, Massimiliano Pontil
  • A Novel Two-tep Method for Cross Language Representation Learning Min Xiao, Yuhong Guo
  • A Scalable Approach to Probabilistic Latent Space Inference of Large-cale Networks Junming Yin, Qirong Ho, Eric Xing
  • A Stability-ased Validation Procedure for Differentially Private Machine Learning Kamalika Chaudhuri, Staal A. Vinterbo
  • A memory frontier for complex synapses Subhaneil Lahiri, Surya Ganguli
  • A message-assing algorithm for multi-gent trajectory planning Jose Bento, Nate Derbinsky, Javier Alonso-ora, Jonathan S. Yedidia
  • A multi-gent control framework for co-daptation in brain-omputer interfaces Josh S. Merel, Roy Fox, Tony Jebara, Liam Paninski
  • A simple example of Dirichlet process mixture inconsistency for the number of components Jeffrey W. Miller, Matthew T. Harrison
  • A* Lasso for Learning a Sparse Bayesian Network Structure for Continuous Variables Jing Xiang, Seyoung Kim
  • Accelerated Mini-atch Stochastic Dual Coordinate Ascent Shai Shalev-hwartz, Tong Zhang
  • Accelerating Stochastic Gradient Descent using Predictive Variance Reduction Rie Johnson, Tong Zhang
  • Action from Still Image Dataset and Inverse Optimal Control to Learn Task Specific Visual Scanpaths Stefan Mathe, Cristian Sminchisescu
  • Action is in the Eye of the Beholder: Eye-aze Driven Model for Spatio-emporal Action Localization Nataliya Shapovalova, Michalis Raptis, Leonid Sigal, Greg Mori
  • 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
  • Actor-ritic Algorithms for Risk-ensitive MDPs Prashanth L.A., Mohammad Ghavamzadeh
  • Adaptive Anonymity via b-atching Krzysztof M. Choromanski, Tony Jebara, Kui Tang
  • Adaptive Market Making via Online Learning Jacob Abernethy, Satyen Kale
  • Adaptive Step-ize for Policy Gradient Methods Matteo Pirotta, Marcello Restelli, Luca Bascetta
  • Adaptive Submodular Maximization in Bandit Setting Victor Gabillon, Branislav Kveton, Zheng Wen, Brian Eriksson, S. Muthukrishnan
  • Adaptive dropout for training deep neural networks Jimmy Ba, Brendan Frey
  • Adaptivity to Local Smoothness and Dimension in Kernel Regression Samory Kpotufe, Vikas Garg
  • Aggregating Optimistic Planning Trees for Solving Markov Decision Processes Gunnar Kedenburg, Raphael Fonteneau, Remi Munos
  • An Approximate, Efficient LP Solver for LP Rounding Srikrishna Sridhar, Stephen Wright, Christopher Re, Ji Liu, Victor Bittorf, Ce Zhang
  • Analyzing Hogwild Parallel Gaussian Gibbs Sampling Matthew Johnson, James Saunderson, Alan Willsky
  • Analyzing the Harmonic Structure in Graph-ased Learning Xiao-ing Wu, Zhenguo Li, Shih-u Chang
  • Annealing between distributions by averaging moments Roger B. Grosse, Chris J. Maddison, Ruslan Salakhutdinov
  • Approximate Bayesian Image Interpretation using Generative Probabilistic Graphics Programs Vikash Mansinghka, Tejas D. Kulkarni, Yura N. Perov, Josh Tenenbaum
  • Approximate Dynamic Programming Finally Performs Well in the Game of Tetris Victor Gabillon, Mohammad Ghavamzadeh, Bruno Scherrer
  • Approximate Gaussian process inference for the drift function in stochastic differential equations Andreas Ruttor, Philipp Batz, Manfred Opper
  • Approximate Inference in Continuous Determinantal Processes Raja Hafiz Affandi, Emily Fox, Ben Taskar
  • Approximate inference in latent Gaussian-arkov models from continuous time observations Botond Cseke, Manfred Opper, Guido Sanguinetti
  • Auditing: Active Learning with Outcome-ependent Query Costs Sivan Sabato, Anand D. Sarwate, Nati Srebro
  • Auxiliary-ariable Exact Hamiltonian Monte Carlo Samplers for Binary Distributions Ari Pakman, Liam Paninski
  • B-est: A Non-arametric, Low Variance Kernel Two-ample Test Wojciech Zaremba, Arthur Gretton, Matthew Blaschko
  • BIG & QUIC: Sparse Inverse Covariance Estimation for a Million Variables Cho-ui Hsieh, Matyas A. Sustik, Inderjit Dhillon, Pradeep Ravikumar, Russell Poldrack
  • Bayesian Estimation of Latently-rouped Parameters in Undirected Graphical Models Jie Liu, David Page
  • Bayesian Hierarchical Community Discovery Charles Blundell, Yee Whye Teh
  • Bayesian Inference and Learning in Gaussian Process State-pace Models with Particle MCMC Roger Frigola, Fredrik Lindsten, Thomas B. Schon, Carl Rasmussen
  • Bayesian Inference and Online Experimental Design for Mapping Neural Microcircuits Ben Shababo, Brooks Paige, Ari Pakman, Liam Paninski
  • Bayesian Mixture Modelling and Inference based Thompson Sampling in Monte-arlo Tree Search Aijun Bai, Feng Wu, Xiaoping Chen
  • Bayesian entropy estimation for binary spike train data using parametric prior knowledge Evan W. Archer, Il M. Park, Jonathan Pillow
  • Bayesian inference as iterated random functions with applications to sequential inference in graphical models Arash Amini, Xuanlong Nguyen
  • Bayesian inference for low rank spatiotemporal neural receptive fields Mijung Park, Jonathan Pillow
  • Bayesian optimization explains human active search Ali Borji, Laurent Itti
  • Bellman Error Based Feature Generation using Random Projections on Sparse Spaces Mahdi Milani Fard, Yuri Grinberg, Amir massoud Farahmand, Joelle Pineau, Doina Precup
  • Better Approximation and Faster Algorithm Using the Proximal Average Yao-iang Yu
  • Beyond Pairwise: Provably Fast Algorithms for Approximate k-ay Similarity Search Anshumali Shrivastava, Ping Li
  • Binary to Bushy: Bayesian Hierarchical Clustering with the Beta Coalescent Yuening Hu, Jordan Boyd-raber, Hal Daume III, Z. Irene Ying
  • Blind Calibration in Compressed Sensing using Message Passing Algorithms Christophe Schulke, Francesco Caltagirone, Florent Krzakala, Lenka Zdeborova
  • Buy-n-ulk Active Learning Liu Yang, Jaime Carbonell
  • Capacity of strong attractor patterns to model behavioural and cognitive prototypes Abbas Edalat
  • Causal Inference on Time Series using Restricted Structural Equation Models Jonas Peters, Dominik Janzing, Bernhard Schölkopf
  • Cluster Trees on Manifolds Sivaraman Balakrishnan, Srivatsan Narayanan, Alessandro Rinaldo, Aarti Singh, Larry Wasserman
  • Compete to Compute Rupesh K. Srivastava, Jonathan Masci, Sohrob Kazerounian, Faustino Gomez, Jürgen Schmidhuber
  • Compressive Feature Learning Hristo S. Paskov, Robert West, John C. Mitchell, Trevor Hastie
  • Computing the Stationary Distribution Locally Christina E. Lee, Asuman Ozdaglar, Devavrat Shah
  • Conditional Random Fields via Univariate Exponential Families Eunho Yang, Pradeep Ravikumar, Genevera I. Allen, Zhandong Liu
  • Confidence Intervals and Hypothesis Testing for High-imensional Statistical Models Adel Javanmard, Andrea Montanari
  • Context-ensitive active sensing in humans Sheeraz Ahmad, He Huang, Angela J. Yu
  • Contrastive Learning Using Spectral Methods James Y. Zou, Daniel Hsu, David C. Parkes, Ryan P. Adams
  • Convergence of Monte Carlo Tree Search in Simultaneous Move Games Viliam Lisy, Vojta Kovarik, Marc Lanctot, Branislav Bosansky
  • Convex Calibrated Surrogates for Low-ank Loss Matrices with Applications to Subset Ranking Losses Harish G. Ramaswamy, Shivani Agarwal, Ambuj Tewari
  • Convex Relaxations for Permutation Problems Fajwel Fogel, Rodolphe Jenatton, Francis Bach, Alexandre D’Aspremont
  • Convex Tensor Decomposition via Structured Schatten Norm Regularization Ryota Tomioka, Taiji Suzuki
  • Convex Two-ayer Modeling Özlem Aslan, Hao Cheng, Xinhua Zhang, Dale Schuurmans
  • Correlated random features for fast semi-upervised learning Brian McWilliams, David Balduzzi, Joachim Buhmann
  • Correlations strike back (again): the case of associative memory retrieval Cristina Savin, Peter Dayan, Mate Lengyel
  • Curvature and Optimal Algorithms for Learning and Minimizing Submodular Functions Rishabh K. Iyer, Stefanie Jegelka, Jeff A. Bilmes
  • DESPOT: Online POMDP Planning with Regularization Adhiraj Somani, Nan Ye, David Hsu, Wee Sun Lee
  • Data-riven Distributionally Robust Polynomial Optimization Martin Mevissen, Emanuele Ragnoli, Jia Yuan Yu
  • DeViSE: A Deep Visual-emantic Embedding Model Andrea Frome, Greg S. Corrado, Jon Shlens, Samy Bengio, Jeff Dean, Marc’Aurelio Ranzato, Tomas Mikolov
  • Decision Jungles: Compact and Rich Models for Classification Jamie Shotton, Toby Sharp, Pushmeet Kohli, Sebastian Nowozin, John Winn, Antonio Criminisi
  • Deep Fisher Networks for Large-cale Image Classification Karen Simonyan, Andrea Vedaldi, Andrew Zisserman
  • Deep Neural Networks for Object Detection Christian Szegedy, Alexander Toshev, Dumitru Erhan
  • Deep content-ased music recommendation Aaron van den Oord, Sander Dieleman, Benjamin Schrauwen
  • Demixing odors -fast inference in olfaction Agnieszka Grabska-arwinska, Jeff Beck, Alexandre Pouget, Peter Latham
  • Density estimation from unweighted k-earest neighbor graphs: a roadmap Ulrike Von Luxburg, Morteza Alamgir
  • Designed Measurements for Vector Count Data Liming Wang, David Carlson, Miguel Rodrigues, David Wilcox, Robert Calderbank, Lawrence Carin
  • Dimension-ree Exponentiated Gradient Francesco Orabona
  • Direct 0* Loss Minimization and Margin Maximization with Boosting Shaodan Zhai, Tian Xia, Ming Tan, Shaojun Wang
  • Dirty Statistical Models Eunho Yang, Pradeep Ravikumar
  • Discovering Hidden Variables in Noisy-r Networks using Quartet Tests Yacine Jernite, Yonatan Halpern, David Sontag
  • Discriminative Transfer Learning with Tree-ased Priors Nitish Srivastava, Ruslan Salakhutdinov
  • Distributed Exploration in Multi-rmed Bandits Eshcar Hillel, Zohar S. Karnin, Tomer Koren, Ronny Lempel, Oren Somekh
  • Distributed Representations of Words and Phrases and their Compositionality Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S. Corrado, Jeff Dean
  • Distributed Submodular Maximization: Identifying Representative Elements in Massive Data Baharan Mirzasoleiman, Amin Karbasi, Rik Sarkar, Andreas Krause
  • Distributed k-eans and k-edian clustering on general communication topologies Maria-lorina Balcan, Steven Ehrlich, Yingyu Liang
  • Documents as multiple overlapping windows into grids of counts Alessandro Perina, Nebojsa Jojic, Manuele Bicego, Andrzej Truski
  • Dropout Training as Adaptive Regularization Stefan Wager, Sida Wang, Percy Liang
  • Dynamic Clustering via Asymptotics of the Dependent Dirichlet Process Mixture Trevor Campbell, Miao Liu, Brian Kulis, Jonathan P. How, Lawrence Carin
  • EDML for Learning Parameters in Directed and Undirected Graphical Models Khaled Refaat, Arthur Choi, Adnan Darwiche
  • Efficient Algorithm for Privately Releasing Smooth Queries Ziteng Wang, Kai Fan, Jiaqi Zhang, Liwei Wang
  • Efficient Exploration and Value Function Generalization in Deterministic Systems Zheng Wen, Benjamin Van Roy
  • Efficient Online Inference for Bayesian Nonparametric Relational Models Dae Il Kim, Prem Gopalan, David Blei, Erik Sudderth
  • Efficient Optimization for Sparse Gaussian Process Regression Yanshuai Cao, Marcus A. Brubaker, David Fleet, Aaron Hertzmann
  • Eluder Dimension and the Sample Complexity of Optimistic Exploration Dan Russo, Benjamin Van Roy
  • Embed and Project: Discrete Sampling with Universal Hashing Stefano Ermon, Carla P. Gomes, Ashish Sabharwal, Bart Selman
  • Error-inimizing Estimates and Universal Entry-ise Error Bounds for Low-ank Matrix Completion Franz Kiraly, Louis Theran
  • Estimating LASSO Risk and Noise Level Mohsen Bayati, Murat A. Erdogdu, Andrea Montanari
  • Estimating the Unseen: Improved Estimators for Entropy and other Properties Paul Valiant, Gregory Valiant
  • Estimation Bias in Multi-rmed Bandit Algorithms for Search Advertising Min Xu, Tao Qin, Tie-an Liu
  • Estimation, Optimization, and Parallelism when Data is Sparse John Duchi, Michael Jordan, Brendan McMahan
  • Exact and Stable Recovery of Pairwise Interaction Tensors Shouyuan Chen, Michael R. Lyu, Irwin King, Zenglin Xu
  • 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
  • Factorized Asymptotic Bayesian Inference for Latent Feature Models Kohei Hayashi, Ryohei Fujimaki
  • Fantope Projection and Selection: A near-ptimal convex relaxation of sparse PCA Vincent Q. Vu, Juhee Cho, Jing Lei, Karl Rohe
  • Fast Algorithms for Gaussian Noise Invariant Independent Component Analysis James R. Voss, Luis Rademacher, Mikhail Belkin
  • Fast Determinantal Point Process Sampling with Application to Clustering Byungkon Kang
  • Fast Template Evaluation with Vector Quantization Mohammad Amin Sadeghi, David Forsyth
  • Faster Ridge Regression via the Subsampled Randomized Hadamard Transform Yichao Lu, Paramveer Dhillon, Dean P. Foster, Lyle Ungar
  • Firing rate predictions in optimal balanced networks David G. Barrett, Sophie Denève, Christian K. Machens
  • First-rder Decomposition Trees Nima Taghipour, Jesse Davis, Hendrik Blockeel
  • Flexible sampling of discrete data correlations without the marginal distributions Alfredo Kalaitzis, Ricardo Silva
  • Forgetful Bayes and myopic planning: Human learning and decision-aking in a bandit setting Shunan Zhang, Angela J. Yu
  • From Bandits to Experts: A Tale of Domination and Independence Noga Alon, Nicolò Cesa-ianchi, Claudio Gentile, Yishay Mansour
  • Gaussian Process Conditional Copulas with Applications to Financial Time Series José Miguel Hernández-obato, James R. Lloyd, Daniel Hernández-obato
  • Generalized Denoising Auto-ncoders as Generative Models Yoshua Bengio, Li Yao, Guillaume Alain, Pascal Vincent
  • Generalized Method-f-oments for Rank Aggregation Hossein Azari Soufiani, William Chen, David C. Parkes, Lirong Xia
  • Generalized Random Utility Models with Multiple Types Hossein Azari Soufiani, Hansheng Diao, Zhenyu Lai, David C. Parkes
  • Generalizing Analytic Shrinkage for Arbitrary Covariance Structures Daniel Bartz, Klaus-obert Müller
  • Geometric optimisation on positive definite matrices for elliptically contoured distributions Suvrit Sra, Reshad Hosseini
  • Global MAP-ptimality by Shrinking the Combinatorial Search Area with Convex Relaxation Bogdan Savchynskyy, Jörg Hendrik Kappes, Paul Swoboda, Christoph Schnörr
  • Global Solver and Its Efficient Approximation for Variational Bayesian Low-ank Subspace Clustering Shinichi Nakajima, Akiko Takeda, S. Derin Babacan, Masashi Sugiyama, Ichiro Takeuchi
  • Graphical Models for Inference with Missing Data Karthika Mohan, Judea Pearl, Jin Tian
  • Heterogeneous-eighborhood-ased Multi-ask Local Learning Algorithms Yu Zhang
  • 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
  • High-imensional Gaussian Process Bandits Josip Djolonga, Andreas Krause, Volkan Cevher
  • Higher Order Priors for Joint Intrinsic Image, Objects, and Attributes Estimation Vibhav Vineet, Carsten Rother, Philip Torr
  • How to Hedge an Option Against an Adversary: Black-choles Pricing is Minimax Optimal Jacob Abernethy, Peter Bartlett, Rafael Frongillo, Andre Wibisono
  • Improved and Generalized Upper Bounds on the Complexity of Policy Iteration Bruno Scherrer
  • 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 Macke
  • Information-heoretic lower bounds for distributed statistical estimation with communication constraints Yuchen Zhang, John Duchi, Michael Jordan, Martin J. Wainwright
  • Integrated Non-actorized Variational Inference Shaobo Han, Xuejun Liao, Lawrence Carin
  • Inverse Density as an Inverse Problem: the Fredholm Equation Approach Qichao Que, Mikhail Belkin
  • It is all in the noise: Efficient multi-ask Gaussian process inference with structured residuals Barbara Rakitsch, Christoph Lippert, Karsten Borgwardt, Oliver Stegle
  • Large Scale Distributed Sparse Precision Estimation Huahua Wang, Arindam Banerjee, Cho-ui Hsieh, Pradeep Ravikumar, Inderjit Dhillon
  • Lasso Screening Rules via Dual Polytope Projection Jie Wang, Jiayu Zhou, Peter Wonka, Jieping Ye
  • Latent Maximum Margin Clustering Guang-ong Zhou, Tian Lan, Arash Vahdat, Greg Mori
  • Latent Structured Active Learning Wenjie Luo, Alex Schwing, Raquel Urtasun
  • Learning Adaptive Value of Information for Structured Prediction David J. Weiss, Ben Taskar
  • Learning Chordal Markov Networks by Constraint Satisfaction Jukka Corander, Tomi Janhunen, Jussi Rintanen, Henrik Nyman, Johan Pensar
  • Learning Efficient Random Maximum A-osteriori Predictors with Non-ecomposable Loss Functions Tamir Hazan, Subhransu Maji, Joseph Keshet, Tommi Jaakkola
  • Learning Feature Selection Dependencies in Multi-ask Learning Daniel Hernández-obato, José Miguel Hernández-obato
  • Learning Gaussian Graphical Models with Observed or Latent FVSs Ying Liu, Alan Willsky
  • Learning Hidden Markov Models from Non-equence Data via Tensor Decomposition Tzu-uo Huang, Jeff Schneider
  • Learning Kernels Using Local Rademacher Complexity Corinna Cortes, Marius Kloft, Mehryar Mohri
  • Learning Multi-evel Sparse Representations Ferran Diego Andilla, Fred A. Hamprecht
  • Learning Multiple Models via Regularized Weighting Daniel Vainsencher, Shie Mannor, Huan Xu
  • Learning Prices for Repeated Auctions with Strategic Buyers Kareem Amin, Afshin Rostamizadeh, Umar Syed
  • Learning Stochastic Feedforward Neural Networks Yichuan Tang, Ruslan Salakhutdinov
  • Learning Stochastic Inverses Andreas Stuhlmüller, Jacob Taylor, Noah Goodman
  • Learning Trajectory Preferences for Manipulators via Iterative Improvement Ashesh Jain, Brian Wojcik, Thorsten Joachims, Ashutosh Saxena
  • Learning a Deep Compact Image Representation for Visual Tracking Naiyan Wang, Dit-an Yeung
  • Learning and using language via recursive pragmatic reasoning about other agents Nathaniel J. Smith, Noah Goodman, Michael Frank
  • Learning from Limited Demonstrations Beomjoon Kim, Amir massoud Farahmand, Joelle Pineau, Doina Precup
  • Learning invariant representations and applications to face verification Qianli Liao, Joel Z. Leibo, Tomaso Poggio
  • Learning the Local Statistics of Optical Flow Dan Rosenbaum, Daniel Zoran, Yair Weiss
  • Learning to Pass Expectation Propagation Messages Nicolas Heess, Daniel Tarlow, John Winn
  • Learning to Prune in Metric and Non-etric Spaces Leonid Boytsov, Bilegsaikhan Naidan
  • Learning with Invariance via Linear Functionals on Reproducing Kernel Hilbert Space Xinhua Zhang, Wee Sun Lee, Yee Whye Teh
  • Learning with Noisy Labels Nagarajan Natarajan, Inderjit Dhillon, Pradeep Ravikumar, Ambuj Tewari
  • Learning word embeddings efficiently with noise-ontrastive estimation Andriy Mnih, Koray Kavukcuoglu
  • Least Informative Dimensions Fabian Sinz, Anna Stockl, January Grewe, January Benda
  • Lexical and Hierarchical Topic Regression Viet-n Nguyen, Jordan Boyd-raber, Philip Resnik
  • Linear Convergence with Condition Number Independent Access of Full Gradients Lijun Zhang, Mehrdad Mahdavi, Rong Jin
  • Linear decision rule as aspiration for simple decision heuristics Ozgur Simsek
  • Local Privacy and Minimax Bounds: Sharp Rates for Probability Estimation John Duchi, Martin J. Wainwright, Michael Jordan
  • Locally Adaptive Bayesian Multivariate Time Series Daniele Durante, Bruno Scarpa, David Dunson
  • Low-ank Matrix and Tensor Completion via Adaptive Sampling Akshay Krishnamurthy, Aarti Singh
  • Low-ank matrix reconstruction and clustering via approximate message passing Ryosuke Matsushita, Toshiyuki Tanaka
  • Machine Teaching for Bayesian Learners in the Exponential Family Xiaojin Zhu
  • Manifold-ased Similarity Adaptation for Label Propagation Masayuki Karasuyama, Hiroshi Mamitsuka
  • Mapping paradigm ontologies to and from the brain Yannick Schwartz, Bertrand Thirion, Gael Varoquaux
  • Marginals-o-odels Reducibility Tim Roughgarden, Michael Kearns
  • Matrix Completion From any Given Set of Observations Troy Lee, Adi Shraibman
  • Matrix factorization with binary components Martin Slawski, Matthias Hein, Pavlo Lutsik
  • Memoized Online Variational Inference for Dirichlet Process Mixture Models Michael Hughes, Erik Sudderth
  • Memory Limited, Streaming PCA Ioannis Mitliagkas, Constantine Caramanis, Prateek Jain
  • Message Passing Inference with Chemical Reaction Networks Nils E. Napp, Ryan P. Adams
  • Mid-evel Visual Element Discovery as Discriminative Mode Seeking Carl Doersch, Abhinav Gupta, Alexei A. Efros
  • Minimax Optimal Algorithms for Unconstrained Linear Optimization Brendan McMahan, Jacob Abernethy
  • Minimax Theory for High-imensional Gaussian Mixtures with Sparse Mean Separation Martin Azizyan, Aarti Singh, Larry Wasserman
  • Mixed Optimization for Smooth Functions Mehrdad Mahdavi, Lijun Zhang, Rong Jin
  • Model Selection for High-imensional Regression under the Generalized Irrepresentability Condition Adel Javanmard, Andrea Montanari
  • Modeling Clutter Perception using Parametric Proto-bject Partitioning Chen-ing Yu, Wen-u Hua, Dimitris Samaras, Greg Zelinsky
  • Modeling Overlapping Communities with Node Popularities Prem Gopalan, Chong Wang, David Blei
  • Moment-ased Uniform Deviation Bounds for k-eans and Friends Matus Telgarsky, Sanjoy Dasgupta
  • 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 Xing
  • More data speeds up training time in learning halfspaces over sparse vectors Amit Daniely, Nati Linial, Shai Shalev-hwartz
  • Multi-rediction Deep Boltzmann Machines Ian Goodfellow, Mehdi Mirza, Aaron Courville, Yoshua Bengio
  • Multi-ask Bayesian Optimization Kevin Swersky, Jasper Snoek, Ryan P. Adams
  • Multiclass Total Variation Clustering Xavier Bresson, Thomas Laurent, David Uminsky, James von Brecht
  • Multilinear Dynamical Systems for Tensor Time Series Mark Rogers, Lei Li, Stuart Russell
  • Multiscale Dictionary Learning for Estimating Conditional Distributions Francesca Petralia, Joshua T. Vogelstein, David Dunson
  • Multisensory Encoding, Decoding, and Identification Aurel A. Lazar, Yevgeniy Slutskiy
  • Near-ptimal Entrywise Sampling for Data Matrices Dimitris Achlioptas, Zohar S. Karnin, Edo Liberty
  • Near-ptimal Anomaly Detection in Graphs using Lovasz Extended Scan Statistic James L. Sharpnack, Akshay Krishnamurthy, Aarti Singh
  • Neural representation of action sequences: how far can a simple snippet-atching model take us? Cheston Tan, Jedediah M. Singer, Thomas Serre, David Sheinberg, Tomaso Poggio
  • New Subsampling Algorithms for Fast Least Squares Regression Paramveer Dhillon, Yichao Lu, Dean P. Foster, Lyle Ungar
  • Noise-nhanced Associative Memories Amin Karbasi, Amir Hesam Salavati, Amin Shokrollahi, Lav R. Varshney
  • Non-inear Domain Adaptation with Boosting Carlos J. Becker, Christos M. Christoudias, Pascal Fua
  • Non-niform Camera Shake Removal Using a Spatially-daptive Sparse Penalty Haichao Zhang, David Wipf
  • Non-trongly-onvex smooth stochastic approximation with convergence rate O(1/n) Francis Bach, Eric Moulines
  • Nonparametric Multi-roup Membership Model for Dynamic Networks Myunghwan Kim, Jure Leskovec
  • On Algorithms for Sparse Multi-actor NMF Siwei Lyu, Xin Wang
  • On Decomposing the Proximal Map Yao-iang Yu
  • On Flat versus Hierarchical Classification in Large-cale Taxonomies Rohit Babbar, Ioannis Partalas, Eric Gaussier, Massih-eza Amini
  • On Poisson Graphical Models Eunho Yang, Pradeep Ravikumar, Genevera I. Allen, Zhandong Liu
  • On Sampling from the Gibbs Distribution with Random Maximum A-osteriori Perturbations Tamir Hazan, Subhransu Maji, Tommi Jaakkola
  • On model selection consistency of penalized M-stimators: a geometric theory Jason Lee, Yuekai Sun, Jonathan E. Taylor
  • On the Complexity and Approximation of Binary Evidence in Lifted Inference Guy van den Broeck, Adnan Darwiche
  • On the Expressive Power of Restricted Boltzmann Machines James Martens, Arkadev Chattopadhya, Toni Pitassi, Richard Zemel
  • On the Linear Convergence of the Proximal Gradient Method for Trace Norm Regularization Ke Hou, Zirui Zhou, Anthony Man-ho So, Zhi-uan Luo
  • On the Relationship Between Binary Classification, Bipartite Ranking, and Binary Class Probability Estimation Harikrishna Narasimhan, Shivani Agarwal
  • On the Sample Complexity of Subspace Learning Alessandro Rudi, Guillermo D. Canas, Lorenzo Rosasco
  • One-hot learning and big data with n=2 Lee H. Dicker, Dean P. Foster
  • One-hot learning by inverting a compositional causal process Brenden M. Lake, Ruslan Salakhutdinov, Josh Tenenbaum
  • Online Learning in Markov Decision Processes with Adversarially Chosen Transition Probability Distributions Yasin Abbasi, Peter Bartlett, Varun Kanade, Yevgeny Seldin, Csaba Szepesvari
  • Online Learning of Dynamic Parameters in Social Networks Shahin Shahrampour, Sasha Rakhlin, Ali Jadbabaie
  • Online Learning of Nonparametric Mixture Models via Sequential Variational Approximation Dahua Lin
  • Online Learning with Costly Features and Labels Navid Zolghadr, Gabor Bartok, Russell Greiner, András György, Csaba Szepesvari
  • Online Learning with Switching Costs and Other Adaptive Adversaries Nicolò Cesa-ianchi, Ofer Dekel, Ohad Shamir
  • Online PCA for Contaminated Data Jiashi Feng, Huan Xu, Shie Mannor, Shuicheng Yan
  • Online Robust PCA via Stochastic Optimization Jiashi Feng, Huan Xu, Shuicheng Yan
  • Online Variational Approximations to non-xponential Family Change Point Models: With Application to Radar Tracking Ryan D. Turner, Steven Bottone, Clay J. Stanek
  • Online learning in episodic Markovian decision processes by relative entropy policy search Alexander Zimin, Gergely Neu
  • Optimal Neural Population Codes for High-imensional Stimulus Variables Zhuo Wang, Alan Stocker, Daniel Lee
  • Optimal integration of visual speed across different spatiotemporal frequency channels Matjaz Jogan, Alan Stocker
  • Optimistic Concurrency Control for Distributed Unsupervised Learning Xinghao Pan, Joseph E. Gonzalez, Stefanie Jegelka, Tamara Broderick, Michael Jordan
  • Optimistic policy iteration and natural actor-ritic: A unifying view and a non-ptimality result Paul Wagner
  • Optimization, Learning, and Games with Predictable Sequences Sasha Rakhlin, Karthik Sridharan
  • Optimizing Instructional Policies Robert Lindsey, Michael Mozer, William J. Huggins, Harold Pashler
  • PAC-ayes-mpirical-ernstein Inequality Ilya O. Tolstikhin, Yevgeny Seldin
  • Parallel Sampling of DP Mixture Models using Sub-luster Splits Jason Chang, John W. Fisher III
  • Parametric Task Learning Ichiro Takeuchi, Tatsuya Hongo, Masashi Sugiyama, Shinichi Nakajima
  • Pass-fficient unsupervised feature selection Crystal Maung, Haim Schweitzer
  • Perfect Associative Learning with Spike-iming-ependent Plasticity Christian Albers, Maren Westkott, Klaus Pawelzik
  • Phase Retrieval using Alternating Minimization Praneeth Netrapalli, Prateek Jain, Sujay Sanghavi
  • Point Based Value Iteration with Optimal Belief Compression for Dec-OMDPs Liam C. MacDermed, Charles Isbell
  • Polar Operators for Structured Sparse Estimation Xinhua Zhang, Yao-iang Yu, Dale Schuurmans
  • Policy Shaping: Integrating Human Feedback with Reinforcement Learning Shane Griffith, Kaushik Subramanian, Jonathan Scholz, Charles Isbell, Andrea L. Thomaz
  • Predicting Parameters in Deep Learning Misha Denil, Babak Shakibi, Laurent Dinh, Marc’Aurelio Ranzato, Nando de Freitas
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  • Probabilistic Movement Primitives Alexandros Paraschos, Christian Daniel, January Peters, Gerhard Neumann
  • Probabilistic Principal Geodesic Analysis Miaomiao Zhang, P.T. Fletcher
  • Projected Natural Actor-ritic Philip S. Thomas, William C. Dabney, Stephen Giguere, Sridhar Mahadevan
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  • Real-ime Inference for a Gamma Process Model of Neural Spiking David Carlson, Vinayak Rao, Joshua T. Vogelstein, Lawrence Carin
  • Reasoning With Neural Tensor Networks for Knowledge Base Completion Richard Socher, Danqi Chen, Christopher D. Manning, Andrew Ng
  • Reciprocally Coupled Local Estimators Implement Bayesian Information Integration Distributively Wenhao Zhang, Si Wu
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  • Recurrent linear models of simultaneously-ecorded neural populations Marius Pachitariu, Biljana Petreska, Maneesh Sahani
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  • Reflection methods for user-riendly submodular optimization Stefanie Jegelka, Francis Bach, Suvrit Sra
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  • Regret based Robust Solutions for Uncertain Markov Decision Processes Asrar Ahmed, Pradeep Varakantham, Yossiri Adulyasak, Patrick Jaillet
  • Regularized M-stimators with nonconvexity: Statistical and algorithmic theory for local optima Po-ing Loh, Martin J. Wainwright
  • Regularized Spectral Clustering under the Degree-orrected Stochastic Blockmodel Tai Qin, Karl Rohe
  • Reinforcement Learning in Robust Markov Decision Processes Shiau Hong Lim, Huan Xu, Shie Mannor
  • Relevance Topic Model for Unstructured Social Group Activity Recognition Fang Zhao, Yongzhen Huang, Liang Wang, Tieniu Tan
  • Reservoir Boosting : Between Online and Offline Ensemble Learning Leonidas Lefakis, François Fleuret
  • Reshaping Visual Datasets for Domain Adaptation Boqing Gong, Kristen Grauman, Fei Sha
  • Restricting exchangeable nonparametric distributions Sinead A. Williamson, Steve N. MacEachern, Eric Xing
  • Reward Mapping for Transfer in Long-ived Agents Xiaoxiao Guo, Satinder Singh, Richard L. Lewis
  • Robust Bloom Filters for Large MultiLabel Classification Tasks Moustapha M. Cisse, Nicolas Usunier, Thierry Artières, Patrick Gallinari
  • Robust Data-riven Dynamic Programming Grani Adiwena Hanasusanto, Daniel Kuhn
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  • Robust Multimodal Graph Matching: Sparse Coding Meets Graph Matching Marcelo Fiori, Pablo Sprechmann, Joshua T. Vogelstein, Pablo Muse, Guillermo Sapiro
  • Robust Sparse Principal Component Regression under the High Dimensional Elliptical Model Fang Han, Han Liu
  • Robust Spatial Filtering with Beta Divergence Wojciech Samek, Duncan Blythe, Klaus-obert Müller, Motoaki Kawanabe
  • Robust Transfer Principal Component Analysis with Rank Constraints Yuhong Guo
  • Robust learning of low-imensional dynamics from large neural ensembles David Pfau, Eftychios A. Pnevmatikakis, Liam Paninski
  • Scalable Inference for Logistic-ormal Topic Models Jianfei Chen, June Zhu, Zi Wang, Xun Zheng, Bo Zhang
  • Scalable Influence Estimation in Continuous-ime Diffusion Networks Nan Du, Le Song, Manuel Gomez-odriguez, Hongyuan Zha
  • Scalable kernels for graphs with continuous attributes Aasa Feragen, Niklas Kasenburg, Jens Petersen, Marleen de Bruijne, Karsten Borgwardt
  • Scoring Workers in Crowdsourcing: How Many Control Questions are Enough? Qiang Liu, Alex Ihler, Mark Steyvers
  • Sensor Selection in High-imensional Gaussian Trees with Nuisances Daniel S. Levine, Jonathan P. How
  • Sequential Transfer in Multi-rmed Bandit with Finite Set of Models Mohammad Gheshlaghi azar, Alessandro Lazaric, Emma Brunskill
  • Sign Cauchy Projections and Chi-quare Kernel Ping Li, Gennady Samorodnitsk, John Hopcroft
  • Similarity Component Analysis Soravit Changpinyo, Kuan Liu, Fei Sha
  • Simultaneous Rectification and Alignment via Robust Recovery of Low-ank Tensors Xiaoqin Zhang, Di Wang, Zhengyuan Zhou, Yi Ma
  • Sinkhorn Distances: Lightspeed Computation of Optimal Transportation Marco Cuturi
  • Sketching Structured Matrices for Faster Nonlinear Regression Haim Avron, Vikas Sindhwani, David Woodruff
  • Small-ariance Asymptotics for Hidden Markov Models Anirban Roychowdhury, Ke Jiang, Brian Kulis
  • Solving inverse problem of Markov chain with partial observations Tetsuro Morimura, Takayuki Osogami, Tsuyoshi Ide
  • Solving the multi-ay matching problem by permutation synchronization Deepti Pachauri, Risi Kondor, Vikas Singh
  • Sparse Additive Text Models with Low Rank Background Lei Shi
  • Sparse Inverse Covariance Estimation with Calibration Tuo Zhao, Han Liu
  • Sparse Overlapping Sets Lasso for Multitask Learning and its Application to fMRI Analysis Nikhil Rao, Christopher Cox, Rob Nowak, Timothy T. Rogers
  • Sparse nonnegative deconvolution for compressive calcium imaging: algorithms and phase transitions Eftychios A. Pnevmatikakis, Liam Paninski
  • Spectral methods for neural characterization using generalized quadratic models Il M. Park, Evan W. Archer, Nicholas Priebe, Jonathan Pillow
  • Speeding up Permutation Testing in Neuroimaging Chris Hinrichs, Vamsi Ithapu, Qinyuan Sun, Sterling C. Johnson, Vikas Singh
  • Speedup Matrix Completion with Side Information: Application to Multi-abel Learning Miao Xu, Rong Jin, Zhi-ua Zhou
  • Spike train entropy-ate estimation using hierarchical Dirichlet process priors Karin C. Knudson, Jonathan Pillow
  • Statistical Active Learning Algorithms Maria-lorina Balcan, Vitaly Feldman
  • Statistical analysis of coupled time series with Kernel Cross-pectral Density operators Michel Besserve, Nikos K. Logothetis, Bernhard Schölkopf
  • Stochastic Convex Optimization with Multiple Objectives Mehrdad Mahdavi, Tianbao Yang, Rong Jin
  • Stochastic Gradient Riemannian Langevin Dynamics on the Probability Simplex Sam Patterson, Yee Whye Teh
  • Stochastic Majorization-inimization Algorithms for Large-cale Optimization Julien Mairal
  • Stochastic Optimization of PCA with Capped MSG Raman Arora, Andy Cotter, Nati Srebro
  • Stochastic Ratio Matching of RBMs for Sparse High-imensional Inputs Yann Dauphin, Yoshua Bengio
  • Stochastic blockmodel approximation of a graphon: Theory and consistent estimation Edoardo M. Airoldi, Thiago B. Costa, Stanley H. Chan
  • Streaming Variational Bayes Tamara Broderick, Nicholas Boyd, Andre Wibisono, Ashia C. Wilson, Michael Jordan
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  • Submodular Optimization with Submodular Cover and Submodular Knapsack Constraints Rishabh K. Iyer, Jeff A. Bilmes
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