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 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
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
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
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
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
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
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
Predictive PAC Learning and Process Decompositions Cosma Shalizi, Aryeh Kontorovitch
Prior-ree and prior-ependent regret bounds for Thompson Sampling Sebastien Bubeck, Che-u Liu
Probabilistic Low-ank Matrix Completion with Adaptive Spectral Regularization Algorithms Adrien Todeschini, François Caron, Marie Chavent
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
Projecting Ising Model Parameters for Fast Mixing Justin Domke, Xianghang Liu
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
Structured Learning via Logistic Regression Justin Domke
Submodular Optimization with Submodular Cover and Submodular Knapsack Constraints Rishabh K. Iyer, Jeff A. Bilmes
Summary Statistics for Partitionings and Feature Allocations Isik B. Fidaner, Taylan Cemgil
Supervised Sparse Analysis and Synthesis Operators Pablo Sprechmann, Roee Litman, Tal Ben Yakar, Alexander M. Bronstein, Guillermo Sapiro
Symbolic Opportunistic Policy Iteration for Factored-ction MDPs Aswin Raghavan, Roni Khardon, Alan Fern, Prasad Tadepalli
Synthesizing Robust Plans under Incomplete Domain Models Tuan A. Nguyen, Subbarao Kambhampati, Minh Do
The Fast Convergence of Incremental PCA Akshay Balsubramani, Sanjoy Dasgupta, Yoav Freund
The Pareto Regret Frontier Wouter M. Koolen
The Power of Asymmetry in Binary Hashing Behnam Neyshabur, Nati Srebro, Ruslan Salakhutdinov, Yury Makarychev, Payman Yadollahpour
The Randomized Dependence Coefficient David Lopez-az, Philipp Hennig, Bernhard Schölkopf
The Total Variation on Hypergraphs -Learning on Hypergraphs Revisited Matthias Hein, Simon Setzer, Leonardo Jost, Syama Sundar Rangapuram
Third-rder Edge Statistics: Contour Continuation, Curvature, and Cortical Connections Matthew Lawlor, Steven W. Zucker
Thompson Sampling for 1-imensional Exponential Family Bandits Nathaniel Korda, Emilie Kaufmann, Remi Munos
Top-own Regularization of Deep Belief Networks Hanlin Goh, Nicolas Thome, Matthieu Cord, Joo-wee Lim
Tracking Time-arying Graphical Structure Erich Kummerfeld, David Danks
Trading Computation for Communication: Distributed Stochastic Dual Coordinate Ascent Tianbao Yang
Training and Analysing Deep Recurrent Neural Networks Michiel Hermans, Benjamin Schrauwen
Transfer Learning in a Transductive Setting Marcus Rohrbach, Sandra Ebert, Bernt Schiele
Translating Embeddings for Modeling Multi-elational Data Antoine Bordes, Nicolas Usunier, Alberto Garcia-uran, Jason Weston, Oksana Yakhnenko
Transportability from Multiple Environments with Limited Experiments Elias Bareinboim, Sanghack Lee, Vasant Honavar, Judea Pearl
Two-arget Algorithms for Infinite-rmed Bandits with Bernoulli Rewards Thomas Bonald, Alexandre Proutiere
Understanding Dropout Pierre Baldi, Peter J. Sadowski
Understanding variable importances in forests of randomized trees Gilles Louppe, Louis Wehenkel, Antonio Sutera, Pierre Geurts
Universal models for binary spike patterns using centered Dirichlet processes Il M. Park, Evan W. Archer, Kenneth Latimer, Jonathan Pillow
Unsupervised Spectral Learning of Finite State Transducers Raphael Bailly, Xavier Carreras, Ariadna Quattoni
Unsupervised Structure Learning of Stochastic And-r Grammars Kewei Tu, Maria Pavlovskaia, Song-hun Zhu
Using multiple samples to learn mixture models Jason Lee, Ran Gilad-achrach, Rich Caruana
Variance Reduction for Stochastic Gradient Optimization Chong Wang, Xi Chen, Alex Smola, Eric Xing
Variational Inference for Mahalanobis Distance Metrics in Gaussian Process Regression Michalis Titsias, Miguel Lazaro-redilla
Variational Planning for Graph-ased MDPs Qiang Cheng, Qiang Liu, Feng Chen, Alex Ihler
Variational Policy Search via Trajectory Optimization Sergey Levine, Vladlen Koltun
Visual Concept Learning: Combining Machine Vision and Bayesian Generalization on Concept Hierarchies Yangqing Jia, Joshua T. Abbott, Joseph Austerweil, Thomas Griffiths, Trevor Darrell
Wavelets on Graphs via Deep Learning Raif Rustamov, Leonidas Guibas
What Are the Invariant Occlusive Components of Image Patches? A Probabilistic Generative Approach Zhenwen Dai, Georgios Exarchakis, Jörg Lücke
What do row and column marginals reveal about your dataset? Behzad Golshan, John Byers, Evimaria Terzi
When are Overcomplete Topic Models Identifiable? Uniqueness of Tensor Tucker Decompositions with Structured Sparsity Anima Anandkumar, Daniel Hsu, Majid Janzamin, Sham M. Kakade
When in Doubt, SWAP: High-imensional Sparse Recovery from Correlated Measurements Divyanshu Vats, Richard Baraniuk
Which Space Partitioning Tree to Use for Search? Parikshit Ram, Alexander Gray
Zero-hot Learning Through Cross-odal Transfer Richard Socher, Milind Ganjoo, Christopher D. Manning, Andrew Ng
k-rototype Learning for 3D Rigid Structures Hu Ding, Ronald Berezney, Jinhui Xu
q-CSVM: A q-uantile Estimator for High-imensional Distributions Assaf Glazer, Michael Lindenbaoum, Shaul Markovitch
Σ-ptimality for Active Learning on Gaussian Random Fields Yifei Ma, Roman Garnett, Jeff Schneider