JMLR Volume 17

• On the Complexity of Best-Arm Identification in Multi-Armed Bandit Models

  Emilie Kaufmann, Olivier Cappé, Aurélien Garivier (1):1−42, 2016 PDF BibTeX

• Multiscale Dictionary Learning: Non-Asymptotic Bounds and Robustness

  Mauro Maggioni, Stanislav Minsker, Nate Strawn (2):1−51, 2016 PDF BibTeX

• Consistent Algorithms for Clustering Time Series

  Azadeh Khaleghi, Daniil Ryabko, Jérémie Mary, Philippe Preux (3):1−32, 2016 PDF BibTeX

• Random Rotation Ensembles

  Rico Blaser, Piotr Fryzlewicz (4):1−26, 2016 PDF BibTeX

• Should We Really Use Post-Hoc Tests Based on Mean-Ranks?

  Alessio Benavoli, Giorgio Corani, Francesca Mangili (5):1−10, 2016 PDF BibTeX

• Minimax Rates in Permutation Estimation for Feature Matching

  Olivier Collier, Arnak S. Dalalyan (6):1−31, 2016 PDF BibTeX

• Consistency and Fluctuations For Stochastic Gradient Langevin Dynamics

  Yee Whye Teh, Alexandre H. Thiery, Sebastian J. Vollmer (7):1−33, 2016 PDF BibTeX

• Knowledge Matters: Importance of Prior Information for Optimization

  Çağlar Gülçehre, Yoshua Bengio (8):1−32, 2016 PDF BibTeX

• Harry: A Tool for Measuring String Similarity

  Konrad Rieck, Christian Wressnegger (9):1−5, 2016 codewebpagePDF BibTeX

• Herded Gibbs Sampling

  Yutian Chen, Luke Bornn, Nando de Freitas, Mareija Eskelin, Jing Fang, Max Welling (10):1−29, 2016 PDF BibTeX

• Complexity of Representation and Inference in Compositional Models with Part Sharing

  Alan Yuille, Roozbeh Mottaghi (11):1−28, 2016 PDF BibTeX

• Noisy Sparse Subspace Clustering

  Yu-Xiang Wang, Huan Xu (12):1−41, 2016 PDF BibTeX

• Learning the Variance of the Reward-To-Go

  Aviv Tamar, Dotan Di Castro, Shie Mannor (13):1−36, 2016 PDF BibTeX

• Convex Calibration Dimension for Multiclass Loss Matrices

  Harish G. Ramaswamy, Shivani Agarwal (14):1−45, 2016 PDF BibTeX

• LLORMA: Local Low-Rank Matrix Approximation

  Joonseok Lee, Seungyeon Kim, Guy Lebanon, Yoram Singer, Samy Bengio (15):1−24, 2016 PDF BibTeX

• A Consistent Information Criterion for Support Vector Machines in Diverging Model Spaces

  Xiang Zhang, Yichao Wu, Lan Wang, Runze Li (16):1−26, 2016 PDF BibTeX

• Extremal Mechanisms for Local Differential Privacy

  Peter Kairouz, Sewoong Oh, Pramod Viswanath (17):1−51, 2016 PDF BibTeX

• Loss Minimization and Parameter Estimation with Heavy Tails

  Daniel Hsu, Sivan Sabato (18):1−40, 2016 PDF BibTeX

• Analysis of Classification-based Policy Iteration Algorithms

  Alessandro Lazaric, Mohammad Ghavamzadeh, R{\'e}mi Munos (19):1−30, 2016 PDF BibTeX

• Operator-valued Kernels for Learning from Functional Response Data

  Hachem Kadri, Emmanuel Duflos, Philippe Preux, Stéphane Canu, Alain Rakotomamonjy, Julien Audiffren (20):1−54, 2016 PDF BibTeX

• MEKA: A Multi-label/Multi-target Extension to WEKA

  Jesse Read, Peter Reutemann, Bernhard Pfahringer, Geoff Holmes (21):1−5, 2016 codewebpagePDF BibTeX

• Gradients Weights improve Regression and Classification

  Samory Kpotufe, Abdeslam Boularias, Thomas Schultz, Kyoungok Kim (22):1−34, 2016 PDF BibTeX

• A Closer Look at Adaptive Regret

  Dmitry Adamskiy, Wouter M. Koolen, Alexey Chernov, Vladimir Vovk (23):1−21, 2016 PDF BibTeX

• Learning Using Anti-Training with Sacrificial Data

  Michael L. Valenzuela, Jerzy W. Rozenblit (24):1−42, 2016 PDF BibTeX

• A Unifying Framework in Vector-valued Reproducing Kernel Hilbert Spaces for Manifold Regularization and Co-Regularized Multi-view Learning

  Hà Quang Minh, Loris Bazzani, Vittorio Murino (25):1−72, 2016 PDF BibTeX

• Quantifying Uncertainty in Random Forests via Confidence Intervals and Hypothesis Tests

  Lucas Mentch, Giles Hooker (26):1−41, 2016 PDF BibTeX

• Statistical-Computational Tradeoffs in Planted Problems and Submatrix Localization with a Growing Number of Clusters and Submatrices

  Yudong Chen, Jiaming Xu (27):1−57, 2016 PDF BibTeX

• Non-linear Causal Inference using Gaussianity Measures

  Daniel Hern{\'a}ndez-Lobato, Pablo Morales-Mombiela, David Lopez-Paz, Alberto Su{\'a}rez (28):1−39, 2016 PDF BibTeX

• Consistent Distribution-Free $K$-Sample and Independence Tests for Univariate Random Variables

  Ruth Heller, Yair Heller, Shachar Kaufman, Barak Brill, Malka Gorfine (29):1−54, 2016 PDF BibTeX

• A Gibbs Sampler for Learning DAGs

  Robert J. B. Goudie, Sach Mukherjee (30):1−39, 2016 PDF BibTeX

• Dimension-free Concentration Bounds on Hankel Matrices for Spectral Learning

  François Denis, Mattias Gybels, Amaury Habrard (31):1−32, 2016 PDF BibTeX

• Distinguishing Cause from Effect Using Observational Data: Methods and Benchmarks

  Joris M. Mooij, Jonas Peters, Dominik Janzing, Jakob Zscheischler, Bernhard Schölkopf (32):1−102, 2016 appendix 1appendix 2PDF BibTeX

• Multi-task Sparse Structure Learning with Gaussian Copula Models

  André R. Gonçalves, Fernando J. Von Zuben, Arindam Banerjee (33):1−30, 2016 PDF BibTeX

• MLlib: Machine Learning in Apache Spark

  Xiangrui Meng, Joseph Bradley, Burak Yavuz, Evan Sparks, Shivaram Venkataraman, Davies Liu, Jeremy Freeman, DB Tsai, Manish Amde, Sean Owen, Doris Xin, Reynold Xin, Michael J. Franklin, Reza Zadeh, Matei Zaharia, Ameet Talwalkar (34):1−7, 2016 codewebpagePDF BibTeX

• OLPS: A Toolbox for On-Line Portfolio Selection

  Bin Li, Doyen Sahoo, Steven C.H. Hoi (35):1−5, 2016 PDF BibTeX

• A Bounded p-norm Approximation of Max-Convolution for Sub-Quadratic Bayesian Inference on Additive Factors

  Julianus Pfeuffer, Oliver Serang (36):1−39, 2016 PDF BibTeX

• Hybrid Orthogonal Projection and Estimation (HOPE): A New Framework to Learn Neural Networks

  Shiliang Zhang, Hui Jiang, Lirong Dai (37):1−33, 2016 PDF BibTeX

• The Optimal Sample Complexity of PAC Learning

  Steve Hanneke (38):1−15, 2016 PDF BibTeX

• End-to-End Training of Deep Visuomotor Policies

  Sergey Levine, Chelsea Finn, Trevor Darrell, Pieter Abbeel (39):1−40, 2016 PDF BibTeX

• On Quantile Regression in Reproducing Kernel Hilbert Spaces with the Data Sparsity Constraint

  Chong Zhang, Yufeng Liu, Yichao Wu (40):1−45, 2016 PDF BibTeX

• BayesPy: Variational Bayesian Inference in Python

  Jaakko Luttinen (41):1−6, 2016 PDF BibTeX

• Variational Inference for Latent Variables and Uncertain Inputs in Gaussian Processes

  Andreas C. Damianou, Michalis K. Titsias, Neil D. Lawrence (42):1−62, 2016 PDF BibTeX

• On the Estimation of the Gradient Lines of a Density and the Consistency of the Mean-Shift Algorithm

  Ery Arias-Castro, David Mason, Bruno Pelletier (43):1−28, 2016 PDF BibTeX

• Scalable Learning of Bayesian Network Classifiers

  Ana M. Martínez, Geoffrey I. Webb, Shenglei Chen, Nayyar A. Zaidi (44):1−35, 2016 PDF BibTeX

• A Unified View on Multi-class Support Vector Classification

  {\"U}rün Do\u{g}an, Tobias Glasmachers, Christian Igel (45):1−32, 2016 PDF BibTeX

• Addressing Environment Non-Stationarity by Repeating Q-learning Updates

  Sherief Abdallah, Michael Kaisers (46):1−31, 2016 PDF BibTeX

• Large Scale Online Kernel Learning

  Jing Lu, Steven C.H. Hoi, Jialei Wang, Peilin Zhao, Zhi-Yong Liu (47):1−43, 2016 PDF BibTeX

• Kernel Mean Shrinkage Estimators

  Krikamol Mu, et, Bharath Sriperumbudur, Kenji Fukumizu, Arthur Gretton, Bernhard Schölkopf (48):1−41, 2016 PDF BibTeX

• SPSD Matrix Approximation vis Column Selection: Theories, Algorithms, and Extensions

  Shusen Wang, Luo Luo, Zhihua Zhang (49):1−49, 2016 PDF BibTeX

• Combinatorial Multi-Armed Bandit and Its Extension to Probabilistically Triggered Arms

  Wei Chen, Yajun Wang, Yang Yuan, Qinshi Wang (50):1−33, 2016 PDF BibTeX

• Differentially Private Data Releasing for Smooth Queries

  Ziteng Wang, Chi Jin, Kai Fan, Jiaqi Zhang, Junliang Huang, Yiqiao Zhong, Liwei Wang (51):1−42, 2016 PDF BibTeX

• Subspace Learning with Partial Information

  Alon Gonen, Dan Rosenbaum, Yonina C. Eldar, Shai Shalev-Shwartz (52):1−21, 2016 PDF BibTeX

• Iterative Hessian Sketch: Fast and Accurate Solution Approximation for Constrained Least-Squares

  Mert Pilanci, Martin J. Wainwright (53):1−38, 2016 PDF BibTeX

• Estimating Causal Structure Using Conditional DAG Models

  Chris. J. Oates, Jim Q. Smith, Sach Mukherjee (54):1−23, 2016 PDF BibTeX

• Adaptive Lasso and group-Lasso for functional Poisson regression

  Stéphane Ivanoff, Franck Picard, Vincent Rivoirard (55):1−46, 2016 PDF BibTeX

• Causal Inference through a Witness Protection Program

  Ricardo Silva, Robin Evans (56):1−53, 2016 PDF BibTeX

• Structure Discovery in Bayesian Networks by Sampling Partial Orders

  Teppo Niinim\"{a}ki, Pekka Parviainen, Mikko Koivisto (57):1−47, 2016 PDF BibTeX

• Estimation from Pairwise Comparisons: Sharp Minimax Bounds with Topology Dependence

  Nihar B. Shah, Sivaraman Balakrishnan, Joseph Bradley, Abhay Parekh, Kannan Ramch, ran, Martin J. Wainwright (58):1−47, 2016 PDF BibTeX

• Domain-Adversarial Training of Neural Networks

  Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario March, Victor Lempitsky (59):1−35, 2016 PDF BibTeX

• Probabilistic Low-Rank Matrix Completion from Quantized Measurements

  Sonia A. Bhaskar (60):1−34, 2016 PDF BibTeX

• DSA: Decentralized Double Stochastic Averaging Gradient Algorithm

  Aryan Mokhtari, Alejandro Ribeiro (61):1−35, 2016 PDF BibTeX

• The Statistical Performance of Collaborative Inference

  Gérard Biau, Kevin Bleakley, Benoît Cadre (62):1−29, 2016 PDF BibTeX

• Convergence of an Alternating Maximization Procedure

  Andreas Andresen, Vladimir Spokoiny (63):1−53, 2016 PDF BibTeX

• StructED: Risk Minimization in Structured Prediction

  Yossi Adi, Joseph Keshet (64):1−5, 2016 PDF BibTeX

• Stereo Matching by Training a Convolutional Neural Network to Compare Image Patches

  Jure Žbontar, Yann LeCun (65):1−32, 2016 codePDF BibTeX

• Bayesian Policy Gradient and Actor-Critic Algorithms

  Mohammad Ghavamzadeh, Yaakov Engel, Michal Valko (66):1−53, 2016 PDF BibTeX

• Practical Kernel-Based Reinforcement Learning

  André M.S. Barreto, Doina Precup, Joelle Pineau (67):1−70, 2016 PDF BibTeX

• An Information-Theoretic Analysis of Thompson Sampling

  Daniel Russo, Benjamin Van Roy (68):1−30, 2016 PDF BibTeX

• Compressed Gaussian Process for Manifold Regression

  Rajarshi Guhaniyogi, David B. Dunson (69):1−26, 2016 PDF BibTeX

• On the Characterization of a Class of Fisher-Consistent Loss Functions and its Application to Boosting

  Matey Neykov, Jun S. Liu, Tianxi Cai (70):1−32, 2016 PDF BibTeX

• Exact Inference on Gaussian Graphical Models of Arbitrary Topology using Path-Sums

  P.-L. Giscard, Z. Choo, S. J. Thwaite, D. Jaksch (71):1−19, 2016 PDF BibTeX

• Challenges in multimodal gesture recognition

  Sergio Escalera, Vassilis Athitsos, Isabelle Guyon (72):1−54, 2016 PDF BibTeX

• An Emphatic Approach to the Problem of Off-policy Temporal-Difference Learning

  Richard S. Sutton, A. Rupam Mahmood, Martha White (73):1−29, 2016 PDF BibTeX

• Learning Algorithms for Second-Price Auctions with Reserve

  Mehryar Mohri, Andres Munoz Medina (74):1−25, 2016 PDF BibTeX

• Distributed Coordinate Descent Method for Learning with Big Data

  Peter Richtárik, Martin Takáč (75):1−25, 2016 PDF BibTeX

• Scaling-up Empirical Risk Minimization: Optimization of Incomplete $U$-statistics

  Stephan Clémençon, Igor Colin, Aurélien Bellet (76):1−36, 2016 PDF BibTeX

• Iterative Regularization for Learning with Convex Loss Functions

  Junhong Lin, Lorenzo Rosasco, Ding-Xuan Zhou (77):1−38, 2016 PDF BibTeX

• Latent Space Inference of Internet-Scale Networks

  Qirong Ho, Junming Yin, Eric P. Xing (78):1−41, 2016 PDF BibTeX

• Patient Risk Stratification with Time-Varying Parameters: A Multitask Learning Approach

  Jenna Wiens, John Guttag, Eric Horvitz (79):1−23, 2016 PDF BibTeX

• Multiplicative Multitask Feature Learning

  Xin Wang, Jinbo Bi, Shipeng Yu, Jiangwen Sun, Minghu Song (80):1−33, 2016 PDF BibTeX

• The Benefit of Multitask Representation Learning

  Andreas Maurer, Massimiliano Pontil, Bernardino Romera-Paredes (81):1−32, 2016 PDF BibTeX

• Model-free Variable Selection in Reproducing Kernel Hilbert Space

  Lei Yang, Shaogao Lv, Junhui Wang (82):1−24, 2016 PDF BibTeX

• CVXPY: A Python-Embedded Modeling Language for Convex Optimization

  Steven Diamond, Stephen Boyd (83):1−5, 2016 codewebpagePDF BibTeX

• Lenient Learning in Independent-Learner Stochastic Cooperative Games

  Ermo Wei, Sean Luke (84):1−42, 2016 PDF BibTeX

• Structure-Leveraged Methods in Breast Cancer Risk Prediction

  Jun Fan, Yirong Wu, Ming Yuan, David Page, Jie Liu, Irene M. Ong, Peggy Peissig, Elizabeth Burnside (85):1−15, 2016 PDF BibTeX

• LIBMF: A Library for Parallel Matrix Factorization in Shared-memory Systems

  Wei-Sheng Chin, Bo-Wen Yuan, Meng-Yuan Yang, Yong Zhuang, Yu-Chin Juan, Chih-Jen Lin (86):1−5, 2016 codePDF BibTeX

• L1-Regularized Least Squares for Support Recovery of High Dimensional Single Index Models with Gaussian Designs

  Matey Neykov, Jun S. Liu, Tianxi Cai (87):1−37, 2016 PDF BibTeX

• Spectral Ranking using Seriation

  Fajwel Fogel, Alexandre d'Aspremont, Milan Vojnovic (88):1−45, 2016 PDF BibTeX

• Sparsity and Error Analysis of Empirical Feature-Based Regularization Schemes

  Xin Guo, Jun Fan, Ding-Xuan Zhou (89):1−34, 2016 PDF BibTeX

• Estimating Diffusion Networks: Recovery Conditions, Sample Complexity and Soft-thresholding Algorithm

  Manuel Gomez-Rodriguez, Le Song, Hadi Daneshm, Bernhard Schölkopf (90):1−29, 2016 PDF BibTeX

• Rounding-based Moves for Semi-Metric Labeling

  M. Pawan Kumar, Puneet K. Dokania (91):1−42, 2016 PDF BibTeX

• Rate Optimal Denoising of Simultaneously Sparse and Low Rank Matrices

  Dan Yang, Zongming Ma, Andreas Buja (92):1−27, 2016 PDF BibTeX

• Hierarchical Relative Entropy Policy Search

  Christian Daniel, Gerhard Neumann, Oliver Kroemer, Jan Peters (93):1−50, 2016 PDF BibTeX

• Convex Regression with Interpretable Sharp Partitions

  Ashley Petersen, Noah Simon, Daniela Witten (94):1−31, 2016 PDF BibTeX

• JCLAL: A Java Framework for Active Learning

  Oscar Reyes, Eduardo Pérez, María del Carmen Rodríguez-Hernández, Habib M. Fardoun, Sebastián Ventura (95):1−5, 2016 codePDF BibTeX

• Integrated Common Sense Learning and Planning in POMDPs

  Brendan Juba (96):1−37, 2016 PDF BibTeX

• Cells in Multidimensional Recurrent Neural Networks

  Gundram Leifert, Tobias Strau{\ss}, Tobias Gr{ü}ning, Welf Wustlich, Roger Labahn (97):1−37, 2016 PDF BibTeX

• Learning Taxonomy Adaptation in Large-scale Classification

  Rohit Babbar, Ioannis Partalas, Eric Gaussier, Massih-Reza Amini, Cécile Amblard (98):1−37, 2016 PDF BibTeX

• How to Center Deep Boltzmann Machines

  Jan Melchior, Asja Fischer, Laurenz Wiskott (99):1−61, 2016 PDF BibTeX

• Control Function Instrumental Variable Estimation of Nonlinear Causal Effect Models

  Zijian Guo, Dylan S. Small (100):1−35, 2016 PDF BibTeX

• Structure Learning in Bayesian Networks of a Moderate Size by Efficient Sampling

  Ru He, Jin Tian, Huaiqing Wu (101):1−54, 2016 appendixPDF BibTeX

• Spectral Methods Meet EM: A Provably Optimal Algorithm for Crowdsourcing

  Yuchen Zhang, Xi Chen, Dengyong Zhou, Michael I. Jordan (102):1−44, 2016 PDF BibTeX

• Bayesian Leave-One-Out Cross-Validation Approximations for Gaussian Latent Variable Models

  Aki Vehtari, Tommi Mononen, Ville Tolvanen, Tuomas Sivula, Ole Winther (103):1−38, 2016 PDF BibTeX

• e-PAL: An Active Learning Approach to the Multi-Objective Optimization Problem

  Marcela Zuluaga, Andreas Krause, Markus P{ü}schel (104):1−32, 2016 PDF BibTeX

• Trend Filtering on Graphs

  Yu-Xiang Wang, James Sharpnack, Alexander J. Smola, Ryan J. Tibshirani (105):1−41, 2016 PDF BibTeX

• Multi-Task Learning for Straggler Avoiding Predictive Job Scheduling

  Neeraja J. Yadwadkar, Bharath Hariharan, Joseph E. Gonzalez, R, y Katz (106):1−37, 2016 PDF BibTeX

• Interleaved Text/Image Deep Mining on a Large-Scale Radiology Database for Automated Image Interpretation

  Hoo-Chang Shin, Le Lu, Lauren Kim, Ari Seff, Jianhua Yao, Ronald M. Summers (107):1−31, 2016 PDF BibTeX

• Distribution-Matching Embedding for Visual Domain Adaptation

  Mahsa Baktashmotlagh, Mehrtash Har, i, Mathieu Salzmann (108):1−30, 2016 PDF BibTeX

• Monotonic Calibrated Interpolated Look-Up Tables

  Maya Gupta, Andrew Cotter, Jan Pfeifer, Konstantin Voevodski, Kevin Canini, Alexander Mangylov, Wojciech Moczydlowski, Alexander van Esbroeck (109):1−47, 2016 PDF BibTeX

• Are Random Forests Truly the Best Classifiers?

  Michael Wainberg, Babak Alipanahi, Brendan J. Frey (110):1−5, 2016 PDF BibTeX

• Minimax Adaptive Estimation of Nonparametric Hidden Markov Models

  Yohann De Castro, {\'E}lisabeth Gassiat, Claire Lacour (111):1−43, 2016 PDF BibTeX

• Decrypting “Cryptogenic” Epilepsy: Semi-supervised Hierarchical Conditional Random Fields For Detecting Cortical Lesions In MRI-Negative Patients

  Bilal Ahmed, Thomas Thesen, Karen E. Blackmon, Ruben Kuzniekcy, Orrin Devinsky, Carla E. Brodley (112):1−30, 2016 PDF BibTeX

• Fused Lasso Approach in Regression Coefficients Clustering -- Learning Parameter Heterogeneity in Data Integration

  Lu Tang, Peter X.K. Song (113):1−23, 2016 PDF BibTeX

• The LRP Toolbox for Artificial Neural Networks

  Sebastian Lapuschkin, Alexander Binder, Grégoire Montavon, Klaus-Robert Müller, Wojciech Samek (114):1−5, 2016 PDF BibTeX

• Equivalence of Graphical Lasso and Thresholding for Sparse Graphs

  Somayeh Sojoudi (115):1−21, 2016 PDF BibTeX

• A Network That Learns Strassen Multiplication

  Veit Elser (116):1−13, 2016 PDF BibTeX

• Revisiting the Nyström Method for Improved Large-scale Machine Learning

  Alex Gittens, Michael W. Mahoney (117):1−65, 2016 PDF BibTeX

• Improving Structure MCMC for Bayesian Networks through Markov Blanket Resampling

  Chengwei Su, Mark E. Borsuk (118):1−20, 2016 PDF BibTeX

• Volumetric Spanners: An Efficient Exploration Basis for Learning

  Elad Hazan, Zohar Karnin (119):1−34, 2016 PDF BibTeX

• Quasi-Monte Carlo Feature Maps for Shift-Invariant Kernels

  Haim Avron, Vikas Sindhwani, Jiyan Yang, Michael W. Mahoney (120):1−38, 2016 PDF BibTeX

• Variational Dependent Multi-output Gaussian Process Dynamical Systems

  Jing Zhao, Shiliang Sun (121):1−36, 2016 PDF BibTeX

• Multiple Output Regression with Latent Noise

  Jussi Gillberg, Pekka Marttinen, Matti Pirinen, Antti J. Kangas, Pasi Soininen, Mehreen Ali, Aki S. Havulinna, Marjo-Riitta Järvelin, Mika Ala-Korpela, Samuel Kaski (122):1−35, 2016 PDF BibTeX

• The Constrained Dantzig Selector with Enhanced Consistency

  Yinfei Kong, Zemin Zheng, Jinchi Lv (123):1−22, 2016 PDF BibTeX

• Bootstrap-Based Regularization for Low-Rank Matrix Estimation

  Julie Josse, Stefan Wager (124):1−29, 2016 PDF BibTeX

• Bayesian Optimization for Likelihood-Free Inference of Simulator-Based Statistical Models

  Michael U. Gutmann, Jukka Cor, er (125):1−47, 2016 PDF BibTeX

• On Lower and Upper Bounds in Smooth and Strongly Convex Optimization

  Yossi Arjevani, Shai Shalev-Shwartz, Ohad Shamir (126):1−51, 2016 PDF BibTeX

• Dual Control for Approximate Bayesian Reinforcement Learning

  Edgar D. Klenske, Philipp Hennig (127):1−30, 2016 PDF BibTeX

• Multiple-Instance Learning from Distributions

  Gary Doran, Soumya Ray (128):1−50, 2016 PDF BibTeX

• An Online Convex Optimization Approach to Blackwell's Approachability

  Nahum Shimkin (129):1−23, 2016 PDF BibTeX

• A Well-Conditioned and Sparse Estimation of Covariance and Inverse Covariance Matrices Using a Joint Penalty

  Ashwini Maurya (130):1−28, 2016 PDF BibTeX

• String and Membrane Gaussian Processes

  Yves-Laurent Kom Samo, Stephen J. Roberts (131):1−87, 2016 PDF BibTeX

• Extracting PICO Sentences from Clinical Trial Reports using Supervised Distant Supervision

  Byron C. Wallace, Joël Kuiper, Aakash Sharma, Mingxi (Brian) Zhu, Iain J. Marshall (132):1−25, 2016 PDF BibTeX

• Cross-Corpora Unsupervised Learning of Trajectories in Autism Spectrum Disorders

  Huseyin Melih Elibol, Vincent Nguyen, Scott Linderman, Matthew Johnson, Amna Hashmi, Finale Doshi-Velez (133):1−38, 2016 PDF BibTeX

• Adjusting for Chance Clustering Comparison Measures

  Simone Romano, Nguyen Xuan Vinh, James Bailey, Karin Verspoor (134):1−32, 2016 PDF BibTeX

• Refined Error Bounds for Several Learning Algorithms

  Steve Hanneke (135):1−55, 2016 PDF BibTeX

• Synergy of Monotonic Rules

  Vladimir Vapnik, Rauf Izmailov (136):1−33, 2016 PDF BibTeX

• Pymanopt: A Python Toolbox for Optimization on Manifolds using Automatic Differentiation

  James Townsend, Niklas Koep, Sebastian Weichwald (137):1−5, 2016 PDF BibTeX

• CrossCat: A Fully Bayesian Nonparametric Method for Analyzing Heterogeneous, High Dimensional Data

  Vikash Mansinghka, Patrick Shafto, Eric Jonas, Cap Petschulat, Max Gasner, Joshua B. Tenenbaum (138):1−49, 2016 PDF BibTeX

• Regularized Policy Iteration with Nonparametric Function Spaces

  Amir-massoud Farahm, , Mohammad Ghavamzadeh, Csaba Szepesvári, Shie Mannor (139):1−66, 2016 PDF BibTeX

• Multiscale Adaptive Representation of Signals: I. The Basic Framework

  Cheng Tai, Weinan E (140):1−38, 2016 PDF BibTeX

• Sparse PCA via Covariance Thresholding

  Yash Deshp, e, Andrea Montanari (141):1−41, 2016 PDF BibTeX

• Large Scale Visual Recognition through Adaptation using Joint Representation and Multiple Instance Learning

  Judy Hoffman, Deepak Pathak, Eric Tzeng, Jonathan Long, Sergio Guadarrama, Trevor Darrell, Kate Saenko (142):1−31, 2016 PDF BibTeX

• Covariance-based Clustering in Multivariate and Functional Data Analysis

  Francesca Ieva, Anna Maria Paganoni, Nicholas Tarabelloni (143):1−21, 2016 PDF BibTeX

• MOCCA: Mirrored Convex/Concave Optimization for Nonconvex Composite Functions

  Rina Foygel Barber, Emil Y. Sidky (144):1−51, 2016 PDF BibTeX

• True Online Temporal-Difference Learning

  Harm van Seijen, A. Rupam Mahmood, Patrick M. Pilarski, Marlos C. Machado, Richard S. Sutton (145):1−40, 2016 PDF BibTeX

• Penalized Maximum Likelihood Estimation of Multi-layered Gaussian Graphical Models

  Jiahe Lin, Sumanta Basu, Moulinath Banerjee, George Michailidis (146):1−51, 2016 PDF BibTeX

• Local Network Community Detection with Continuous Optimization of Conductance and Weighted Kernel K-Means

  Twan van Laarhoven, Elena Marchiori (147):1−28, 2016 PDF BibTeX

• Megaman: Scalable Manifold Learning in Python

  James McQueen, Marina Meilă, Jacob VanderPlas, Zhongyue Zhang (148):1−5, 2016 codewebpagePDF BibTeX

• Kernel Estimation and Model Combination in A Bandit Problem with Covariates

  Wei Qian, Yuhong Yang (149):1−37, 2016 PDF BibTeX

• A General Framework for Consistency of Principal Component Analysis

  Dan Shen, Haipeng Shen, J. S. Marron (150):1−34, 2016 PDF BibTeX

• Conditional Independencies under the Algorithmic Independence of Conditionals

  Jan Lemeire (151):1−20, 2016 PDF BibTeX

• Learning Theory for Distribution Regression

  Zoltán Szabó, Bharath K. Sriperumbudur, Barnabás Póczos, Arthur Gretton (152):1−40, 2016 PDF BibTeX

• A Differential Equation for Modeling Nesterov's Accelerated Gradient Method: Theory and Insights

  Weijie Su, Stephen Boyd, Emmanuel J. Candès (153):1−43, 2016 PDF BibTeX

• Importance Weighting Without Importance Weights: An Efficient Algorithm for Combinatorial Semi-Bandits

  Gergely Neu, Gábor Bartók (154):1−21, 2016 PDF BibTeX

• New Perspectives on k-Support and Cluster Norms

  Andrew M. McDonald, Massimiliano Pontil, Dimitris Stamos (155):1−38, 2016 PDF BibTeX

• Minimum Density Hyperplanes

  Nicos G. Pavlidis, David P. Hofmeyr, Sotiris K. Tasoulis (156):1−33, 2016 PDF BibTeX

• Theoretical Analysis of the Optimal Free Responses of Graph-Based SFA for the Design of Training Graphs

  Alberto N. Escalante-B., Laurenz Wiskott (157):1−36, 2016 PDF BibTeX

• Universal Approximation Results for the Temporal Restricted Boltzmann Machine and the Recurrent Temporal Restricted Boltzmann Machine

  Simon Odense, Roderick Edwards (158):1−21, 2016 PDF BibTeX

• Exploration of the (Non-)Asymptotic Bias and Variance of Stochastic Gradient Langevin Dynamics

  Sebastian J. Vollmer, Konstantinos C. Zygalakis, Yee Whye Teh (159):1−48, 2016 PDF BibTeX

• A General Framework for Constrained Bayesian Optimization using Information-based Search

  Jos\'{e} Miguel Hern\'{a}ndez-Lobato, Michael A. Gelbart, Ryan P. Adams, Matthew W. Hoffman, Zoubin Ghahramani (160):1−53, 2016 PDF BibTeX

• Optimal Estimation and Completion of Matrices with Biclustering Structures

  Chao Gao, Yu Lu, Zongming Ma, Harrison H. Zhou (161):1−29, 2016 PDF BibTeX

• The Teaching Dimension of Linear Learners

  Ji Liu, Xiaojin Zhu (162):1−25, 2016 PDF BibTeX

• Augmentable Gamma Belief Networks

  Mingyuan Zhou, Yulai Cong, Bo Chen (163):1−44, 2016 PDF BibTeX

• Optimal Estimation of Derivatives in Nonparametric Regression

  Wenlin Dai, Tiejun Tong, Marc G. Genton (164):1−25, 2016 PDF BibTeX

• Double or Nothing: Multiplicative Incentive Mechanisms for Crowdsourcing

  Nihar B. Shah, Dengyong Zhou (165):1−52, 2016 PDF BibTeX

• Joint Structural Estimation of Multiple Graphical Models

  Jing Ma, George Michailidis (166):1−48, 2016 PDF BibTeX

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• Feature-Level Domain Adaptation

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• Efficient Computation of Gaussian Process Regression for Large Spatial Data Sets by Patching Local Gaussian Processes

  Chiwoo Park, Jianhua Z. Huang (174):1−29, 2016 PDF BibTeX

• bandicoot: a Python Toolbox for Mobile Phone Metadata

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• Input Output Kernel Regression: Supervised and Semi-Supervised Structured Output Prediction with Operator-Valued Kernels

  Céline Brouard, Marie Szafranski, Florence d'Alché-Buc (176):1−48, 2016 PDF BibTeX

• A Note on the Sample Complexity of the Er-SpUD Algorithm by Spielman, Wang and Wright for Exact Recovery of Sparsely Used Dictionaries

  Radoslaw Adamczak (177):1−18, 2016 PDF BibTeX

• The Asymptotic Performance of Linear Echo State Neural Networks

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• On the Consistency of the Likelihood Maximization Vertex Nomination Scheme: Bridging the Gap Between Maximum Likelihood Estimation and Graph Matching

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• Consistency of Cheeger and Ratio Graph Cuts

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• Jointly Informative Feature Selection Made Tractable by Gaussian Modeling

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• fastFM: A Library for Factorization Machines

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• The Factorized Self-Controlled Case Series Method: An Approach for Estimating the Effects of Many Drugs on Many Outcomes

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• Electronic Health Record Analysis via Deep Poisson Factor Models

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• Low-Rank Doubly Stochastic Matrix Decomposition for Cluster Analysis

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• Neural Autoregressive Distribution Estimation

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• ERRATA: On the Estimation of the Gradient Lines of a Density and the Consistency of the Mean-Shift Algorithm

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• Weak Convergence Properties of Constrained Emphatic Temporal-difference Learning with Constant and Slowly Diminishing Stepsize

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• Learning Latent Variable Models by Pairwise Cluster Comparison: Part I - Theory and Overview

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• GenSVM: A Generalized Multiclass Support Vector Machine

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• Gains and Losses are Fundamentally Different in Regret Minimization: The Sparse Case

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• Linear Convergence of Randomized Feasible Descent Methods Under the Weak Strong Convexity Assumption

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• Learning Latent Variable Models by Pairwise Cluster Comparison: Part II - Algorithm and Evaluation

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• An Error Bound for L1-norm Support Vector Machine Coefficients in Ultra-high Dimension

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