JMLR 2016
227 papers
A Closer Look at Adaptive Regret
Dmitry Adamskiy, Wouter M. Koolen, Alexey Chernov, Vladimir Vovk A General Framework for Constrained Bayesian Optimization Using Information-Based Search
José Miguel Hernández-Lobato, Michael A. Gelbart, Ryan P. Adams, Matthew W. Hoffman, Zoubin Ghahramani A Gibbs Sampler for Learning DAGs
Robert J. B. Goudie, Sach Mukherjee Adjusting for Chance Clustering Comparison Measures
Simone Romano, Nguyen Xuan Vinh, James Bailey, Karin Verspoor Are Random Forests Truly the Best Classifiers?
Michael Wainberg, Babak Alipanahi, Brendan J. Frey Augmentable Gamma Belief Networks
Mingyuan Zhou, Yulai Cong, Bo Chen Bandicoot: A Python Toolbox for Mobile Phone Metadata
Yves-Alexandre de Montjoye, Luc Rocher, Alex Sandy Pentland Bayesian Group Factor Analysis with Structured Sparsity
Shiwen Zhao, Chuan Gao, Sayan Mukherjee, Barbara E Engelhardt Cells in Multidimensional Recurrent Neural Networks
Gundram Leifert, Tobias Strauß, Tobias Grüning, Welf Wustlich, Roger Labahn Challenges in Multimodal Gesture Recognition
Sergio Escalera, Vassilis Athitsos, Isabelle Guyon Composite Multiclass Losses
Robert C. Williamson, Elodie Vernet, Mark D. Reid Consistency of Cheeger and Ratio Graph Cuts
Nicolás García Trillos, Dejan Slepčev, James von Brecht, Thomas Laurent, Xavier Bresson Consistent Algorithms for Clustering Time Series
Azadeh Khaleghi, Daniil Ryabko, Jérémie Mary, Philippe Preux Cross-Corpora Unsupervised Learning of Trajectories in Autism Spectrum Disorders
Huseyin Melih Elibol, Vincent Nguyen, Scott Linderman, Matthew Johnson, Amna Hashmi, Finale Doshi-Velez Differentially Private Data Releasing for Smooth Queries
Ziteng Wang, Chi Jin, Kai Fan, Jiaqi Zhang, Junliang Huang, Yiqiao Zhong, Liwei Wang Distinguishing Cause from Effect Using Observational Data: Methods and Benchmarks
Joris M. Mooij, Jonas Peters, Dominik Janzing, Jakob Zscheischler, Bernhard Schölkopf Distributed Submodular Maximization
Baharan Mirzasoleiman, Amin Karbasi, Rik Sarkar, Andreas Krause Domain-Adversarial Training of Neural Networks
Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario March, Victor Lempitsky Electronic Health Record Analysis via Deep Poisson Factor Models
Ricardo Henao, James T. Lu, Joseph E. Lucas, Jeffrey Ferranti, Lawrence Carin End-to-End Training of Deep Visuomotor Policies
Sergey Levine, Chelsea Finn, Trevor Darrell, Pieter Abbeel Estimation from Pairwise Comparisons: Sharp Minimax Bounds with Topology Dependence
Nihar B. Shah, Sivaraman Balakrishnan, Joseph Bradley, Abhay Parekh, Kannan Ramchandran, Martin J. Wainwright Feature-Level Domain Adaptation
Wouter M. Kouw, Laurens J.P. van der Maaten, Jesse H. Krijthe, Marco Loog Gradients Weights Improve Regression and Classification
Samory Kpotufe, Abdeslam Boularias, Thomas Schultz, Kyoungok Kim Herded Gibbs Sampling
Yutian Chen, Luke Bornn, Nando de Freitas, Mareija Eskelin, Jing Fang, Max Welling Hierarchical Relative Entropy Policy Search
Christian Daniel, Gerhard Neumann, Oliver Kroemer, Jan Peters How to Center Deep Boltzmann Machines
Jan Melchior, Asja Fischer, Laurenz Wiskott Kernel Mean Shrinkage Estimators
Krikamol Muandet, Bharath Sriperumbudur, Kenji Fukumizu, Arthur Gretton, Bernhard Schölkopf Large Scale Online Kernel Learning
Jing Lu, Steven C.H. Hoi, Jialei Wang, Peilin Zhao, Zhi-Yong Liu Learning Planar Ising Models
Jason K. Johnson, Diane Oyen, Michael Chertkov, Praneeth Netrapalli Learning Taxonomy Adaptation in Large-Scale Classification
Rohit Babbar, Ioannis Partalas, Eric Gaussier, Massih-Reza Amini, Cécile Amblard Learning the Variance of the Reward-to-Go
Aviv Tamar, Dotan Di Castro, Shie Mannor Learning Theory for Distribution Regression
Zoltán Szabó, Bharath K. Sriperumbudur, Barnabás Póczos, Arthur Gretton LLORMA: Local Low-Rank Matrix Approximation
Joonseok Lee, Seungyeon Kim, Guy Lebanon, Yoram Singer, Samy Bengio Measuring Dependence Powerfully and Equitably
Yakir A. Reshef, David N. Reshef, Hilary K. Finucane, Pardis C. Sabeti, Michael Mitzenmacher Minimum Density Hyperplanes
Nicos G. Pavlidis, David P. Hofmeyr, Sotiris K. Tasoulis Mlr: Machine Learning in R
Bernd Bischl, Michel Lang, Lars Kotthoff, Julia Schiffner, Jakob Richter, Erich Studerus, Giuseppe Casalicchio, Zachary M. Jones Monotonic Calibrated Interpolated Look-up Tables
Maya Gupta, Andrew Cotter, Jan Pfeifer, Konstantin Voevodski, Kevin Canini, Alexander Mangylov, Wojciech Moczydlowski, Alexander van Esbroeck Multi-Task Learning for Straggler Avoiding Predictive Job Scheduling
Neeraja J. Yadwadkar, Bharath Hariharan, Joseph E. Gonzalez, Randy Katz 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 Multiplicative Multitask Feature Learning
Xin Wang, Jinbo Bi, Shipeng Yu, Jiangwen Sun, Minghu Song Neural Autoregressive Distribution Estimation
Benigno Uria, Marc-Alexandre Côté, Karol Gregor, Iain Murray, Hugo Larochelle New Perspectives on K-Support and Cluster Norms
Andrew M. McDonald, Massimiliano Pontil, Dimitris Stamos Non-Linear Causal Inference Using Gaussianity Measures
Daniel Hernández-Lobato, Pablo Morales-Mombiela, David Lopez-Paz, Alberto Suárez On Bayes Risk Lower Bounds
Xi Chen, Adityanand Guntuboyina, Yuchen Zhang Online PCA with Optimal Regret
Jiazhong Nie, Wojciech Kotlowski, Manfred K. Warmuth Operator-Valued Kernels for Learning from Functional Response Data
Hachem Kadri, Emmanuel Duflos, Philippe Preux, Stéphane Canu, Alain Rakotomamonjy, Julien Audiffren Practical Kernel-Based Reinforcement Learning
André M.S. Barreto, Doina Precup, Joelle Pineau Quasi-Monte Carlo Feature Maps for Shift-Invariant Kernels
Haim Avron, Vikas Sindhwani, Jiyan Yang, Michael W. Mahoney Random Rotation Ensembles
Rico Blaser, Piotr Fryzlewicz Regularized Policy Iteration with Nonparametric Function Spaces
Amir-massoud Farahmand, Mohammad Ghavamzadeh, Csaba Szepesvári, Shie Mannor Scalable Learning of Bayesian Network Classifiers
Ana M. Martínez, Geoffrey I. Webb, Shenglei Chen, Nayyar A. Zaidi Spectral Ranking Using Seriation
Fajwel Fogel, Alexandre d'Aspremont, Milan Vojnovic Stable Graphical Models
Navodit Misra, Ercan E. Kuruoglu String and Membrane Gaussian Processes
Yves-Laurent Kom Samo, Stephen J. Roberts 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 Subspace Learning with Partial Information
Alon Gonen, Dan Rosenbaum, Yonina C. Eldar, Shai Shalev-Shwartz Synergy of Monotonic Rules
Vladimir Vapnik, Rauf Izmailov The Asymptotic Performance of Linear Echo State Neural Networks
Romain Couillet, Gilles Wainrib, Harry Sevi, Hafiz Tiomoko Ali The Benefit of Multitask Representation Learning
Andreas Maurer, Massimiliano Pontil, Bernardino Romera-Paredes The LRP Toolbox for Artificial Neural Networks
Sebastian Lapuschkin, Alexander Binder, Grégoire Montavon, Klaus-Robert Müller, Wojciech Samek Trend Filtering on Graphs
Yu-Xiang Wang, James Sharpnack, Alexander J. Smola, Ryan J. Tibshirani True Online Temporal-Difference Learning
Harm van Seijen, A. Rupam Mahmood, Patrick M. Pilarski, Marlos C. Machado, Richard S. Sutton