Papers
Union Support Recovery in Multi-task Learning
Mladen Kolar, John Lafferty, Larry Wasserman
MULAN: A Java Library for Multi-Label Learning
Grigorios Tsoumakas, Eleftherios Spyromitros-Xioufis, Jozef Vilcek et al.
Universality, Characteristic Kernels and RKHS Embedding of Measures
Bharath K. Sriperumbudur, Kenji Fukumizu, Gert R.G. Lanckriet
Waffles: A Machine Learning Toolkit
Michael Gashler
Producing Power-Law Distributions and Damping Word Frequencies with Two-Stage Language Models
Sharon Goldwater, Thomas L. Griffiths, Mark Johnson
Proximal Methods for Hierarchical Sparse Coding
Rodolphe Jenatton, Julien Mairal, Guillaume Obozinski et al.
MSVMpack: A Multi-Class Support Vector Machine Package
Fabien Lauer, Yann Guermeur
Multiple Kernel Learning Algorithms
Mehmet Gönen, Ethem Alpaydin
Discriminative Learning of Bayesian Networks via Factorized Conditional Log-Likelihood
Alexandra M. Carvalho, Teemu Roos, Arlindo L. Oliveira et al.
Adaptive Subgradient Methods for Online Learning and Stochastic Optimization
John Duchi, Elad Hazan, Yoram Singer
Information Rates of Nonparametric Gaussian Process Methods
Aad van der Vaart, Harry van Zanten
Exploiting Best-Match Equations for Efficient Reinforcement Learning
Harm van Seijen, Shimon Whiteson, Hado van Hasselt et al.
A Cure for Variance Inflation in High Dimensional Kernel Principal Component Analysis
Trine Julie Abrahamsen, Lars Kai Hansen
The arules R-Package Ecosystem: Analyzing Interesting Patterns from Large Transaction Data Sets
Michael Hahsler, Sudheer Chelluboina, Kurt Hornik et al.
Generalized TD Learning
Tsuyoshi Ueno, Shin-ichi Maeda, Motoaki Kawanabe et al.
Kernel Regression in the Presence of Correlated Errors
Kris De Brabanter, Jos De Brabanter, Johan A.K. Suykens et al.
Dirichlet Process Mixtures of Generalized Linear Models
Lauren A. Hannah, David M. Blei, Warren B. Powell
Stochastic Methods for -regularized Loss Minimization
Shai Shalev-Shwartz, Ambuj Tewari
A Refined Margin Analysis for Boosting Algorithms via Equilibrium Margin
Liwei Wang, Masashi Sugiyama, Zhaoxiang Jing et al.
Hyper-Sparse Optimal Aggregation
Stéphane Gaîffas, Guillaume Lecué
Learning Latent Tree Graphical Models
Myung Jin Choi, Vincent Y.F. Tan, Animashree Anandkumar et al.
A Bayesian Approach for Learning and Planning in Partially Observable Markov Decision Processes
Stéphane Ross, Joelle Pineau, Brahim Chaib-draa et al.