Papers
A Comparison of the Lasso and Marginal Regression
Christopher R. Genovese, Jiashun Jin, Larry Wasserman et al.
Optimistic Bayesian Sampling in Contextual-Bandit Problems
Benedict C. May, Nathan Korda, Anthony Lee et al.
Pattern for Python
Tom De Smedt, Walter Daelemans
EP-GIG Priors and Applications in Bayesian Sparse Learning
Zhihua Zhang, Shusen Wang, Dehua Liu et al.
An Improved GLMNET for L1-regularized Logistic Regression
Guo-Xun Yuan, Chia-Hua Ho, Chih-Jen Lin
Jstacs: A Java Framework for Statistical Analysis and Classification of Biological Sequences
Jan Grau, Jens Keilwagen, André Gohr et al.
Integrating a Partial Model into Model Free Reinforcement Learning
Aviv Tamar, Dotan Di Castro, Ron Meir
Confidence-Weighted Linear Classification for Text Categorization
Koby Crammer, Mark Dredze, Fernando Pereira
Regularization Techniques for Learning with Matrices
Sham M. Kakade, Shai Shalev-Shwartz, Ambuj Tewari
Entropy Search for Information-Efficient Global Optimization
Philipp Hennig, Christian J. Schuler
Variational Multinomial Logit Gaussian Process
Kian Ming A. Chai
Manifold Identification in Dual Averaging for Regularized Stochastic Online Learning
Sangkyun Lee, Stephen J. Wright
glm-ie: Generalised Linear Models Inference & Estimation Toolbox
Hannes Nickisch
Restricted Strong Convexity and Weighted Matrix Completion: Optimal Bounds with Noise
Sahand Negahban, Martin J. Wainwright
Mixability is Bayes Risk Curvature Relative to Log Loss
Tim van Erven, Mark D. Reid, Robert C. Williamson
A Model of the Perception of Facial Expressions of Emotion by Humans: Research Overview and Perspectives
Aleix Martinez, Shichuan Du
Structured Sparsity via Alternating Direction Methods
Zhiwei Qin, Donald Goldfarb
Feature Selection via Dependence Maximization
Le Song, Alex Smola, Arthur Gretton et al.
On Ranking and Generalization Bounds
Wojciech Rejchel
Transfer in Reinforcement Learning via Shared Features
George Konidaris, Ilya Scheidwasser, Andrew Barto
Query Strategies for Evading Convex-Inducing Classifiers
Blaine Nelson, Benjamin I. P. Rubinstein, Ling Huang et al.