Papers
On the Foundations of Noise-free Selective Classification
Ran El-Yaniv, Yair Wiener
On the Rate of Convergence of the Bagged Nearest Neighbor Estimate
Gérard Biau, Frédéric Cérou, Arnaud Guyader
Optimal Search on Clustered Structural Constraint for Learning Bayesian Network Structure
Kaname Kojima, Eric Perrier, Seiya Imoto et al.
PAC-Bayesian Analysis of Co-clustering and Beyond
Yevgeny Seldin, Naftali Tishby
Permutation Tests for Studying Classifier Performance
Markus Ojala, Gemma C. Garriga
Posterior Regularization for Structured Latent Variable Models
Kuzman Ganchev, Joã
Practical Approaches to Principal Component Analysis in the Presence of Missing Values
Alexander Ilin, Tapani Raiko
PyBrain
Tom Schaul, Justin Bayer, Daan Wierstra et al.
Quadratic Programming Feature Selection
Irene Rodriguez-Lujan, Ramon Huerta, Charles Elkan et al.
Rademacher Complexities and Bounding the Excess Risk in Active Learning
Vladimir Koltchinskii
Rate Minimaxity of the Lasso and Dantzig Selector for the Loss in Balls
Fei Ye, Cun-Hui Zhang
Regret Bounds and Minimax Policies under Partial Monitoring
Jean-Yves Audibert, Sébastien Bubeck
Regularized Discriminant Analysis, Ridge Regression and Beyond
Zhihua Zhang, Guang Dai, Congfu Xu et al.
Restricted Eigenvalue Properties for Correlated Gaussian Designs
Garvesh Raskutti, Martin J. Wainwright, Bin Yu
Second-Order Bilinear Discriminant Analysis
Christoforos Christoforou, Robert Haralick, Paul Sajda et al.
Semi-Supervised Novelty Detection
Gilles Blanchard, Gyemin Lee, Clayton Scott
SFO: A Toolbox for Submodular Function Optimization
Andreas Krause
Sparse Semi-supervised Learning Using Conjugate Functions
Shiliang Sun, John Shawe-Taylor
Sparse Spectrum Gaussian Process Regression
Miguel Lázaro-Gredilla, Joaquin Quiñnero-Candela, Carl Edward Rasmussen et al.
Spectral Regularization Algorithms for Learning Large Incomplete Matrices
Rahul Mazumder, Trevor Hastie, Robert Tibshirani
Stability Bounds for Stationary φ-mixing and β-mixing Processes
Mehryar Mohri, Afshin Rostamizadeh
Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion
Pascal Vincent, Hugo Larochelle, Isabelle Lajoie et al.
Stochastic Composite Likelihood
Joshua V. Dillon, Guy Lebanon
The SHOGUN Machine Learning Toolbox
Sören Sonnenburg, Gunnar Rätsch, Sebastian Henschel et al.