Papers
Matched Gene Selection and Committee Classifier for Molecular Classification of Heterogeneous Diseases
Guoqiang Yu, Yuanjian Feng, David J. Miller et al.
Importance Sampling for Continuous Time Bayesian Networks
Yu Fan, Jing Xu, Christian R. Shelton
Model-based Boosting 2.0
Torsten Hothorn, Peter Bühlmann, Thomas Kneib et al.
On Over-fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation
Gavin C. Cawley, Nicola L. C. Talbot
Matrix Completion from Noisy Entries
Raghunandan H. Keshavan, Andrea Montanari, Sewoong Oh
A Surrogate Modeling and Adaptive Sampling Toolbox for Computer Based Design
Dirk Gorissen, Ivo Couckuyt, Piet Demeester et al.
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
Chromatic PAC-Bayes Bounds for Non-IID Data: Applications to Ranking and Stationary β-Mixing Processes
Liva Ralaivola, Marie Szafranski, Guillaume Stempfel
Fast and Scalable Local Kernel Machines
Nicola Segata, Enrico Blanzieri
Sparse Spectrum Gaussian Process Regression
Miguel Lázaro-Gredilla, Joaquin Quiñnero-Candela, Carl Edward Rasmussen et al.
Permutation Tests for Studying Classifier Performance
Markus Ojala, Gemma C. Garriga
How to Explain Individual Classification Decisions
David Baehrens, Timon Schroeter, Stefan Harmeling et al.
The SHOGUN Machine Learning Toolbox
Sören Sonnenburg, Gunnar Rätsch, Sebastian Henschel et al.
Bayesian Learning in Sparse Graphical Factor Models via Variational Mean-Field Annealing
Ryo Yoshida, Mike West
Evolving Static Representations for Task Transfer
Phillip Verbancsics, Kenneth O. Stanley
FastInf: An Efficient Approximate Inference Library
Ariel Jaimovich, Ofer Meshi, Ian McGraw et al.
Estimation of a Structural Vector Autoregression Model Using Non-Gaussianity
Aapo Hyvärinen, Kun Zhang, Shohei Shimizu et al.
Consensus-Based Distributed Support Vector Machines
Pedro A. Forero, Alfonso Cano, Georgios B. Giannakis
Introduction to Causal Inference
Peter Spirtes
On the Foundations of Noise-free Selective Classification
Ran El-Yaniv, Yair Wiener
MOA: Massive Online Analysis
Albert Bifet, Geoff Holmes, Richard Kirkby et al.
Near-optimal Regret Bounds for Reinforcement Learning
Thomas Jaksch, Ronald Ortner, Peter Auer
Hilbert Space Embeddings and Metrics on Probability Measures
Bharath K. Sriperumbudur, Arthur Gretton, Kenji Fukumizu et al.
Quadratic Programming Feature Selection
Irene Rodriguez-Lujan, Ramon Huerta, Charles Elkan et al.