Papers
High-dimensional Variable Selection with Sparse Random Projections: Measurement Sparsity and Statistical Efficiency
Dapo Omidiran, Martin J. Wainwright
Efficient Heuristics for Discriminative Structure Learning of Bayesian Network Classifiers
Franz Pernkopf, Jeff A. Bilmes
Spectral Regularization Algorithms for Learning Large Incomplete Matrices
Rahul Mazumder, Trevor Hastie, Robert Tibshirani
Restricted Eigenvalue Properties for Correlated Gaussian Designs
Garvesh Raskutti, Martin J. Wainwright, Bin Yu
Erratum: SGDQN is Less Careful than Expected
Antoine Bordes, Léon Bottou, Patrick Gallinari et al.
Regularized Discriminant Analysis, Ridge Regression and Beyond
Zhihua Zhang, Guang Dai, Congfu Xu et al.
Learning Gradients: Predictive Models that Infer Geometry and Statistical Dependence
Qiang Wu, Justin Guinney, Mauro Maggioni et al.
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