Statistical Learning

Restricted Strong Convexity and Weighted Matrix Completion: Optimal Bounds with Noise JMLR 2012 Robust Kernel Density Estimation JMLR 2012 Sample Complexity of Composite Likelihood AISTATS 2012 Semiparametric Principal Component Analysis NeurIPS 2012 Semiparametric Pseudo-Likelihood Estimation in Markov Random Fields AISTATS 2012 Smooth-projected Neighborhood Pursuit for High-dimensional Nonparanormal Graph Estimation NeurIPS 2012 Spectral learning of linear dynamics from generalised-linear observations with application to neural population data NeurIPS 2012 Statistical Consistency of Ranking Methods in A Rank-Differentiable Probability Space NeurIPS 2012 Statistical Optimization in High Dimensions AISTATS 2012 Statistical test for consistent estimation of causal effects in linear non-Gaussian models AISTATS 2012 Stochastic optimization and sparse statistical recovery: Optimal algorithms for high dimensions NeurIPS 2012 Structured Learning of Gaussian Graphical Models NeurIPS 2012 The Perturbed Variation NeurIPS 2012 Tight Bounds on Profile Redundancy and Distinguishability NeurIPS 2012 Transelliptical Component Analysis NeurIPS 2012 Transelliptical Graphical Models NeurIPS 2012 Wilks’ phenomenon and penalized likelihood-ratio test for nonparametric curve registration AISTATS 2012 A blind sparse deconvolution method for neural spike identification NeurIPS 2011 Active Learning with a Drifting Distribution NeurIPS 2011 A Cure for Variance Inflation in High Dimensional Kernel Principal Component Analysis JMLR 2011 A General Linear Non-Gaussian State-Space Model: Identifiability, Identification, and Applications ACML 2011 Agnostic Selective Classification NeurIPS 2011 A More Powerful Two-Sample Test in High Dimensions using Random Projection NeurIPS 2011 Asymptotic Theory for Linear-Chain Conditional Random Fields AISTATS 2011 Bayesian Spike-Triggered Covariance Analysis NeurIPS 2011