Statistical Learning

Implementable confidence sets in high dimensional regression AISTATS 2015 Improved Regret Bounds for Undiscounted Continuous Reinforcement Learning ICML 2015 Information Geometry and Minimum Description Length Networks ICML 2015 Interpolating Convex and Non-Convex Tensor Decompositions via the Subspace Norm NeurIPS 2015 Large-scale probabilistic predictors with and without guarantees of validity NeurIPS 2015 Learning Large-Scale Poisson DAG Models based on OverDispersion Scoring NeurIPS 2015 Learning Parametric-Output HMMs with Two Aliased States ICML 2015 Learning structured densities via infinite dimensional exponential families NeurIPS 2015 Learning Theory and Algorithms for Forecasting Non-stationary Time Series NeurIPS 2015 Learning with Incremental Iterative Regularization NeurIPS 2015 Learning with Square Loss: Localization through Offset Rademacher Complexity COLT 2015 Less is More: Nyström Computational Regularization NeurIPS 2015 Local Smoothness in Variance Reduced Optimization NeurIPS 2015 Max vs Min: Tensor Decomposition and ICA with nearly Linear Sample Complexity COLT 2015 Nearly Optimal Private LASSO NeurIPS 2015 Newton-Stein Method: A Second Order Method for GLMs via Stein's Lemma NeurIPS 2015 Non-asymptotic Analysis of Compressive Fisher Discriminants in terms of the Effective Dimension ACML 2015 Nonparametric von Mises Estimators for Entropies, Divergences and Mutual Informations NeurIPS 2015 On Anomaly Ranking and Excess-Mass Curves AISTATS 2015 On Consistent Surrogate Risk Minimization and Property Elicitation COLT 2015 On Elicitation Complexity NeurIPS 2015 On Estimating L_2^2 Divergence AISTATS 2015 On Learning Distributions from their Samples COLT 2015 On Linearly Constrained Minimum Variance Beamforming JMLR 2015 On the Accuracy of Self-Normalized Log-Linear Models NeurIPS 2015