Learning Theory

Smoothness, Low Noise and Fast Rates NeurIPS 2010 Sparse Semi-supervised Learning Using Conjugate Functions JMLR 2010 Spike timing-dependent plasticity as dynamic filter NeurIPS 2010 Stability Approach to Regularization Selection (StARS) for High Dimensional Graphical Models NeurIPS 2010 Stability Bounds for Stationary φ-mixing and β-mixing Processes JMLR 2010 Stochastic Complexity and Generalization Error of a Restricted Boltzmann Machine in Bayesian Estimation JMLR 2010 Sufficient Conditions for Generating Group Level Sparsity in a Robust Minimax Framework NeurIPS 2010 Supervised Clustering NeurIPS 2010 The Coding Divergence for Measuring the Complexity of Separating Two Sets ACML 2010 The LASSO risk: asymptotic results and real world examples NeurIPS 2010 Tight Sample Complexity of Large-Margin Learning NeurIPS 2010 Towards Property-Based Classification of Clustering Paradigms NeurIPS 2010 Trading off Mistakes and Don't-Know Predictions NeurIPS 2010 Two-Layer Generalization Analysis for Ranking Using Rademacher Average NeurIPS 2010 Universal Consistency of Multi-Class Support Vector Classification NeurIPS 2010 Universal Kernels on Non-Standard Input Spaces NeurIPS 2010 Using Contextual Representations to Efficiently Learn Context-Free Languages JMLR 2010 Variable margin losses for classifier design NeurIPS 2010 Why Does Unsupervised Pre-training Help Deep Learning? AISTATS 2010 Worst-Case Linear Discriminant Analysis NeurIPS 2010 A Parameter-free Hedging Algorithm NeurIPS 2009 Asymptotically Optimal Regularization in Smooth Parametric Models NeurIPS 2009 A unified framework for high-dimensional analysis of M-estimators with decomposable regularizers NeurIPS 2009 Code-specific policy gradient rules for spiking neurons NeurIPS 2009 Compressed Least-Squares Regression NeurIPS 2009