Statistical Learning

Relating Function Class Complexity and Cluster Structure in the Function Domain with Applications to Transduction AISTATS 2010 Rescaling, thinning or complementing? On goodness-of-fit procedures for point process models and Generalized Linear Models NeurIPS 2010 Restricted Eigenvalue Properties for Correlated Gaussian Designs JMLR 2010 Risk Bounds for Levy Processes in the PAC-Learning Framework AISTATS 2010 Scrambled Objects for Least-Squares Regression NeurIPS 2010 Smoothness, Low Noise and Fast Rates NeurIPS 2010 Sparse Inverse Covariance Selection via Alternating Linearization Methods NeurIPS 2010 Spectral Regularization for Support Estimation NeurIPS 2010 Stability Approach to Regularization Selection (StARS) for High Dimensional Graphical Models NeurIPS 2010 Stochastic Composite Likelihood JMLR 2010 Sufficient covariates and linear propensity analysis AISTATS 2010 The Group Dantzig Selector AISTATS 2010 The LASSO risk: asymptotic results and real world examples NeurIPS 2010 Tight Sample Complexity of Large-Margin Learning NeurIPS 2010 Two-Layer Generalization Analysis for Ranking Using Rademacher Average NeurIPS 2010 Universal Kernels on Non-Standard Input Spaces NeurIPS 2010 Adaptive False Discovery Rate Control under Independence and Dependence JMLR 2009 A Data-Driven Approach to Modeling Choice NeurIPS 2009 A Fast, Consistent Kernel Two-Sample Test NeurIPS 2009 A joint maximum-entropy model for binary neural population patterns and continuous signals NeurIPS 2009 Asymptotically Optimal Regularization in Smooth Parametric Models NeurIPS 2009 Asymptotic Analysis of MAP Estimation via the Replica Method and Compressed Sensing NeurIPS 2009 AUC optimization and the two-sample problem NeurIPS 2009 A unified framework for high-dimensional analysis of M-estimators with decomposable regularizers NeurIPS 2009 Compressed Least-Squares Regression NeurIPS 2009