rademacher complexity

Deep Boosting ICML 2014 Learning on graphs using Orthonormal Representation is Statistically Consistent NeurIPS 2014 Maximum Margin Multiclass Nearest Neighbors ICML 2014 Multi-Class Deep Boosting NeurIPS 2014 Learning Kernels Using Local Rademacher Complexity NeurIPS 2013 Localization and Adaptation in Online Learning AISTATS 2013 Multi-Class Classification with Maximum Margin Multiple Kernel ICML 2013 On the Generalization Ability of Online Learning Algorithms for Pairwise Loss Functions ICML 2013 Rounding Methods for Discrete Linear Classification ICML 2013 On the Convergence Rate of -Norm Multiple Kernel Learning JMLR 2012 Q-MKL: Matrix-induced Regularization in Multi-Kernel Learning with Applications to Neuroimaging NeurIPS 2012 A Two-Stage Weighting Framework for Multi-Source Domain Adaptation NeurIPS 2011 Concentration-Based Guarantees for Low-Rank Matrix Reconstruction COLT 2011 Improved Loss Bounds For Multiple Kernel Learning AISTATS 2011 Online Learning: Stochastic, Constrained, and Smoothed Adversaries NeurIPS 2011 The Impact of Unlabeled Patterns in Rademacher Complexity Theory for Kernel Classifiers NeurIPS 2011 Co-regularization Based Semi-supervised Domain Adaptation NeurIPS 2010 Online Learning: Random Averages, Combinatorial Parameters, and Learnability NeurIPS 2010 Permutation Complexity Bound on Out-Sample Error NeurIPS 2010 Rademacher Complexities and Bounding the Excess Risk in Active Learning JMLR 2010 Smoothness, Low Noise and Fast Rates NeurIPS 2010 Two-Layer Generalization Analysis for Ranking Using Rademacher Average NeurIPS 2010 Human Rademacher Complexity NeurIPS 2009 On the Complexity of Linear Prediction: Risk Bounds, Margin Bounds, and Regularization NeurIPS 2008 Rademacher Complexity Bounds for Non-I.I.D. Processes NeurIPS 2008