rademacher complexity

On the Generalization Gap in Reparameterizable Reinforcement Learning ICML 2019 Optimality Implies Kernel Sum Classifiers are Statistically Efficient ICML 2019 Rademacher Complexity for Adversarially Robust Generalization ICML 2019 Regularized Gradient Boosting NeurIPS 2019 Robustness to Adversarial Perturbations in Learning from Incomplete Data NeurIPS 2019 Theoretical Analysis of Label Distribution Learning AAAI 2019 Theoretical Investigation of Generalization Bound for Residual Networks IJCAI 2019 Uniform concentration and symmetrization for weak interactions COLT 2019 Adaptive Dropout with Rademacher Complexity Regularization ICLR 2018 Dropout Training, Data-dependent Regularization, and Generalization Bounds ICML 2018 Learning in Reproducing Kernel Kreı̆n Spaces ICML 2018 Learning Maximum-A-Posteriori Perturbation Models for Structured Prediction in Polynomial Time ICML 2018 On the Power of Over-parametrization in Neural Networks with Quadratic Activation ICML 2018 Rademacher Complexity Bounds for a Penalized Multi-class Semi-supervised Algorithm (Extended Abstract) IJCAI 2018 Size-Independent Sample Complexity of Neural Networks COLT 2018 Teaching Semi-Supervised Classifier via Generalized Distillation IJCAI 2018 Understanding Weight Normalized Deep Neural Networks with Rectified Linear Units NeurIPS 2018 Uniform Convergence of Gradients for Non-Convex Learning and Optimization NeurIPS 2018 A Sample Complexity Measure with Applications to Learning Optimal Auctions NeurIPS 2017 Early stopping for kernel boosting algorithms: A general analysis with localized complexities NeurIPS 2017 ZigZag: A New Approach to Adaptive Online Learning COLT 2017 Learning with Square Loss: Localization through Offset Rademacher Complexity COLT 2015 Structural Maxent Models ICML 2015 (Almost) No Label No Cry NeurIPS 2014 An Inequality with Applications to Structured Sparsity and Multitask Dictionary Learning COLT 2014