conftrace_

rademacher complexity

109 papers

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Also known as

RC

Co-occurring keywords

generalization bound (652) kernel methods (1097) neural network (6616) learning theory (519) empirical risk minimization (352) sample complexity (1169) multi-class classification (280) risk bound (71) covering number (34) online learning (1777)

Papers

Beyond Dropout: Feature Map Distortion to Regularize Deep Neural Networks AAAI 2020
Agnostic Federated Learning ICML 2019
Learning from Weakly Dependent Data under Dobrushin’s Condition COLT 2019
Optimality Implies Kernel Sum Classifiers are Statistically Efficient ICML 2019
Uniform concentration and symmetrization for weak interactions COLT 2019
Data-dependent Sample Complexity of Deep Neural Networks via Lipschitz Augmentation NIPS 2019
Theoretical Analysis of Label Distribution Learning AAAI 2019
Robustness to Adversarial Perturbations in Learning from Incomplete Data NIPS 2019
On the Generalization Gap in Reparameterizable Reinforcement Learning ICML 2019
A tight excess risk bound via a unified PAC-Bayesian–Rademacher–Shtarkov–MDL complexity ALT 2019
Learning Adaptive Random Features AAAI 2019
Fast-rate PAC-Bayes Generalization Bounds via Shifted Rademacher Processes NIPS 2019
Rademacher Complexity for Adversarially Robust Generalization ICML 2019
Regularized Gradient Boosting NIPS 2019
Theoretical Investigation of Generalization Bound for Residual Networks IJCAI 2019
Hypothesis Set Stability and Generalization NIPS 2019
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
Dropout Training, Data-dependent Regularization, and Generalization Bounds ICML 2018
Learning Maximum-A-Posteriori Perturbation Models for Structured Prediction in Polynomial Time ICML 2018
Learning in Reproducing Kernel Kreı̆n Spaces ICML 2018
Teaching Semi-Supervised Classifier via Generalized Distillation IJCAI 2018
Uniform Convergence of Gradients for Non-Convex Learning and Optimization NIPS 2018
Understanding Weight Normalized Deep Neural Networks with Rectified Linear Units NIPS 2018
A Sample Complexity Measure with Applications to Learning Optimal Auctions NIPS 2017