rademacher complexity

Fine-grained Generalization Analysis of Vector-Valued Learning AAAI 2021 Generalization error bounds for deep unfolding RNNs UAI 2021 Learning Theory Can (Sometimes) Explain Generalisation in Graph Neural Networks NeurIPS 2021 Localization, Convexity, and Star Aggregation NeurIPS 2021 Majorizing Measures, Sequential Complexities, and Online Learning COLT 2021 Norm-Based Generalisation Bounds for Deep Multi-Class Convolutional Neural Networks AAAI 2021 Refined Learning Bounds for Kernel and Approximate k-Means NeurIPS 2021 Relative Deviation Margin Bounds ICML 2021 Risk Bounds and Rademacher Complexity in Batch Reinforcement Learning ICML 2021 The Effect of the Intrinsic Dimension on the Generalization of Quadratic Classifiers NeurIPS 2021 Adversarial Learning Guarantees for Linear Hypotheses and Neural Networks ICML 2020 Agnostic Learning with Multiple Objectives NeurIPS 2020 Automated Spectral Kernel Learning AAAI 2020 Beyond Dropout: Feature Map Distortion to Regularize Deep Neural Networks AAAI 2020 Learning discrete distributions with infinite support NeurIPS 2020 Risk Bounds for Learning Multiple Components with Permutation-Invariant Losses AISTATS 2020 Risk Bounds for Reservoir Computing JMLR 2020 Sharp uniform convergence bounds through empirical centralization NeurIPS 2020 Agnostic Federated Learning ICML 2019 A tight excess risk bound via a unified PAC-Bayesian–Rademacher–Shtarkov–MDL complexity ALT 2019 Data-dependent Sample Complexity of Deep Neural Networks via Lipschitz Augmentation NeurIPS 2019 Fast-rate PAC-Bayes Generalization Bounds via Shifted Rademacher Processes NeurIPS 2019 Hypothesis Set Stability and Generalization NeurIPS 2019 Learning Adaptive Random Features AAAI 2019 Learning from Weakly Dependent Data under Dobrushin’s Condition COLT 2019