rademacher complexity

To Pool or Not To Pool: Analyzing the Regularizing Effects of Group-Fair Training on Shared Models AISTATS 2024 Deep learning with kernels through RKHM and the Perron-Frobenius operator NeurIPS 2023 Empirical Bayes via ERM and Rademacher complexities: the Poisson model COLT 2023 Exploring the Relationship Between Architectural Design and Adversarially Robust Generalization CVPR 2023 Generalization Analysis for Contrastive Representation Learning ICML 2023 Generalization and Estimation Error Bounds for Model-based Neural Networks ICLR 2023 Generalization Bounds for Adversarial Contrastive Learning JMLR 2023 Generalization Guarantees via Algorithm-dependent Rademacher Complexity COLT 2023 Learning with Explanation Constraints NeurIPS 2023 Nearly-tight Bounds for Deep Kernel Learning ICML 2023 On Achieving Optimal Adversarial Test Error ICLR 2023 On Learning Latent Models with Multi-Instance Weak Supervision NeurIPS 2023 Tight and fast generalization error bound of graph embedding in metric space ICML 2023 Tighter Information-Theoretic Generalization Bounds from Supersamples ICML 2023 Towards Empirical Process Theory for Vector-Valued Functions: Metric Entropy of Smooth Function Classes ALT 2023 A Non-Asymptotic Moreau Envelope Theory for High-Dimensional Generalized Linear Models NeurIPS 2022 Discrepancy-Based Active Learning for Domain Adaptation ICLR 2022 Discriminative Similarity for Data Clustering ICLR 2022 Generalization Analysis on Learning with a Concurrent Verifier NeurIPS 2022 Improved Generalization Bounds for Adversarially Robust Learning JMLR 2022 Mean-field nonparametric estimation of interacting particle systems COLT 2022 On Measuring Excess Capacity in Neural Networks NeurIPS 2022 Provable Learning-based Algorithm For Sparse Recovery ICLR 2022 Statistical Rates of Convergence for Functional Partially Linear Support Vector Machines for Classification JMLR 2022 Dropout: Explicit Forms and Capacity Control ICML 2021