Local Rademacher Complexity Bounds based on Covering Numbers
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Abstract (arXiv)
This paper provides a general result on controlling local Rademacher complexities, which captures in an elegant form to relate the complexities with constraint on the expected norm to the corresponding ones with constraint on the empirical norm. This result is convenient to apply in real applications and could yield refined local Rademacher complexity bounds for function classes satisfying general entropy conditions. We demonstrate the power of our complexity bounds by applying them to derive effective generalization error bounds.
Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Oct 2015.
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Citing papers per year: 2021: 1; 2022: 3; 2023: 1; 2024: 1; 2025: 1; 2026: 3.
10 papers.
- 2022Optimizing Two-way Partial AUC with an End-to-end Frameworkcited by 15
- 2022Understanding Deep Neural Function Approximation in Reinforcement Learning via ε-Greedy Explorationcited by 11
- 2022The Minority Matters: A Diversity-Promoting Collaborative Metric Learning Algorithmcited by 9
- 2024Improved Diversity-Promoting Collaborative Metric Learning for Recommendationcited by 9
- 2021Sample Complexity of Offline Reinforcement Learning with Deep ReLU Networkscited by 7
- 2023Fitted value iteration methods for bicausal optimal transportcited by 2
- 2026Does Order Matter : Connecting The Law of Robustness to Robust Generalizationcited by 1
- 2025Closing the Approximation Gap of Partial AUC Optimization: A Tale of Two Formulationscited by 0
- 2026Sample Complexity of Scientific Discovery: PAC Learnability of Compositional Function Treescited by 0
- 2026Optimal Allocation of Embedding Dimensions under Finite-Sample Constraintscited by 0
- 2026Optimal Allocation of Embedding Dimensions under Finite-Sample Constraints25 Aug 2026
- 2026Sample Complexity of Scientific Discovery: PAC Learnability of Compositional Function Trees28 Jun 2026
- 2026Does Order Matter : Connecting The Law of Robustness to Robust Generalization24 Feb 2026
- 2025Closing the Approximation Gap of Partial AUC Optimization: A Tale of Two Formulations1 Dec 2025
- 2024Improved Diversity-Promoting Collaborative Metric Learning for Recommendation2 Sep 2024
- 2023Fitted value iteration methods for bicausal optimal transport22 Jun 2023
- 2022The Minority Matters: A Diversity-Promoting Collaborative Metric Learning Algorithm30 Sep 2022
- 2022Understanding Deep Neural Function Approximation in Reinforcement Learning via ε-Greedy Exploration15 Sep 2022
- 2022Optimizing Two-way Partial AUC with an End-to-end Framework23 Jun 2022
- 2021Sample Complexity of Offline Reinforcement Learning with Deep ReLU Networks11 Mar 2021