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Information-Theoretic Generalization Bounds for SGLD via Data-Dependent Estimates., , , , и . NeurIPS, стр. 11013-11023. (2019)Sequential Classification with Empirically Observed Statistics., , и . ITW, стр. 1-5. IEEE, (2019)On the Information Complexity of Proper Learners for VC Classes in the Realizable Case., , , и . CoRR, (2020)Why Is Public Pretraining Necessary for Private Model Training?, , , , , , , и . ICML, том 202 из Proceedings of Machine Learning Research, стр. 10611-10627. PMLR, (2023)Limitations of Information-Theoretic Generalization Bounds for Gradient Descent Methods in Stochastic Convex Optimization., , , , , и . ALT, том 201 из Proceedings of Machine Learning Research, стр. 663-706. PMLR, (2023)Towards a Unified Information-Theoretic Framework for Generalization., , , и . NeurIPS, стр. 26370-26381. (2021)Sharpened Generalization Bounds based on Conditional Mutual Information and an Application to Noisy, Iterative Algorithms., , , , и . NeurIPS, (2020)Information Complexity of Stochastic Convex Optimization: Applications to Generalization, Memorization, and Tracing., , , , и . ICML, OpenReview.net, (2024)Sequential Classification With Empirically Observed Statistics., , и . IEEE Trans. Inf. Theory, 67 (5): 3095-3113 (2021)Faster Differentially Private Convex Optimization via Second-Order Methods., , , и . CoRR, (2023)