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The Implicit Bias of Gradient Descent on Separable Data

, , , , and . (2017)cite arxiv:1710.10345Comment: Final JMLR version, with improved discussions over v3. Main improvements in journal version over conference version (v2 appeared in ICLR): We proved the measure zero case for main theorem (with implications for the rates), and the multi-class case.

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Lexicographic and Depth-Sensitive Margins in Homogeneous and Non-Homogeneous Deep Models., , , , and . ICML, volume 97 of Proceedings of Machine Learning Research, page 4683-4692. PMLR, (2019)Semi-Cyclic Stochastic Gradient Descent., , , , and . ICML, volume 97 of Proceedings of Machine Learning Research, page 1764-1773. PMLR, (2019)The Implicit Bias of Gradient Descent on Separable Data, , , , and . (2017)cite arxiv:1710.10345Comment: Final JMLR version, with improved discussions over v3. Main improvements in journal version over conference version (v2 appeared in ICLR): We proved the measure zero case for main theorem (with implications for the rates), and the multi-class case.The Complexity of Making the Gradient Small in Stochastic Convex Optimization, , , , , and . (2019)cite arxiv:1902.04686.Semi-Cyclic Stochastic Gradient Descent., , , , and . CoRR, (2019)The Min-Max Complexity of Distributed Stochastic Convex Optimization with Intermittent Communication., , , and . COLT, volume 134 of Proceedings of Machine Learning Research, page 4386-4437. PMLR, (2021)Efficiently Learning Neural Networks: What Assumptions May Suffice?, , and . CoRR, (2023)When is Agnostic Reinforcement Learning Statistically Tractable?, , , , and . CoRR, (2023)Interpolation Learning With Minimum Description Length., and . CoRR, (2023)Quantifying the Benefit of Using Differentiable Learning over Tangent Kernels., , , and . ICML, volume 139 of Proceedings of Machine Learning Research, page 7379-7389. PMLR, (2021)