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Understanding Incremental Learning of Gradient Descent: A Fine-grained Analysis of Matrix Sensing.

, , , , and . ICML, volume 202 of Proceedings of Machine Learning Research, page 15200-15238. PMLR, (2023)

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Structure Learning of Mixed Graphical Models., and . AISTATS, volume 31 of JMLR Workshop and Conference Proceedings, page 388-396. JMLR.org, (2013)Sketching Meets Random Projection in the Dual: A Provable Recovery Algorithm for Big and High-dimensional Data., , , , and . AISTATS, volume 54 of Proceedings of Machine Learning Research, page 1150-1158. PMLR, (2017)Gradient Descent Finds Global Minima of Deep Neural Networks., , , , and . ICML, volume 97 of Proceedings of Machine Learning Research, page 1675-1685. PMLR, (2019)Shape Matters: Understanding the Implicit Bias of the Noise Covariance., , , and . COLT, volume 134 of Proceedings of Machine Learning Research, page 2315-2357. PMLR, (2021)Neural Networks can Learn Representations with Gradient Descent., , and . COLT, volume 178 of Proceedings of Machine Learning Research, page 5413-5452. PMLR, (2022)Optimization-Based Separations for Neural Networks., and . COLT, volume 178 of Proceedings of Machine Learning Research, page 3-64. PMLR, (2022)Bilinear Classes: A Structural Framework for Provable Generalization in RL., , , , , , and . ICML, volume 139 of Proceedings of Machine Learning Research, page 2826-2836. PMLR, (2021)Local Optimization Achieves Global Optimality in Multi-Agent Reinforcement Learning., , , and . ICML, volume 202 of Proceedings of Machine Learning Research, page 42200-42226. PMLR, (2023)Computationally Efficient PAC RL in POMDPs with Latent Determinism and Conditional Embeddings., , , , and . ICML, volume 202 of Proceedings of Machine Learning Research, page 34615-34641. PMLR, (2023)Provable Hierarchy-Based Meta-Reinforcement Learning., , and . AISTATS, volume 206 of Proceedings of Machine Learning Research, page 10918-10967. PMLR, (2023)