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GitHub Pages - Yuqing Wang My research lies at the intersection of machine learning and applied math, combining tools from optimization, (stochastic) dynamics, computational math, analysis, topology, and sampling
Large Learning Rate Tames Homogeneity: Convergence and Balancing Effect . . . Abstract: Recent empirical advances show that training deep models with large learning rate often improves generalization performance However, theoretical justifications on the benefits of large learning rate are highly limited, due to challenges in analysis
arXiv:2110. 03677v1 [cs. LG] 7 Oct 2021 fywang3398, mchen393, tourzhao, mtaog@gatech edu Abstract improves generalization performance However, theoretical justi cations on the bene ts of large learning rate are highly l mited, due to challenges in analysis In this paper, we consider using Gradient Descent (GD) with a large learning rate on a homogeneous matrix factorizat
Large Learning Rate Tames Homogeneity: Convergence and Balancing Effect . . . Large Learning Rate Tames Homogeneity: Convergence and Balancing Effect Award ID (s): 1847802 PAR ID: 10335984 Author (s) Creator (s): Wang, Yuqing; Chen, Minshuo; Zhao, Tuo; Tao, Molei Date Published: 2022-01-01 Journal Name: The International Conference on Learning Representations Format (s): Medium: X Sponsoring Org: National Science
LARGE LEARNING RATE TAMES HOMOGENEITY CONVERGENCE AND BALANCING EFFECT large learning rate are highly limited, due to challenges in analysis In this paper, we consider using Gradient Descent (GD) with a large learning rate o a homogeneous matrix factorization problem, i e , minX;Y kA XY >k2 F We prove a convergence theory for constant large learning rates well beyond 2
Large Learning Rate Tames Homogeneity: Convergence and Balancing Effect Abstract: Recent empirical advances show that training deep models with large learning rate often improves generalization performance However, theoretical justifications on the benefits of large learning rate are highly limited, due to challenges in analysis
Yuqing Wang “Good regularity creates large learning rate implicit biases: edge of stability, balancing, and catapult” Short version is accepted in Mathematics of Modern Machine Learning, NeurIPS 2023 Workshop; long version is under review