895 citations · 5.3k across the 104 of their papers we have counts for
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First-Order Algorithms Without Lipschitz Gradient: A Sequential Local Optimization Approach
Junyu Zhang, Mingyi Hong
First-order algorithms have been popular for solving convex and non-convex optimization problems. A key assumption for the majority of these algorithms is that the gradient of the…
A Two-Timescale Framework for Bilevel Optimization: Complexity Analysis and Application to Actor-Critic
Mingyi Hong, Hoi-To Wai, Zhaoran Wang +1
This paper analyzes a two-timescale stochastic algorithm framework for bilevel optimization. Bilevel optimization is a class of problems which exhibit a two-level structure, and it…
On the Divergence of Decentralized Non-Convex Optimization
Mingyi Hong, Siliang Zeng, Junyu Zhang +1
We study a generic class of decentralized algorithms in which agents jointly optimize the non-convex objective , while only communicating with th…
Generalization Bounds for Stochastic Saddle Point Problems
Junyu Zhang, Mingyi Hong, Mengdi Wang +1
This paper studies the generalization bounds for the empirical saddle point (ESP) solution to stochastic saddle point (SSP) problems. For SSP with Lipschitz continuous and strongly…
Non-convex Min-Max Optimization: Applications, Challenges, and Recent Theoretical Advances
Meisam Razaviyayn, Tianjian Huang, Songtao Lu +3
The min-max optimization problem, also known as the saddle point problem, is a classical optimization problem which is also studied in the context of zero-sum games. Given a class…