52 citations · 85 across the 8 of their papers we have counts for
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Last-Iterate Convergence of Single-Loop Stochastic Methods for Constrained Convex-Concave Minimax Problems
Taoli Zheng, Jiajin Li, Anthony Man-Cho So
In this paper, we study last-iterate convergence of stochastic first-order methods for constrained smooth convex--concave minimax optimization under the standard bounded-variance s…
Nonconvex Composite Functional Constraints via First-Order Augmented Lagrangian Methods under Local Regularity
Linglingzhi Zhu, Jiajin Li
We study nonasymptotic convergence of primal-dual methods for a class of nonconvex constrained optimization problems with a convex-composite structure. In this class, both the obje…
Doubly Smoothed Optimistic Gradients: A Universal Approach for Smooth Minimax Problems
Taoli Zheng, Anthony Man-Cho So, Jiajin Li
Smooth minimax optimization problems play a central role in a wide range of applications, including machine learning, game theory, and operations research. However, existing algori…
Set Smoothness Unlocks Clarke Hyper-stationarity in Bilevel Optimization
He Chen, Jiajin Li, Anthony Man-Cho So
Solving bilevel optimization (BLO) problems to global optimality is generally intractable. A common surrogate is to compute a hyper-stationary point -- a stationary point of the hy…
Tikhonov Regularization is Optimal Transport Robust under Martingale Constraints
Jiajin Li, Sirui Lin, Jose Blanchet +1
Distributionally robust optimization has been shown to offer a principled way to regularize learning models. In this paper, we find that Tikhonov regularization is distributionally…
Fast Epigraphical Projection-based Incremental Algorithms for Wasserstein Distributionally Robust Support Vector Machine
Jiajin Li, Caihua Chen, Anthony Man-Cho So
Wasserstein \textbf{D}istributionally \textbf{R}obust \textbf{O}ptimization (DRO) is concerned with finding decisions that perform well on data that are drawn from the worst-case p…