Author

Jiajin Li

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Preprint Oct 2026

Nonminimizing Attractors of Stochastic Subgradient Methods on Semialgebraic Functions

We disprove a conjecture of Davis, Drusvyatskiy, and Jiang (2026) that subdifferential regularity can be removed from the generic guarantee of convergence to local minimizers for stochastic subgradient methods. We construct a globally Lipschitz, coercive, semialgebraic function in two dimensions for which the method co...

Jia-Jin Li · 0 citations

#machine learning Preprint Oct 2026

On the Depth Transfer of Neural Optimizers via L-Smoothness

Scaling deep learning models efficiently requires predictable performance transfer across compute regimes. Establishing reliable hyperparameter transfer allows optimal training dynamics and structural choices to be discovered on low-cost proxy models, avoiding catastrophic failures during massive-scale training. Existi...

Maximilian Wong, Chen-Yu Xu, Jia-Jin Li et al. · 0 citations

Preprint Jul 2026

Last-Iterate Convergence of Single-Loop Stochastic Methods for Constrained Convex-Concave Minimax Problems

This paper establishes last-iterate convergence of stochastic first-order methods for constrained smooth convex--concave minimax optimization under the standard bounded-variance stochastic oracle and establishes two types of convergence guarantees.

Tao-Li Zheng, Jiajin Li, Anthony Man-Cho So · 1 citation · ⚡1

#data science Preprint Jul 2026

Nonconvex Composite Functional Constraints via First-Order Augmented Lagrangian Methods under Local Regularity

The main contribution is a finite-time mechanism for converting stationarity of the truncated minimax problem into a KKT certificate for the original constrained problem and establishing explicit convergence rates for the proposed method in terms of the KKT residual.

Lingling Zhu, Jiajin Li · 2 citations