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Chuwen Zhang

2 accepted papers

2024

A Homogenization Approach for Gradient-Dominated Stochastic Optimization

UAI 2024poster

Gradient dominance property is a condition weaker than strong convexity, yet sufficiently ensures global convergence even in non-convex optimization. This property finds wide applications in machine learning, reinforcement learning (RL), and operations management. In this paper, we propose the stoch…

Cited by 0SourcePDFScholar
2024

Trust Region Methods for Nonconvex Stochastic Optimization beyond Lipschitz Smoothness

AAAI 2024technical

In many important machine learning applications, the standard assumption of having a globally Lipschitz continuous gradient may fail to hold. This paper delves into a more general (L0, L1)-smoothness setting, which gains particular significance within the realms of deep neural networks and distribut…