2 citations · 3 across the 5 of their papers we have counts for
5 papers
Value Mirror Descent for Reinforcement Learning
Zhichao Jia, Guanghui Lan
Value iteration-type methods have been extensively studied for computing a nearly optimal value function in reinforcement learning (RL). Under a generative sampling model, these me…
Nearly Optimal Risk Minimization
Zhichao Jia, Guanghui Lan, Zhe Zhang
Convex risk measures play a foundational role in the area of stochastic optimization. However, in contrast to risk neutral models, their applications are still limited due to the l…
Goldstein Stationarity in Lipschitz Constrained Optimization
Benjamin Grimmer, Zhichao Jia
We prove the first convergence guarantees for a subgradient method minimizing a generic Lipschitz function over generic Lipschitz inequality constraints. No smoothness or convexity…
Switch Updating in SPSA Algorithm for Stochastic Optimization with Inequality Constraints
Zhichao Jia, Ziyi Wei, James C. Spall
Simultaneous perturbation stochastic approximation (SPSA) is widely used in stochastic optimization due to its high efficiency, asymptotic stability, and reduced number of required…
First-Order Methods for Nonsmooth Nonconvex Functional Constrained Optimization with or without Slater Points
Zhichao Jia, Benjamin Grimmer
Constrained optimization problems where both the objective and constraints may be nonsmooth and nonconvex arise across many learning and data science settings. In this paper, we sh…