7 citations · 9 across the 8 of their papers we have counts for
8 papers
Revisiting Zeroth-Order Optimization: Minimum-Variance Two-Point Estimators and Directionally Aligned Perturbations
Shaocong Ma, Heng Huang
In this paper, we explore the two-point zeroth-order gradient estimator and identify the distribution of random perturbations that minimizes the estimator's asymptotic variance as…
Rectified Robust Policy Optimization for Model-Uncertain Constrained Reinforcement Learning without Strong Duality
Shaocong Ma, Ziyi Chen, Yi Zhou +1
The goal of robust constrained reinforcement learning (RL) is to optimize an agent's performance under the worst-case model uncertainty while satisfying safety or resource constrai…
End-to-End Mesh Optimization of a Hybrid Deep Learning Black-Box PDE Solver
Shaocong Ma, James Diffenderfer, Bhavya Kailkhura +1
Deep learning has been widely applied to solve partial differential equations (PDEs) in computational fluid dynamics. Recent research proposed a PDE correction framework that lever…
Data Sampling Affects the Complexity of Online SGD over Dependent Data
Shaocong Ma, Ziyi Chen, Yi Zhou +2
Conventional machine learning applications typically assume that data samples are independently and identically distributed (i.i.d.). However, practical scenarios often involve a d…
Accelerated Proximal Alternating Gradient-Descent-Ascent for Nonconvex Minimax Machine Learning
Ziyi Chen, Shaocong Ma, Yi Zhou
Alternating gradient-descent-ascent (AltGDA) is an optimization algorithm that has been widely used for model training in various machine learning applications, which aims to solve…
Greedy-GQ with Variance Reduction: Finite-time Analysis and Improved Complexity
Shaocong Ma, Ziyi Chen, Yi Zhou +1
Greedy-GQ is a value-based reinforcement learning (RL) algorithm for optimal control. Recently, the finite-time analysis of Greedy-GQ has been developed under linear function appro…