5 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…
On the Optimal Construction of Unbiased Gradient Estimators for Zeroth-Order Optimization
Shaocong Ma, Heng Huang
Zeroth-order optimization (ZOO) is an important framework for stochastic optimization when gradients are unavailable or expensive to compute. A potential limitation of existing ZOO…
Robust Reinforcement Learning in Finance: Modeling Market Impact with Elliptic Uncertainty Sets
Shaocong Ma, Heng Huang
In financial applications, reinforcement learning (RL) agents are commonly trained on historical data, where their actions do not influence prices. However, during deployment, thes…
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…
Mixture of Efficient Diffusion Experts Through Automatic Interval and Sub-Network Selection
Alireza Ganjdanesh, Yan Kang, Yuchen Liu +3
Diffusion probabilistic models can generate high-quality samples. Yet, their sampling process requires numerous denoising steps, making it slow and computationally intensive. We pr…