1 citations · 1 across the 5 of their papers we have counts for
5 papers
On Structural Properties of Risk-Averse Optimal Stopping Problems
Xingyu Ren, Michael C. Fu, Steven I. Marcus
We establish structural properties of optimal stopping problems under time-consistent dynamic (coherent) risk measures, focusing on value function monotonicity and the existence of…
Stochastic Derivative Estimation for Discontinuous Sample Performances: A Leibniz Integration Perspective
Xingyu Ren, Michael C. Fu, Pierre L'Ecuyer
We develop a novel stochastic derivative estimation framework for sample performance functions that are discontinuous in the parameter of interest, based on the multidimensional Le…
New Bounds and Truncation Boundaries for Importance Sampling
Yijuan Liang, Guangxin Jiang, Michael C. Fu
Importance sampling (IS) is a technique that enables statistical estimation of output performance at multiple input distributions from a single nominal input distribution. IS is co…
Generalizing the Generalized Likelihood Ratio Method Through a Push-Out Leibniz Integration Approach
Xingyu Ren, Michael C. Fu
We generalize the generalized likelihood ratio (GLR) method through a novel push-out Leibniz integration approach. Extending the conventional push-out likelihood ratio (LR) method,…
Risk-Sensitive Reinforcement Learning via Policy Gradient Search
Prashanth L. A., Michael Fu
The objective in a traditional reinforcement learning (RL) problem is to find a policy that optimizes the expected value of a performance metric such as the infinite-horizon cumula…