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20172025
most citedA Multi-Level Simulation Optimization Approach for Quantile Functions

4 citations · 6 across the 6 of their papers we have counts for

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Showing 2019Show all

5 papers · 1 filter

stat.ML2019

A Unifying Framework for Variance Reduction Algorithms for Finding Zeroes of Monotone Operators

Xun Zhang, William B. Haskell, Zhisheng Ye

It is common to encounter large-scale monotone inclusion problems where the objective has a finite sum structure. We develop a general framework for variance-reduced forward-backwa…

math.OC20192 cited

A Flexible Multi-Facility Capacity Expansion Problem with Risk Aversion

Sixiang Zhao, William B. Haskell, Michel-Alexandre Cardin

This paper studies flexible multi-facility capacity expansion with risk aversion. In this setting, the decision maker can periodically expand the capacity of facilities given obser…

math.OC20194 cited

A Multi-Level Simulation Optimization Approach for Quantile Functions

Songhao Wang, Szu Hui Ng, William Benjamin Haskell

Quantile is a popular performance measure for a stochastic system to evaluate its variability and risk. To reduce the risk, selecting the actions that minimize the tail quantiles o…

math.OC2019

An Accelerated Fitted Value Iteration Algorithm for MDPs with Finite and Vector-Valued Action Space

Sixiang Zhao, William B. Haskell, Michel-Alexandre Cardin

This paper studies an accelerated fitted value iteration (FVI) algorithm to solve high-dimensional Markov decision processes (MDPs). FVI is an approximate dynamic programming algor…

cs.GT2019

Model and Reinforcement Learning for Markov Games with Risk Preferences

Wenjie Huang, Pham Viet Hai, William B. Haskell

We motivate and propose a new model for non-cooperative Markov game which considers the interactions of risk-aware players. This model characterizes the time-consistent dynamic "ri…