50 citations · 53 across the 7 of their papers we have counts for
7 papers
Identifying Risk Variables From Raw ESG Data Using Its Hierarchical Structure
Zhi Chen, Zachary Feinstein, Ionut Florescu
Environmental, Social, and Governance (ESG) data provides non-financial insights into corporations. In this study, we aim to identify relevant ESG raw variables to assess financial…
The Blessing of Strategic Customers in Personalized Pricing
Zhi Chen, Bradley Sturt, Weijun Xie
We consider a feature-based personalized pricing problem in which the buyer is strategic: given the seller's pricing policy, the buyer can augment the features that they reveal to…
A Max-Min-Max Algorithm for Large-Scale Robust Optimization
Kai Tu, Zhi Chen, Man-Chung Yue
Robust optimization (RO) is a powerful paradigm for decision making under uncertainty. Existing algorithms for solving RO, including the reformulation approach and the cutting-plan…
Risk-Averse MDPs under Reward Ambiguity
Haolin Ruan, Zhi Chen, Chin Pang Ho
We propose a distributionally robust return-risk model for Markov decision processes (MDPs) under risk and reward ambiguity. The proposed model optimizes the weighted average of me…
On Approximations of Data-Driven Chance Constrained Programs over Wasserstein Balls
Zhi Chen, Daniel Kuhn, Wolfram Wiesemann
Distributionally robust chance constrained programs minimize a deterministic cost function subject to the satisfaction of one or more safety conditions with high probability, given…
Data-Driven Chance Constrained Programs over Wasserstein Balls
Zhi Chen, Daniel Kuhn, Wolfram Wiesemann
We provide an exact deterministic reformulation for data-driven chance constrained programs over Wasserstein balls. For individual chance constraints as well as joint chance constr…
Distributionally Robust Optimization for Sequential Decision Making
Zhi Chen, Pengqian Yu, William B. Haskell
The distributionally robust Markov Decision Process (MDP) approach asks for a distributionally robust policy that achieves the maximal expected total reward under the most adversar…