39 citations · 70 across the 28 of their papers we have counts for
29 papers
History-Dependent Recursive Preferences in Markov Decision Processes
William B. Haskell
In finite horizon dynamic programming with history-dependent preferences, the relevant state may be the entire realized history, even when the physical state is Markov. This paper…
Robust Data-Driven Quasiconcave Optimization
Jian Wu, William B. Haskell, Wenjie Huang +1
We investigate a data-driven quasiconcave maximization problem where information about the objective function is limited to a finite sample of data points. We begin by defining an…
Robustness to Modeling Errors in Risk-Sensitive Markov Decision Problems with Markov Risk Measures
Shiping Shao, Abhishek Gupta, William B. Haskell
We consider risk-sensitive Markov decision processes (MDPs), where the MDP model is influenced by a parameter which takes values in a compact metric space. We identify sufficient c…
Information Projection on Banach spaces with Applications to State Independent KL-Weighted Optimal Control
Zachary Selk, William Haskell, Harsha Honnappa
This paper studies constrained information projections on Banach spaces with respect to a Gaussian reference measure. Specifically our interest lies in characterizing projections o…
Efficiently Computing the Quasiconcave Envelope with Incomplete Information
Jian Wu, William B. Haskell, Wenjie Huang +1
In this paper, we study the approximation of an unknown quasiconcave function based on limited partial information. Available information includes lower bounds on the values of the…
Asymptotic Analysis for Data-Driven Inventory Policies
Xun Zhang, Zhisheng Ye, William B. Haskell
We study periodic review stochastic inventory control in the data-driven setting where the retailer makes ordering decisions based only on historical demand observations without an…