82 citations · 103 across the 10 of their papers we have counts for
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A Shrinkage Path Heuristic for Wasserstein Distributionally Robust Optimization
Lingjun Meng, Ryan Cory-Wright, Wolfram Wiesemann
Wasserstein distributionally robust optimization (DRO) is a versatile and widely adopted framework for decision-making under uncertainty, yet its standard deterministic reformulati…
Efficient Algorithms for Robust Markov Decision Processes with -Rectangular Ambiguity Sets
Chin Pang Ho, Marek Petrik, Wolfram Wiesemann
Robust Markov decision processes (MDPs) have attracted significant interest due to their ability to protect MDPs from poor out-of-sample performance in the presence of ambiguity. I…
Don't Look Back in Anger: Wasserstein Distributionally Robust Optimization with Nonstationary Data
Dominic S. T. Keehan, Edward J. Anderson, Wolfram Wiesemann
We study data-driven decision problems where historical observations are generated by a time-evolving distribution whose consecutive shifts are bounded in Wasserstein distance. We…
Distributionally Robust Optimization
Daniel Kuhn, Soroosh Shafiee, Wolfram Wiesemann
Distributionally robust optimization (DRO) studies decision problems under uncertainty where the probability distribution governing the uncertain problem parameters is itself uncer…
A Note on Piecewise Affine Decision Rules for Robust, Stochastic, and Data-Driven Optimization
Simon Thomä, Maximilian Schiffer, Wolfram Wiesemann
Multi-stage decision-making under uncertainty, where decisions are taken under sequentially revealing uncertain problem parameters, is often essential to faithfully model manageria…
An MILP-Based Solution Scheme for Factored and Robust Factored Markov Decision Processes
Huikang Liu, Wolfram Wiesemann, Man-Chung Yue
Factored Markov decision processes (MDPs) are a prominent paradigm within the artificial intelligence community for modeling and solving large-scale MDPs whose rewards and dynamics…