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Distributionally Robust Optimization
2021The robust optimization methodology that we have introduced so far is built on a fundamental modeling approach, that is based on set-theoretic, deterministic uncertainty models.
Xu Andy Sun, Antonio J. Conejo
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Distributionally Robust Optimization under Distorted Expectations
SSRN Electronic Journal, 2020Optimal Decision Making Under Distorted Expectation with Partial Distribution Information Decision makers who are not risk neutral may evaluate expected values by distorting objective probabilities to reflect their risk attitudes, a phenomenon known as distorted expectations. This concept is widely applied in behavioral economics, insurance, finance,
Jun Cai +2 more
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Computational Management Science, 2018
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
A. B. Philpott +2 more
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zbMATH Open Web Interface contents unavailable due to conflicting licenses.
A. B. Philpott +2 more
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Distributionally Robust Stochastic Programming
SIAM Journal on Optimization, 2017zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Adaptive Distributionally Robust Optimization
Management Science, 2019We develop a modular and tractable framework for solving an adaptive distributionally robust linear optimization problem, where we minimize the worst-case expected cost over an ambiguity set of probability distributions. The adaptive distributionally robust optimization framework caters for dynamic decision making, where decisions adapt to the ...
Dimitris Bertsimas +2 more
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Conditional Distributionally Robust Functionals
Operations ResearchThis paper addresses decision making in multiple stages, where prior information is available and where consecutive and successive decisions are made. Risk measures assess the random outcome by taking various candidate probability measures into account.
Alexander Shapiro, Alois Pichler
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DRoP: Distributionally Robust Pruning
In the era of exceptionally data-hungry models, careful selection of the training data is essential to mitigate the extensive costs of deep learning. Data pruning offers a solution by removing redundant or uninformative samples from the dataset, which yields faster convergence and improved neural scaling laws.Vysogorets, Artem +2 more
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Distributionally Robust Structural Learning
2023Decision-making under uncertainty is common in various areas of study. Structural learning is a decision problem that involves seeking the optimal structure typically from an exponential number of structures. The task is usually performed on a finite set of samples observed from uncertain environments, which may be subject to unexpected contamination ...
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Distributionally Robust Sequential Recommnedation
Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2023Rui Zhou +4 more
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