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Achieving First-Order Statistical Improvements in Data-Driven Optimization: From No-Free-Lunch to Amplified Decision Perturbation
Henry Lam, Tianyu Wang
Recent proliferation of data-optimization integration has led to a range of methods that aim to improve the statistical performance of data-driven optimization decisions. However,…
Revisit First-order Methods for Geodesically Convex Optimization
Yunlu Shu, Jiaxin Jiang, Lei Shi +1
In a seminal work of Zhang and Sra, gradient descent methods for geodesically convex optimization were comprehensively studied. In particular, Zhang and Sra derived a comparison in…
Contextual Optimization under Covariate Shift: A Robust Approach by Intersecting Wasserstein Balls
Tianyu Wang, Ningyuan Chen, Chun Wang
In contextual optimization, a decision-maker leverages contextual information, often referred to as covariates, to better resolve uncertainty and make informed decisions. In this p…
Distributionally Robust Prescriptive Analytics with Wasserstein Distance
Tianyu Wang, Ningyuan Chen, Chun Wang
In prescriptive analytics, the decision-maker observes historical samples of , where is the uncertain problem parameter and is the concurrent covariate, without kno…