Yishay Mansour

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About

Yishay Mansour is a leading figure in theoretical computer science and machine learning, whose work has profoundly shaped online learning, algorithmic game theory, and reinforcement learning. He is perhaps best known for pioneering the study of adversarial multi-armed bandits and online convex optimization, providing foundational algorithms that balance exploration and exploitation under uncertainty. His research also spans computational learning theory, where he contributed to the analysis of boosting and decision trees, and networking, where he developed models for competitive analysis of storage and retrieval systems. With over 30,000 citations, Mansour’s impact is immense; his 2002 paper on competitive access time via dynamic storage rearrangement, though modestly cited, exemplifies his early work on algorithmic efficiency in dynamic environments. A recipient of multiple best paper awards and a Fellow of the ACM, Mansour has also made notable contributions to the theory of no-regret learning and its applications in economics and distributed systems. His clear, rigorous style has inspired a generation of researchers, making complex ideas in online decision-making accessible and actionable.