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Towards More Efficient, Robust, Instance-adaptive, and Generalizable Sequential Decision making
The primary goal of my Ph.D. study is to develop provably efficient and practical algorithms for data-driven sequential decision-making under uncertainty. My work focuses on reinforcement learning (RL), multi-armed bandits, and their applications, including recommendation systems, computer networks,
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