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cs.LG2022
Slowly Changing Adversarial Bandit Algorithms are Efficient for Discounted MDPs
Ian A. Kash, Lev Reyzin, Zishun Yu
Reinforcement learning generalizes multi-armed bandit problems with additional difficulties of a longer planning horizon and unknown transition kernel. We explore a black-box reduc…
cs.LG2022
Orthogonal Gromov-Wasserstein Discrepancy with Efficient Lower Bound
Hongwei Jin, Zishun Yu, Xinhua Zhang
Comparing structured data from possibly different metric-measure spaces is a fundamental task in machine learning, with applications in, e.g., graph classification. The Gromov-Wass…