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pro vyhledávání: '"Dong, Perry"'
Ranking algorithms are fundamental to various online platforms across e-commerce sites to content streaming services. Our research addresses the challenge of adaptively ranking items from a candidate pool for heterogeneous users, a key component in p
Externí odkaz:
http://arxiv.org/abs/2406.05017
Although reinforcement learning methods offer a powerful framework for automatic skill acquisition, for practical learning-based control problems in domains such as robotics, imitation learning often provides a more convenient and accessible alternat
Externí odkaz:
http://arxiv.org/abs/2311.12996
The offline reinforcement learning (RL) paradigm provides a general recipe to convert static behavior datasets into policies that can perform better than the policy that collected the data. While policy constraints, conservatism, and other methods fo
Externí odkaz:
http://arxiv.org/abs/2310.11731
Traditional analyses for non-convex stochastic optimization problems characterize convergence bounds in expectation, which is inadequate as it does not supply a useful performance guarantee on a single run. Motivated by its importance, an emerging li
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a17dd7894fa9d78eee975fb421983982