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pro vyhledávání: '"TAN, KEVIN"'
Autor:
Fang, Dongping, Duan, Lian, Yuan, Xiaojing, Klunder, Allyn, Tan, Kevin, Cao, Suiting, Ji, Yeqing, Xu, Mike
Accurate prediction of medical conditions with straight past clinical evidence is a long-sought topic in the medical management and health insurance field. Although great progress has been made with machine learning algorithms, the medical community
Externí odkaz:
http://arxiv.org/abs/2412.03701
Hybrid Reinforcement Learning (RL), where an agent learns from both an offline dataset and online explorations in an unknown environment, has garnered significant recent interest. A crucial question posed by Xie et al. (2022) is whether hybrid RL can
Externí odkaz:
http://arxiv.org/abs/2408.04526
Automatic differentiation (AD) has driven recent advances in machine learning, including deep neural networks and Hamiltonian Markov Chain Monte Carlo methods. Partially observed nonlinear stochastic dynamical systems have proved resistant to AD tech
Externí odkaz:
http://arxiv.org/abs/2407.03085
Sequential decision-making domains such as recommender systems, healthcare and education often have unobserved heterogeneity in the population that can be modeled using latent bandits $-$ a framework where an unobserved latent state determines the mo
Externí odkaz:
http://arxiv.org/abs/2405.17324
Matrix sketching is a powerful tool for reducing the size of large data matrices. Yet there are fundamental limitations to this size reduction when we want to recover an accurate estimator for a task such as least square regression. We show that thes
Externí odkaz:
http://arxiv.org/abs/2405.05343
Autor:
Tan, Kevin, Xu, Ziping
Hybrid Reinforcement Learning (RL), leveraging both online and offline data, has garnered recent interest, yet research on its provable benefits remains sparse. Additionally, many existing hybrid RL algorithms (Song et al., 2023; Nakamoto et al., 202
Externí odkaz:
http://arxiv.org/abs/2403.09701