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Squeeze All: Novel Estimator and Self-Normalized Bound for Linear Contextual Bandits.

, , и . AISTATS, том 206 из Proceedings of Machine Learning Research, стр. 3098-3124. PMLR, (2023)

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In-Hwan Oh

In-Hwan Oh

Jung-Hwan Oh

Min-Jung Oh

Min-Zae Oh

 

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Multinomial Logit Contextual Bandits: Provable Optimality and Practicality., и . AAAI, стр. 9205-9213. AAAI Press, (2021)Crowd Counting with Decomposed Uncertainty., , и . AAAI, стр. 11799-11806. AAAI Press, (2020)Thompson Sampling for Multinomial Logit Contextual Bandits., и . NeurIPS, стр. 3145-3155. (2019)Combinatorial Neural Bandits., , и . ICML, том 202 из Proceedings of Machine Learning Research, стр. 14203-14236. PMLR, (2023)Local Anti-Concentration Class: Logarithmic Regret for Greedy Linear Contextual Bandit., и . CoRR, (2024)Counting and Segmenting Sorghum Heads., , и . CoRR, (2019)Adaptive Pattern Matching with Reinforcement Learning for Dynamic Graphs., , , , и . HiPC, стр. 92-101. IEEE, (2018)Semi-Parametric Contextual Pricing Algorithm using Cox Proportional Hazards Model., , , , , и . ICML, том 202 из Proceedings of Machine Learning Research, стр. 5771-5786. PMLR, (2023)Mixed-Effects Contextual Bandits., , , и . AAAI, стр. 13409-13417. AAAI Press, (2024)Model-based Offline Reinforcement Learning with Count-based Conservatism., и . ICML, том 202 из Proceedings of Machine Learning Research, стр. 16728-16746. PMLR, (2023)