Tracking Most Significant Shifts in Infinite-Armed Bandits
Joe Suk, Jung-hun Kim
2025年份
1顶会引用
摘要
We study an infinite-armed bandit problem where actions' mean rewards are initially sampled from a reservoir distribution. Most prior works in this setting focused on stationary rewards (
问问这篇 Paper
智能体会读完全文。
Lune 把这篇 Paper 索引到了每一个公式,引用它的顶会 Paper 也一样。你提问,回答直接引用原文。
引用它的顶会 Paper1
问问它们各自怎么用它它引用的顶会 Paper5
- Smooth Non-stationary BanditsSu Jia, Qian Xie, Nathan Kallus, Peter I. FrazierICML 2023 · 被引用 14 次
- Unreasonable Effectiveness of Greedy Algorithms in Multi-Armed Bandit with Many ArmsMohsen Bayati, Nima Hamidi, Ramesh Johari, Khashayar KhosraviNeurIPS 2020 · 被引用 10 次
- Rotting Infinitely Many-Armed BanditsJung-Hun Kim, Milan Vojnovic, Se-Young YunICML 2022 · 被引用 5 次
- When Can We Track Significant Preference Shifts in Dueling Bandits?Joe Suk, Arpit AgarwalNeurIPS 2023 · 被引用 5 次
- An Adaptive Approach for Infinitely Many-armed Bandits under Generalized Rotting ConstraintsJung-Hun Kim, Milan Vojnovic, Se-Young YunNeurIPS 2024 · 被引用 2 次
相关 Paper
- Beyond the Best: Distribution Functional Estimation in Infinite-Armed BanditsYifei Wang, Tavor Z. Baharav, Yanjun Han, Jiantao Jiao 等NeurIPS 2022 · 被引用 2 次
- Bandits for BMO FunctionsTianyu Wang, Cynthia RudinICML 2020 · 被引用 5 次
- Rebounding Bandits for Modeling Satiation EffectsLiu Leqi, Fatma Kilinç-Karzan, Zachary C. Lipton, Alan L. MontgomeryNeurIPS 2021 · 被引用 30 次
- Dynamic Planning and Learning under Recovering RewardsDavid Simchi-Levi, Zeyu Zheng, Feng ZhuICML 2021 · 被引用 6 次
- A New Framework: Short-Term and Long-Term Returns in Stochastic Multi-Armed BanditAbdalaziz Sawwan, Jie WuINFOCOM 2023 · 被引用 11 次
