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TS-UCB: Improving on Thompson Sampling With Little to No Additional
  Computation
v1v2 (latest)

TS-UCB: Improving on Thompson Sampling With Little to No Additional Computation

International Conference on Artificial Intelligence and Statistics (AISTATS), 2020
11 June 2020
Jackie Baek
Vivek F. Farias
ArXiv (abs)PDFHTML

Papers citing "TS-UCB: Improving on Thompson Sampling With Little to No Additional Computation"

5 / 5 papers shown
A Unified Confidence Sequence for Generalized Linear Models, with Applications to Bandits
A Unified Confidence Sequence for Generalized Linear Models, with Applications to Bandits
Junghyun Lee
Se-Young Yun
Kwang-Sung Jun
716
23
0
19 Jul 2024
Optimal Design for Human Preference Elicitation
Optimal Design for Human Preference Elicitation
Subhojyoti Mukherjee
Anusha Lalitha
Kousha Kalantari
Aniket Deshmukh
Ge Liu
Yifei Ma
Branislav Kveton
428
0
0
22 Apr 2024
TS-RSR: A provably efficient approach for batch Bayesian Optimization
TS-RSR: A provably efficient approach for batch Bayesian Optimization
Tongzheng Ren
Na Li
555
2
0
07 Mar 2024
Simple Modification of the Upper Confidence Bound Algorithm by
  Generalized Weighted Averages
Simple Modification of the Upper Confidence Bound Algorithm by Generalized Weighted AveragesPLoS ONE (PLoS ONE), 2023
Nobuhito Manome
Shuji Shinohara
Ung-il Chung
277
9
0
28 Aug 2023
Optimistic Whittle Index Policy: Online Learning for Restless Bandits
Optimistic Whittle Index Policy: Online Learning for Restless BanditsAAAI Conference on Artificial Intelligence (AAAI), 2022
Kai Wang
Lily Xu
Aparna Taneja
Milind Tambe
245
28
0
30 May 2022
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