Which LLM to Play? Convergence-Aware Online Model Selection with Time-Increasing Bandits
Author:
Affiliation:
1. Shanghai Jiao Tong University & University of Michigan, Shanghai, China
2. Shanghai Jiao Tong University, Shanghai, China
3. Adobe Research, San Jose, CA, USA
4. Tsinghua University, Beijing, China
Funder
National Natural Science Foundation of China
National Key Research and Development Program of China
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3589334.3645420
Reference45 articles.
1. The non-stationary stochastic multi-armed bandit problem
2. Peter Auer, Pratik Gajane, and Ronald Ortner. 2019. Adaptively tracking the best bandit arm with an unknown number of distribution changes. In Conference on Learning Theory. PMLR, 138--158.
3. Omar Besbes, Yonatan Gur, and Assaf Zeevi. 2014. Stochastic multi-armed-bandit problem with non-stationary rewards. Advances in neural information processing systems, Vol. 27 (2014), 199--207.
4. Multi-armed bandit problem with known trend
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