Non-stationary Bandits with Heavy Tail
In this study, we investigate the performance of multi-armed bandit algorithms in environments characterized by heavytailed and non-stationary reward distributions, a setting that deviates from the conventional risk-neutral and sub- Gaussian assumptions.
Authors
- Weici Pan (ORCID: https://orcid.org/0000-0001-9231-8785)
- Zhenhua Liu (ORCID: https://orcid.org/0000-0002-9477-1791)
Institutions
- Stony Brook University (US)
Publication Details
- Journal
- ACM SIGMETRICS Performance Evaluation Review
- Published
- 2024-09-05
- DOI
- https://doi.org/10.1145/3695411.3695424
- Primary Topic
- Advanced Bandit Algorithms Research
- Type
- article
- Field-Weighted Citation Impact
- 0.00