Partial Likelihood Thompson Sampling
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Abstract (arXiv)
We consider the problem of deciding how best to target and prioritize existing vaccines that may offer protection against new variants of an infectious disease. Sequential experiments are a promising approach; however, challenges due to delayed feedback and the overall ebb and flow of disease prevalence make available methods inapplicable for this task. We present a method, partial likelihood Thompson sampling, that can handle these challenges. Our method involves running Thompson sampling with belief updates determined by partial likelihood each time we observe an event. To test our approach, we ran a semi-synthetic experiment based on 200 days of COVID-19 infection data in the US.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 11 of its 48 references that are in the map. First posted on arXiv 2 Mar 2022.
Builds on
11 of 48 references.
References · 11
- 2017A Tutorial on Thompson Samplingcited by 420
- 2014An Information-Theoretic Analysis of Thompson Samplingcited by 178
- 2013Online Learning under Delayed Feedbackcited by 137
- 2016Simple Bayesian Algorithms for Best Arm Identificationcited by 116
- 2017Constrained Bayesian Optimization with Noisy Experimentscited by 103
- 2017Stochastic Bandit Models for Delayed Conversionscited by 53
- 2019Nonstochastic Multiarmed Bandits with Unrestricted Delayscited by 46
- 2020Stochastic bandits with arm-dependent delayscited by 36
- 2021Stochastic Multi-Armed Bandits with Unrestricted Delay Distributionscited by 33
- 2018Best arm identification in multi-armed bandits with delayed feedbackcited by 29
- 2016Online Algorithms For Parameter Mean And Variance Estimation In Dynamic Regression Modelscited by 1
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Citing papers per year: 2023: 1; 2024: 0; 2025: 1; 2026: 1.
3 papers.