# Partial Likelihood Thompson Sampling

arXiv: https://arxiv.org/abs/2203.00820 · HTML: https://mlsanity.com/paper/2203.00820/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 48 references that are in the map.
- First posted on arXiv 2 Mar 2022.

## Builds on (most cited first, 11 of 11)

- [A Tutorial on Thompson Sampling](https://mlsanity.com/paper/1707.02038/) (2017)
- [An Information-Theoretic Analysis of Thompson Sampling](https://mlsanity.com/paper/1403.5341/) (2014)
- [Online Learning under Delayed Feedback](https://mlsanity.com/paper/1306.0686/) (2013)
- [Simple Bayesian Algorithms for Best Arm Identification](https://mlsanity.com/paper/1602.08448/) (2016)
- [Constrained Bayesian Optimization with Noisy Experiments](https://mlsanity.com/paper/1706.07094/) (2017)
- [Stochastic Bandit Models for Delayed Conversions](https://mlsanity.com/paper/1706.09186/) (2017)
- [Nonstochastic Multiarmed Bandits with Unrestricted Delays](https://mlsanity.com/paper/1906.00670/) (2019)
- [Stochastic bandits with arm-dependent delays](https://mlsanity.com/paper/2006.10459/) (2020)
- [Stochastic Multi-Armed Bandits with Unrestricted Delay Distributions](https://mlsanity.com/paper/2106.02436/) (2021)
- [Best arm identification in multi-armed bandits with delayed feedback](https://mlsanity.com/paper/1803.10937/) (2018)
- [Online Algorithms For Parameter Mean And Variance Estimation In Dynamic Regression Models](https://mlsanity.com/paper/1605.05697/) (2016)

## Built on by (most cited first, 3 of 3)

- [Impatient Bandits: Optimizing Recommendations for the Long-Term Without Delay](https://mlsanity.com/paper/2307.09943/) (2023)
- [Reinforcement Learning in the Real World: A Survey of Statistical Challenges and Future Directions](https://mlsanity.com/paper/2601.15353/) (2026)
- [Admissibility of Completely Randomized Trials: A Large-Deviation Approach](https://mlsanity.com/paper/2506.05329/) (2025)

## 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.