Summary of Bayesian Collaborative Bandits with Thompson Sampling For Improved Outreach in Maternal Health Program, by Arpan Dasgupta et al.
Bayesian Collaborative Bandits with Thompson Sampling for Improved Outreach in Maternal Health Program
by Arpan Dasgupta, Gagan Jain, Arun Suggala, Karthikeyan Shanmugam, Milind Tambe, Aparna Taneja
First submitted to arxiv on: 28 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Bayesian approach uses Thompson Sampling to optimize the timing of automated health information calls for mobile health programs. By leveraging prior information through Gibbs sampling, it enables faster convergence and improves upon state-of-the-art baselines by 16% in call reduction and 47% compared to a deployed random policy. This efficiency gain translates to an increase in program capacity by 0.5-1.4 million beneficiaries, granting them access to vital ante-natal and post-natal care information. Additionally, the approach shows improvements in beneficiary retention by 7% and 29% compared to state-of-the-art and deployed baselines, respectively. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Mobile health programs want to send important health messages at the right time. They use a special algorithm called Thompson Sampling that learns from experience. The new method is better than old methods because it uses information from before to make decisions faster. This means they can reach more people (0.5-1.4 million) with important health info, helping them get prenatal and post-natal care. |
