Summary of A Resource-constrained Stochastic Scheduling Algorithm For Homeless Street Outreach and Gleaning Edible Food, by Conor M. Artman et al.
A resource-constrained stochastic scheduling algorithm for homeless street outreach and gleaning edible food
by Conor M. Artman, Aditya Mate, Ezinne Nwankwo, Aliza Heching, Tsuyoshi Idé, Jiří Navrátil, Karthikeyan Shanmugam, Wei Sun, Kush R. Varshney, Lauri Goldkind, Gidi Kroch, Jaclyn Sawyer, Ian Watson
First submitted to arxiv on: 15 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computers and Society (cs.CY); Machine Learning (stat.ML)
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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 This paper presents an algorithmic solution to address resource constraints faced by social change organizations, such as Breaking Ground and Leket. The authors developed an estimation and optimization approach for partially-observed episodic restless bandits under k-step transitions. The proposed Thompson sampling with Markov chain recovery (via Stein variational gradient descent) algorithm outperforms baselines in solving the problems of both organizations. This work aims to create a flexible yet effective solution to overcome data science limitations for social good. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps organizations that want to make a difference, like Breaking Ground and Leket. They developed a special way to solve a problem they faced. It’s called restless bandits, which is hard because you don’t have all the information. They used an algorithm to help them make better decisions. This worked really well for both organizations. The goal was to create something that would truly help these groups. |
Keywords
* Artificial intelligence * Gradient descent * Optimization
