# A resource-constrained stochastic scheduling algorithm for homeless street outreach and gleaning edible food

arXiv: https://arxiv.org/abs/2403.10638 · HTML: https://mlsanity.com/paper/2403.10638/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 54 references that are in the map.
- First posted on arXiv 15 Mar 2024.

## Builds on (most cited first, 21 of 21)

- [Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm](https://mlsanity.com/paper/1608.04471/) (2016)
- [AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias](https://mlsanity.com/paper/1810.01943/) (2018)
- [Stein Variational Gradient Descent as Gradient Flow](https://mlsanity.com/paper/1704.07520/) (2017)
- [Why is Posterior Sampling Better than Optimism for Reinforcement Learning?](https://mlsanity.com/paper/1607.00215/) (2016)
- [Measuring Sample Quality with Stein's Method](https://mlsanity.com/paper/1506.03039/) (2015)
- [Learning Unknown Markov Decision Processes: A Thompson Sampling Approach](https://mlsanity.com/paper/1709.04570/) (2017)
- [Collapsing Bandits and Their Application to Public Health Interventions](https://mlsanity.com/paper/2007.04432/) (2020)
- [Whittle index based Q-learning for restless bandits with average reward](https://mlsanity.com/paper/2004.14427/) (2020)
- [Field Study in Deploying Restless Multi-Armed Bandits: Assisting Non-Profits in Improving Maternal and Child Health](https://mlsanity.com/paper/2109.08075/) (2021)
- [Regret Bounds for Thompson Sampling in Episodic Restless Bandit Problems](https://mlsanity.com/paper/1905.12673/) (2019)
- [Artificial Intelligence for Social Good: A Survey](https://mlsanity.com/paper/2001.01818/) (2020)
- [A Finite-Particle Convergence Rate for Stein Variational Gradient Descent](https://mlsanity.com/paper/2211.09721/) (2022)
- [Optimistic Whittle Index Policy: Online Learning for Restless Bandits](https://mlsanity.com/paper/2205.15372/) (2022)
- [A Framework for Fairness: A Systematic Review of Existing Fair AI Solutions](https://mlsanity.com/paper/2112.05700/) (2021)
- [Thompson Sampling in Non-Episodic Restless Bandits](https://mlsanity.com/paper/1910.05654/) (2019)
- [Planning to Fairly Allocate: Probabilistic Fairness in the Restless Bandit Setting](https://mlsanity.com/paper/2106.07677/) (2021)
- [On learning Whittle index policy for restless bandits with scalable regret](https://mlsanity.com/paper/2202.03463/) (2022)
- [Posterior Coreset Construction with Kernelized Stein Discrepancy for Model-Based Reinforcement Learning](https://mlsanity.com/paper/2206.01162/) (2022)
- [Provably Fast Finite Particle Variants of SVGD via Virtual Particle Stochastic Approximation](https://mlsanity.com/paper/2305.17558/) (2023)
- [Open Platforms for Artificial Intelligence for Social Good: Common Patterns as a Pathway to True Impact](https://mlsanity.com/paper/1905.11519/) (2019)
- [Screening for an Infectious Disease as a Problem in Stochastic Control](https://mlsanity.com/paper/2011.00635/) (2020)

## Abstract (arXiv)

We developed a common algorithmic solution addressing the problem of resource-constrained outreach encountered by social change organizations with different missions and operations: Breaking Ground -- an organization that helps individuals experiencing homelessness in New York transition to permanent housing and Leket -- the national food bank of Israel that rescues food from farms and elsewhere to feed the hungry. Specifically, we developed an estimation and optimization approach for partially-observed episodic restless bandits under k-step transitions. The results show that our Thompson sampling with Markov chain recovery (via Stein variational gradient descent) algorithm significantly outperforms baselines for the problems of both organizations. We carried out this work in a prospective manner with the express goal of devising a flexible-enough but also useful-enough solution that can help overcome a lack of sustainable impact in data science for social good.