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

arXiv 2403.10638 ↗First posted 15 Mar 2024cs.LG cs.CY stat.MLcited by 0

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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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 21 of its 54 references that are in the map. First posted on arXiv 15 Mar 2024.

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References · 21

  1. 2016Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithmcited by 498
  2. 2018AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Biascited by 319
  3. 2017Stein Variational Gradient Descent as Gradient Flowcited by 149
  4. 2016Why is Posterior Sampling Better than Optimism for Reinforcement Learning?cited by 116
  5. 2015Measuring Sample Quality with Stein's Methodcited by 94
  6. 2017Learning Unknown Markov Decision Processes: A Thompson Sampling Approachcited by 65
  7. 2020Collapsing Bandits and Their Application to Public Health Interventionscited by 47
  8. 2020Whittle index based Q-learning for restless bandits with average rewardcited by 43
  9. 2021Field Study in Deploying Restless Multi-Armed Bandits: Assisting Non-Profits in Improving Maternal and Child Healthcited by 43
  10. 2019Regret Bounds for Thompson Sampling in Episodic Restless Bandit Problemscited by 34
  11. 2020Artificial Intelligence for Social Good: A Surveycited by 29
  12. 2022A Finite-Particle Convergence Rate for Stein Variational Gradient Descentcited by 22
9 more
  1. 2021A Framework for Fairness: A Systematic Review of Existing Fair AI Solutionscited by 18
  2. 2022Optimistic Whittle Index Policy: Online Learning for Restless Banditscited by 18
  3. 2019Thompson Sampling in Non-Episodic Restless Banditscited by 15
  4. 2021Planning to Fairly Allocate: Probabilistic Fairness in the Restless Bandit Settingcited by 12
  5. 2022On learning Whittle index policy for restless bandits with scalable regretcited by 9
  6. 2022Posterior Coreset Construction with Kernelized Stein Discrepancy for Model-Based Reinforcement Learningcited by 7
  7. 2023Provably Fast Finite Particle Variants of SVGD via Virtual Particle Stochastic Approximationcited by 6
  8. 2019Open Platforms for Artificial Intelligence for Social Good: Common Patterns as a Pathway to True Impactcited by 1
  9. 2020Screening for an Infectious Disease as a Problem in Stochastic Controlcited by 1

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