activity
20202026
most citedQ-Learning Lagrange Policies for Multi-Action Restless Bandits

12 citations · 46 across the 17 of their papers we have counts for

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5 papers · 1 filter

cs.AI2026

Online Allocation with Unknown Shared Supply

Tzeh Yuan Neoh, Davin Choo, Mengchu Yue +1

Many real-world resource allocation systems, such as humanitarian logistics and vaccine distribution, must preposition limited supply across multiple locations before demand is rea…

cs.AI20236 cited

Reflections from the Workshop on AI-Assisted Decision Making for Conservation

Lily Xu, Esther Rolf, Sara Beery +21

In this white paper, we synthesize key points made during presentations and discussions from the AI-Assisted Decision Making for Conservation workshop, hosted by the Center for Res…

cs.AI2023

Limited Resource Allocation in a Non-Markovian World: The Case of Maternal and Child Healthcare

Panayiotis Danassis, Shresth Verma, Jackson A. Killian +2

The success of many healthcare programs depends on participants' adherence. We consider the problem of scheduling interventions in low resource settings (e.g., placing timely suppo…

cs.AI2022

Ranked Prioritization of Groups in Combinatorial Bandit Allocation

Lily Xu, Arpita Biswas, Fei Fang +1

Preventing poaching through ranger patrols protects endangered wildlife, directly contributing to the UN Sustainable Development Goal 15 of life on land. Combinatorial bandits have…

cs.AI2021

Facilitating human-wildlife cohabitation through conflict prediction

Susobhan Ghosh, Pradeep Varakantham, Aniket Bhatkhande +6

With increasing world population and expanded use of forests as cohabited regions, interactions and conflicts with wildlife are increasing, leading to large-scale loss of lives (an…