3 citations · 17 across the 22 of their papers we have counts for
22 papers
Decisions and Deployment: The Five-Year SAHELI Project (2020-2025) on Restless Multi-Armed Bandits for Improving Maternal and Child Health
Shresth Verma, Arpan Dasgupta, Neha Madhiwalla +2
Maternal and child health is a critical concern around the world. In many global health programs disseminating preventive care and health information, limited healthcare worker res…
Beyond Listenership: AI-Predicted Interventions Drive Improvements in Maternal Health Behaviours
Arpan Dasgupta, Sarvesh Gharat, Neha Madhiwalla +3
Automated voice calls with health information are a proven method for disseminating maternal and child health information among beneficiaries and are deployed in several programs a…
Learning to Call: A Field Trial of a Collaborative Bandit Algorithm for Improved Message Delivery in Mobile Maternal Health
Arpan Dasgupta, Mizhaan Maniyar, Awadhesh Srivastava +7
Mobile health (mHealth) programs utilize automated voice messages to deliver health information, particularly targeting underserved communities, demonstrating the effectiveness of…
LLM-based Agent Simulation for Maternal Health Interventions: Uncertainty Estimation and Decision-focused Evaluation
Sarah Martinson, Lingkai Kong, Cheol Woo Kim +2
Agent-based simulation is crucial for modeling complex human behavior, yet traditional approaches require extensive domain knowledge and large datasets. In data-scarce healthcare s…
Multilinguality in LLM-Designed Reward Functions for Restless Bandits: Effects on Task Performance and Fairness
Ambreesh Parthasarathy, Chandrasekar Subramanian, Ganesh Senrayan +4
Restless Multi-Armed Bandits (RMABs) have been successfully applied to resource allocation problems in a variety of settings, including public health. With the rapid development of…
Towards Foundation-model-based Multiagent System to Accelerate AI for Social Impact
Yunfan Zhao, Niclas Boehmer, Aparna Taneja +1
AI for social impact (AI4SI) offers significant potential for addressing complex societal challenges in areas such as public health, agriculture, education, conservation, and publi…
IRL for Restless Multi-Armed Bandits with Applications in Maternal and Child Health
Gauri Jain, Pradeep Varakantham, Haifeng Xu +3
Public health practitioners often have the goal of monitoring patients and maximizing patients' time spent in "favorable" or healthy states while being constrained to using limited…
Bayesian Collaborative Bandits with Thompson Sampling for Improved Outreach in Maternal Health Program
Arpan Dasgupta, Gagan Jain, Arun Suggala +3
Mobile health (mHealth) programs face a critical challenge in optimizing the timing of automated health information calls to beneficiaries. This challenge has been formulated as a…
The Bandit Whisperer: Communication Learning for Restless Bandits
Yunfan Zhao, Tonghan Wang, Dheeraj Nagaraj +2
Applying Reinforcement Learning (RL) to Restless Multi-Arm Bandits (RMABs) offers a promising avenue for addressing allocation problems with resource constraints and temporal dynam…
Improving Health Information Access in the World's Largest Maternal Mobile Health Program via Bandit Algorithms
Arshika Lalan, Shresth Verma, Paula Rodriguez Diaz +6
Harnessing the wide-spread availability of cell phones, many nonprofits have launched mobile health (mHealth) programs to deliver information via voice or text to beneficiaries in…
Preliminary Study of the Impact of AI-Based Interventions on Health and Behavioral Outcomes in Maternal Health Programs
Arpan Dasgupta, Niclas Boehmer, Neha Madhiwalla +4
Automated voice calls are an effective method of delivering maternal and child health information to mothers in underserved communities. One method to fight dwindling listenership…
Efficient Public Health Intervention Planning Using Decomposition-Based Decision-Focused Learning
Sanket Shah, Arun Suggala, Milind Tambe +1
The declining participation of beneficiaries over time is a key concern in public health programs. A popular strategy for improving retention is to have health workers `intervene'…
Evaluating the Effectiveness of Index-Based Treatment Allocation
Niclas Boehmer, Yash Nair, Sanket Shah +3
When resources are scarce, an allocation policy is needed to decide who receives a resource. This problem occurs, for instance, when allocating scarce medical resources and is ofte…
A Decision-Language Model (DLM) for Dynamic Restless Multi-Armed Bandit Tasks in Public Health
Nikhil Behari, Edwin Zhang, Yunfan Zhao +3
Restless multi-armed bandits (RMAB) have demonstrated success in optimizing resource allocation for large beneficiary populations in public health settings. Unfortunately, RMAB mod…
Context in Public Health for Underserved Communities: A Bayesian Approach to Online Restless Bandits
Biyonka Liang, Lily Xu, Aparna Taneja +2
Public health programs often provide interventions to encourage program adherence, and effectively allocating interventions is vital for producing the greatest overall health outco…
Analyzing and Predicting Low-Listenership Trends in a Large-Scale Mobile Health Program: A Preliminary Investigation
Arshika Lalan, Shresth Verma, Kumar Madhu Sudan +4
Mobile health programs are becoming an increasingly popular medium for dissemination of health information among beneficiaries in less privileged communities. Kilkari is one of the…
Towards a Pretrained Model for Restless Bandits via Multi-arm Generalization
Yunfan Zhao, Nikhil Behari, Edward Hughes +5
Restless multi-arm bandits (RMABs), a class of resource allocation problems with broad application in areas such as healthcare, online advertising, and anti-poaching, have recently…
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…
Improved Policy Evaluation for Randomized Trials of Algorithmic Resource Allocation
Aditya Mate, Bryan Wilder, Aparna Taneja +1
We consider the task of evaluating policies of algorithmic resource allocation through randomized controlled trials (RCTs). Such policies are tasked with optimizing the utilization…
Decision-Focused Evaluation: Analyzing Performance of Deployed Restless Multi-Arm Bandits
Paritosh Verma, Shresth Verma, Aditya Mate +2
Restless multi-arm bandits (RMABs) is a popular decision-theoretic framework that has been used to model real-world sequential decision making problems in public health, wildlife c…
ADVISER: AI-Driven Vaccination Intervention Optimiser for Increasing Vaccine Uptake in Nigeria
Vineet Nair, Kritika Prakash, Michael Wilbur +7
More than 5 million children under five years die from largely preventable or treatable medical conditions every year, with an overwhelmingly large proportion of deaths occurring i…
Scalable Decision-Focused Learning in Restless Multi-Armed Bandits with Application to Maternal and Child Health
Kai Wang, Shresth Verma, Aditya Mate +5
This paper studies restless multi-armed bandit (RMAB) problems with unknown arm transition dynamics but with known correlated arm features. The goal is to learn a model to predict…