15 citations · 28 across the 10 of their papers we have counts for
5 papers · 1 filter
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…
Field Study in Deploying Restless Multi-Armed Bandits: Assisting Non-Profits in Improving Maternal and Child Health
Aditya Mate, Lovish Madaan, Aparna Taneja +6
The widespread availability of cell phones has enabled non-profits to deliver critical health information to their beneficiaries in a timely manner. This paper describes our work t…
Selective Intervention Planning using Restless Multi-Armed Bandits to Improve Maternal and Child Health Outcomes
Siddharth Nishtala, Lovish Madaan, Aditya Mate +11
India has a maternal mortality ratio of 113 and child mortality ratio of 2830 per 100,000 live births. Lack of access to preventive care information is a major contributing factor…
Efficient Algorithms for Finite Horizon and Streaming Restless Multi-Armed Bandit Problems
Aditya Mate, Arpita Biswas, Christoph Siebenbrunner +2
We propose Streaming Bandits, a Restless Multi Armed Bandit (RMAB) framework in which heterogeneous arms may arrive and leave the system after staying on for a finite lifetime. Str…
Collapsing Bandits and Their Application to Public Health Interventions
Aditya Mate, Jackson A. Killian, Haifeng Xu +2
We propose and study Collpasing Bandits, a new restless multi-armed bandit (RMAB) setting in which each arm follows a binary-state Markovian process with a special structure: when…