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Found 15 papers, 2 papers with code
Efficient Public Health Intervention Planning Using Decomposition-Based Decision-Focused Learning
However, the availability and time of these health workers are limited resources.
A Decision-Language Model (DLM) for Dynamic Restless Multi-Armed Bandit Tasks in Public Health
Efforts to reduce maternal mortality rate, a key UN Sustainable Development target (SDG Target 3. 1), rely largely on preventative care programs to spread critical health information to high-risk populations.
Evaluating the Effectiveness of Index-Based Treatment Allocation
When resources are scarce, an allocation policy is needed to decide who receives a resource.
A Bayesian Approach to Online Learning for Contextual Restless Bandits with Applications to Public Health
Restless multi-armed bandits (RMABs) are used to model sequential resource allocation in public health intervention programs.
Analyzing and Predicting Low-Listenership Trends in a Large-Scale Mobile Health Program: A Preliminary Investigation
Mobile health programs are becoming an increasingly popular medium for dissemination of health information among beneficiaries in less privileged communities.
Towards a Pretrained Model for Restless Bandits via Multi-arm Generalization
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 been studied from a multi-agent reinforcement learning perspective.
Limited Resource Allocation in a Non-Markovian World: The Case of Maternal and Child Healthcare
The success of many healthcare programs depends on participants' adherence.
Improved Policy Evaluation for Randomized Trials of Algorithmic Resource Allocation
We consider the task of evaluating policies of algorithmic resource allocation through randomized controlled trials (RCTs).
Decision-Focused Evaluation: Analyzing Performance of Deployed Restless Multi-Arm Bandits
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 conservation, communication systems, and beyond.
Optimistic Whittle Index Policy: Online Learning for Restless Bandits
Restless multi-armed bandits (RMABs) extend multi-armed bandits to allow for stateful arms, where the state of each arm evolves restlessly with different transitions depending on whether that arm is pulled.
ADVISER: AI-Driven Vaccination Intervention Optimiser for Increasing Vaccine Uptake in Nigeria
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 in under-developed countries with low vaccination uptake.
Scalable Decision-Focused Learning in Restless Multi-Armed Bandits with Application to Maternal and Child Health
To address this shortcoming, we propose a novel approach for decision-focused learning in RMAB that directly trains the predictive model to maximize the Whittle index solution quality.
Facilitating human-wildlife cohabitation through conflict prediction
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 (animal and human) and livelihoods (economic).
Field Study in Deploying Restless Multi-Armed Bandits: Assisting Non-Profits in Improving Maternal and Child Health
Our second major contribution is evaluation of our RMAB system in collaboration with an NGO, via a real-world service quality improvement study.
City-Scale Change Detection in Cadastral 3D Models Using Images
In this paper, we propose a method to detect changes in the geometry of a city using panoramic images captured by a car driving around the city.
