Restless multi-armed bandits (RMAB) have demonstrated success in optimizing resource
allocation for large beneficiary populations in public health settings. Unfortunately, RMAB …

Game theory provides a mathematical way to study the interaction between multiple
decision makers. However, classical game-theoretic analysis is limited in scalability due to …

For public health programs with limited resources, the ability to predict how behaviors
change over time and in response to interventions is crucial for deciding when and to whom …

" Human-aware" has become a popular keyword used to describe a particular class of AI
systems that are designed to work and interact with humans. While there exists a surprising …

Resumen La iniciativa Equidad y Acceso en Algoritmos, Mecanismos y Optimización
(EAAMO) utiliza investigación interdisciplinaria para abordar desafíos globales, enfatizando …

Motivated by applications such as machine repair, project monitoring, and anti-poaching
patrol scheduling, we study intervention planning of stochastic processes under resource …

Online restless bandits extend classic contextual bandits by incorporating state transitions
and budget constraints, representing each agent as a Markov Decision Process (MDP). This …

High dimensional sparse linear bandits serve as an efficient model for sequential decision-
making problems (eg personalized medicine), where high dimensional features (eg …

We define a new subclass of the restless multi-armed bandit framework, that we name
Adherence Bandits, designed to capture the dynamics prevalent in many public health …