We consider a price-based revenue management problem with finite reusable resources
over a finite time horizon T. Customers arrive following a price-dependent Poisson process …

Traditionally, when recommender systems are formalized as multi-armed bandits, the policy
of the recommender system influences the rewards accrued, but not the length of interaction …

We study a decentralized multi-agent multi-armed bandit problem in which multiple clients
are connected by time dependent random graphs provided by an environment. The reward …

In this work, we address the problem of long-distance navigation for battery electric vehicles
(BEVs), where one or more charging sessions are required to reach the intended …

In algorithm optimization in reinforcement learning, how to deal with the exploration-
exploitation dilemma is particularly important. Multi-armed bandit problem can optimize the …

We study decentralized multi-agent multi-armed bandits in fully heavy-tailed settings, where
clients communicate over sparse random graphs with heavy-tailed degree distributions and …

Multi-armed Bandit (MAB) is a classical online sequential decision-making paradigm which
has wide applications in various areas, such as healthcare, e-commerce, advertisement and …

The primary focus of this dissertation is to develop adaptive optimization and learning
models and algorithms for decision-making problems under uncertainty arising in service …

In this work, we address the problem of long-distance navigation for battery electric vehicles
(BEVs), where one or more charging sessions are required to reach the intended …

Building artificial intelligence systems from a human-centered perspective is increasingly
urgent, as large-scale machine learning systems ranging from personalized recommender …