The rapid changes in the finance industry due to the increasing amount of data have
revolutionized the techniques on data processing and data analysis and brought new …

In this big data era, the use of large dataset in conjunction with machine learning (ML) has
been increasingly popular in both industry and academia. In recent times, the field of …

Throughout the coronavirus disease 2019 (COVID-19) pandemic, countries have relied on a
variety of ad hoc border control protocols to allow for non-essential travel while safeguarding …

This paper presents a novel federated linear contextual bandits model, where individual
clients face different $ K $-armed stochastic bandits coupled through common global …

Diffusion models excel at modeling complex data distributions, including those of images,
proteins, and small molecules. However, in many cases, our goal is to model parts of the …

We consider the general (stochastic) contextual bandit problem under the realizability
assumption, that is, the expected reward, as a function of contexts and actions, belongs to a …

We study the hidden-action principal-agent problem in an online setting. In each round, the
principal posts a contract that specifies the payment to the agent based on each outcome …

Multi-armed bandit has been well-known for its efficiency in online decision-making in terms
of minimizing the loss of the participants' welfare during experiments (ie, the regret). In …

We take initial steps in studying PAC-MDP algorithms with limited adaptivity, that is,
algorithms that change its exploration policy as infrequently as possible during regret …

As bandit algorithms are increasingly utilized in scientific studies and industrial applications,
there is an associated increasing need for reliable inference methods based on the resulting …