In this paper, we introduce an innovative approach to handling the multi-armed bandit (MAB)
problem in non-stationary environments, harnessing the predictive power of large language …

In mixed-initiative co-creation tasks, wherein a human and a machine jointly create items, it
is important to provide multiple relevant suggestions to the designer. Quality-diversity …

Blockchains have become the catalyst for a growing movement to create a more
decentralized Internet. A fundamental operation of applications in a decentralized Internet is …

Causality is gaining more and more attention in the machine learning community and
consequently also in recommender systems research. The limitations of learning offline from …

Insufficient knowledge about a new bug or a new developer, in the context of
recommendations done in software bug repositories (SBR) mining, impacts the …

In teleoperation of redundant robotic manipulators, translating an operator's end effector
motion command to joint space can be a tool for maintaining feasible and precise robot …

We explore the promises and challenges of employing sequential decision-making
algorithms--such as bandits, reinforcement learning, and active learning--in law and public …

A phylogenetic graph search relies on a large number of highly parameterized search
procedures (eg branch‐swapping, perturbation, simulated annealing, genetic algorithm) …

Recommender systems are commonly based on a multi-armed bandit model. This model
should be carefully tested because it affects the users, but it is technically complicated …

Task allocation is a critical issue of spatial crowdsourcing. Although the batching strategy
performs better than the real-time matching mode, it still has the following two drawbacks:(1) …