This paper studies the multiobjective bandit problem under lexicographic ordering, wherein
the learner aims to simultaneously maximize $ m $ objectives hierarchically. The only …

This letter considers a cooperative decision-making method for an adversarial bandit
problem on open multi-agent systems. In an open multi-agent system, the network …

Online convex optimization (OCO) is a widely used framework in online learning. In each
round, the learner chooses a decision in a convex set and an adversary chooses a convex …

Modern decision-making scenarios often involve data that is both high-dimensional and rich
in higher-order contextual information, where existing bandits algorithms fail to generate …

Online decision-making can be formulated as the popular stochastic multi-armed bandit
problem where a learner makes decisions (or takes actions) to maximize cumulative …

Interactions between a learner and an environment arise in a variety of domains, ranging
from online recommendations (eg, Spotify) to control of physical dynamical systems (eg …