Multi-armed bandits a simple but very powerful framework for algorithms that make
decisions over time under uncertainty. An enormous body of work has accumulated over the …

In performative prediction, the deployment of a predictive model triggers a shift in the data
distribution. As these shifts are typically unknown ahead of time, the learner needs to deploy …

Lipschitz bandit is a variant of stochastic bandits that deals with a continuous arm set
defined on a metric space, where the reward function is subject to a Lipschitz constraint. In …

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

We propose a hierarchical version of dual averaging for zeroth-order online non-convex
optimization {–} ie, learning processes where, at each stage, the optimizer is facing an …

Optimizing network protocols is crucial for improving application performance. Recent
research works use multi-armed bandit (MAB) online learning methods to address network …

The fault informative frequency band searching is crucial to envelope analysis-based
machine fault diagnosis. Its success often depends on effective filters. However, existing …

In this paper, we investigate Thompson sampling-based sequential block elimination
approaches for dynamic assignment problems in a pure-exploration Multi-Armed Bandit …

This work studies the repeated principal-agent problem from an online learning perspective.
The principal's goal is to learn the optimal contract that maximizes her utility through …

We consider the problem of defense against reward-poisoning attacks in reinforcement
learning and formulate it as a game in T rounds between a defender and an adaptive …