Decision-making in the face of uncertainty is a significant challenge in machine learning,
and the multi-armed bandit model is a commonly used framework to address it. This …

In the classical multi-armed bandit problem, instance-dependent algorithms attain improved
performance on" easy" problems with a gap between the best and second-best arm. Are …

Stochastic linear bandits with high-dimensional sparse features are a practical model for a
variety of domains, such as personalized medicine and online advertising. We derive a …

This paper studies federated linear contextual bandits under the notion of user-level
differential privacy (DP). We first introduce a unified federated bandits framework that can …

In this work, we develop linear bandit algorithms that automatically adapt to different
environments. By plugging a novel loss estimator into the optimization problem that …

Structural causal bandit provides a framework for online decision-making problems when
causal information is available. It models the stochastic environment with a structural causal …

In the stochastic contextual bandit setting, regret-minimizing algorithms have been
extensively researched, but their instance-minimizing best-arm identification counterparts …

This paper studies model-based bandit and reinforcement learning (RL) with nonlinear
function approximations. We propose to study convergence to approximate local maxima …

We consider the development of adaptive, instance-dependent algorithms for interactive
decision making (bandits, reinforcement learning, and beyond) that, rather than only …

Partial monitoring is a rich framework for sequential decision making under uncertainty that
generalizes many well known bandit models, including linear, combinatorial and dueling …