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 …

We present methods for online linear optimization that take advantage of benign (as
opposed to worst-case) sequences. Specifically if the sequence encountered by the learner …

We study the problem of combining multiple bandit algorithms (that is, online learning
algorithms with partial feedback) with the goal of creating a master algorithm that performs …

In this paper we propose efficient algorithms for solving constrained online convex
optimization problems. Our motivation stems from the observation that most algorithms …

In this paper, online convex optimization problem with time-varying constraints is studied
from the perspective of an agent taking sequential actions. Both the objective function and …

Distributed bandit online convex optimization with time-varying coupled inequality
constraints is considered, motivated by a repeated game between a group of learners and …

This work studies the problem of learning episodic Markov Decision Processes with known
transition and bandit feedback. We develop the first algorithm with a``best-of-both …

In constrained convex optimization, existing interior point methods do not scale well with the
dimension of the ambient space. Alternative approaches such as Projected Gradient …

We design differentially private algorithms for the problem of online linear optimization in the
full information and bandit settings with optimal $ O (T^{0.5}) $ regret bounds. In the full …

We develop a new approach to obtaining high probability regret bounds for online learning
with bandit feedback against an adaptive adversary. While existing approaches all require …