Online learning represents a family of machine learning methods, where a learner attempts
to tackle some predictive (or any type of decision-making) task by learning from a sequence …

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 …

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 …

As optimizers are critical to the performances of neural networks, every year a large number
of papers innovating on the subject are published. However, while most of these …

Multi-armed bandit problems are the most basic examples of sequential decision problems
with an exploration-exploitation trade-off. This is the balance between staying with the option …

A stochastic combinatorial semi-bandit is an online learning problem where at each step a
learning agent chooses a subset of ground items subject to constraints, and then observes …

This paper studies the evaluation of policies that recommend an ordered set of items (eg, a
ranking) based on some context---a common scenario in web search, ads, and …

In the past few years, differential privacy has become a standard concept in the area of
privacy. One of the most important problems in this field is to answer queries while …

We study the {\em combinatorial pure exploration (CPE)} problem in the stochastic multi-
armed bandit setting, where a learner explores a set of arms with the objective of identifying …

This paper investigates stochastic and adversarial combinatorial multi-armed bandit
problems. In the stochastic setting under semi-bandit feedback, we derive a problem-specific …