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 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 …

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 …

Traditional online algorithms encapsulate decision making under uncertainty, and give ways
to hedge against all possible future events, while guaranteeing a nearly optimal solution, as …

In this work we study the problem of using machine-learned predictions to improve
performance of online algorithms. We consider two classical problems, ski rental and non …

The key challenge in multiagent learning is learning a best response to the behaviour of
other agents, which may be non-stationary: if the other agents adapt their strategy as well …

Online algorithms are a hallmark of worst case optimization under uncertainty. On the other
hand, in practice, the input is often far from worst case, and has some predictable …

We introduce a new model of stochastic bandits with adversarial corruptions which aims to
capture settings where most of the input follows a stochastic pattern but some fraction of it …

We study the stochastic multi-armed bandits problem in the presence of adversarial
corruption. We present a new algorithm for this problem whose regret is nearly optimal …

We study the linear contextual bandit problem in the presence of adversarial corruption,
where the reward at each round is corrupted by an adversary, and the corruption level (ie …