Pure exploration (aka active testing) is the fundamental task of sequentially gathering
information to answer a query about a stochastic environment. Good algorithms make few …

In fixed budget bandit identification, an algorithm sequentially observes samples from
several distributions up to a given final time. It then answers a query about the set of …

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

This paper proposes near-optimal algorithms for the pure-exploration linear bandit problem
in the fixed confidence and fixed budget settings. Leveraging ideas from the theory of …

In sparse linear bandits, a learning agent sequentially selects an action from a fixed action
set and receives reward feedback, and the reward function depends linearly on a few …

We propose EB-TC $\varepsilon $, a novel sampling rule for $\varepsilon $-best arm
identification in stochastic bandits. It is the first instance of Top Two algorithm analyzed for …

Simple regret is a natural and parameter-free performance criterion for pure exploration in
multi-armed bandits yet is less popular than the probability of missing the best arm or an …

We propose an alternative framework to existing setups for controlling false alarms when
multiple A/B tests are run over time. This setup arises in many practical applications, eg …

Active learning methods have shown great promise in reducing the number of samples
necessary for learning. As automated learning systems are adopted into real-time, real …

A Top Two sampling rule for bandit identification is a method which selects the next arm to
sample from among two candidate arms, a leader and a challenger. Due to their simplicity …