The recent emergence of reinforcement learning (RL) has created a demand for robust
statistical inference methods for the parameter estimates computed using these algorithms …

Clustering is a ubiquitous task in data science. Compared to the commonly used k-means
clustering, k-medoids clustering requires the cluster centers to be actual data points and …

There is a growing interest in using reinforcement learning (RL) to personalize sequences of
treatments in digital health to support users in adopting healthier behaviors. Such sequential …

In this paper we propose the first multi-armed bandit algorithm based on re-sampling that
achieves asymptotically optimal regret simultaneously for different families of arms (namely …

We propose a new bootstrap-based online algorithm for stochastic linear bandit problems.
The key idea is to adopt residual bootstrap exploration, in which the agent estimates the next …

Recommender systems play a crucial role in internet economies by connecting users with
relevant products or services. However, designing effective recommender systems faces two …

Despite the great interest in the bandit problem, designing efficient algorithms for complex
models remains challenging, as there is typically no analytical way to quantify uncertainty. In …

Abstract Upper Confidence Bound (UCB) methods are one of the most effective methods in
dealing with the exploration-exploitation trade-off in online decision-making problems. The …

The stochastic multi-arm bandit problem has been extensively studied under standard
assumptions on the arm's distribution (eg bounded with known support, exponential family …

In non-asymptotic learning, variance-type parameters of sub-Gaussian distributions are of
paramount importance. However, directly estimating these parameters using the empirical …