measure compression

This is a talk presented at AIS conference 2026 held Sept 21-24, Italy.

Talk materials: the slides of the presentation.

Abstract. Algorithms for the Multi-Armed Bandit (MAB) problem play a central role in sequential decision-making and have been extensively explored both
theoretically and numerically. While most classical approaches aim to identify the arm with the highest expected reward, we focus on a risk-aware setting where
the goal is to select the arm with the lowest variance, favoring stability over potentially high but uncertain returns. To model the decision process, we consider
a softmax parameterization of the policy; we propose a new algorithm to select the minimal variance (or minimal risk) arm and prove its convergence under natural conditions. The algorithm constructs an unbiased estimate of the objective by using two independent draws from the selected arm’s distribution. We provide numerical experiments that illustrate the practical behavior of these algorithms and offer guidance on implementation choices. The setting also covers general risk-aware problems where there is a trade-off between maximizing the average reward and minimizing its variance.

This is a talk presented at DETERMINISTIC AND STOCHASTIC PROCESSES conference held at the Apollonia University Iasi together with the Romanian Acadmy of Science (Iasi branch), Feb 25-28 2026.

Talk materials: the slides of the presentation.

This is a talk presented at Nanmat conference held Nov 3-6 2026 at ICTP, Cluj.

Talk materials: the slides of the presentation. and here the Youtube video version.

This is a talk presented at Nanmat conference held Nov 4-7 2024 at ICTP, Cluj.

Talk materials: the slides of the presentation. and here the Youtube video version.

This is a talk presented at Nanmath conference held Nov 6-9 2023 at ICTP, Cluj..

Talk materials (click to open or download): the Slides of the presentation, the ArXiv preprint and the Youtube VIDEO.