Self-driving laboratories (SDLs) that combine automated experiments with machine learning have accelerated data-driven discovery. Although Bayesian optimization (BO) is widely used in SDLs to ...

National statistical institutes (NSI's) are increasingly interested in using non-probability data to produce official statistics. Examples are information on the internet, social media messages, ...

Abstract: This article considers a distributed Thompson sampling algorithm for a cooperative multiplayer multiarmed bandit problem. We consider a multiagent system in which each agent pulls an arm ...

In August 2022 the Department of Health and Human Services (HHS) issued a notice of proposed rulemaking prohibiting covered entities, which include health care providers and health plans, from ...

Abstract: In this article, we propose a Thompson sampling algorithm with Gaussian prior for unimodal bandit under Gaussian reward setting, where the expected reward is unimodal over the partially ...

This repository contains the source code for the “Thompson sampling efficient multiobjective optimization” (TSEMO) algorithm outlined in (Bradford et al., 2018). The algorithm is written to optimize ...

The multi-armed bandit (MAB) problem models a decision-maker that optimizes its actions based on current and acquired new knowledge to maximize its reward. This type of online decision is prominent in ...

Multi-agent coordination is prevalent in many real-world applications. However, such coordination is challenging due to its combinatorial nature. An important observation in this regard is that agents ...

How does one meet the most demanding part of an analysis-sampling? A typical example is soil, which presents a twofold problem for the analyst: first, the selection of the sites where the samples are ...

Thompson Sampling is an algorithm that can be used to analyze multi-armed bandit problems. Imagine you're in a casino standing in front of three slot machines. You have 10 free plays. Each machine ...