2026-07-28T20:53:39Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:27494 2026-07-28T08:37:33Z ddc:004 open_access

Bayesian Optimisation (Dagstuhl Seminar 25451) Branke, Jürgen Hutter, Frank Pedrielli, Giulia Poloczek, Matthias Papenmeier, Leonard AutoML Bayesian optimization Benchmarking Gaussian processes This report documents the programme and outcomes of Dagstuhl Seminar 25451, "Bayesian Optimisation", held from November 2–7, 2025. The seminar brought together 39 international experts from machine learning, optimisation, statistics, and engineering to discuss recent advances, open challenges, and emerging research directions in Bayesian optimisation. The programme comprised plenary talks spanning foundational issues, benchmarking, and the growing interaction between Bayesian optimisation and generative AI, alongside focused working groups on key thematic areas. Beyond technical discussions, the seminar placed strong emphasis on community building and worked towards establishing best practices. This report summarises the plenary contributions, the outcomes of the working groups, and additional community-driven activities, including a collection of practical "tricks of the trade" and exploratory benchmarking exercises. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Jürgen Branke and Frank Hutter and Giulia Pedrielli and Matthias Poloczek and Leonard Papenmeier 2026 Is Part Of Dagstuhl Reports, Volume 15, Issue 11 (2026) Article Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/DagRep.15.11.1 urn:nbn:de:0030-drops-274940 https://drops.dagstuhl.de/entities/document/10.4230/DagRep.15.11.1 eng https://creativecommons.org/licenses/by/4.0/legalcode