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Bayesian Optimisation (Dagstuhl Seminar 25451)

Authors: Jürgen Branke, Frank Hutter, Giulia Pedrielli, Matthias Poloczek, and Leonard Papenmeier

Published in: Dagstuhl Reports, Volume 15, Issue 11 (2026)

Abstract

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.

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Jürgen Branke, Frank Hutter, Giulia Pedrielli, Matthias Poloczek, and Leonard Papenmeier. Bayesian Optimisation (Dagstuhl Seminar 25451). In Dagstuhl Reports, Volume 15, Issue 11, pp. 1-66, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@Article{branke_et_al:DagRep.15.11.1,
  author =	{Branke, J\"{u}rgen and Hutter, Frank and Pedrielli, Giulia and Poloczek, Matthias and Papenmeier, Leonard},
  title =	{{Bayesian Optimisation (Dagstuhl Seminar 25451)}},
  pages =	{1--66},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2026},
  volume =	{15},
  number =	{11},
  editor =	{Branke, J\"{u}rgen and Hutter, Frank and Pedrielli, Giulia and Poloczek, Matthias and Papenmeier, Leonard},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.15.11.1},
  URN =		{urn:nbn:de:0030-drops-274940},
  doi =		{10.4230/DagRep.15.11.1},
  annote =	{Keywords: AutoML, Bayesian optimization, Benchmarking, Gaussian processes}
}

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DOI: 10.4230/DagRep.15.4.126

Challenges and Opportunities of Table Representation Learning (Dagstuhl Seminar 25182)

Authors: Carsten Binnig, Julian Martin Eisenschlos, Madelon Hulsebos, and Frank Hutter

Published in: Dagstuhl Reports, Volume 15, Issue 4 (2025)

Abstract

The growing volume and importance of structured data have sparked increasing interest in Table Representation Learning (TRL), an emerging field that leverages neural models to learn abstract, general-purpose representations for tabular data to support a wide range of downstream tasks such as tabular prediction, table question answering, tabular data cleaning, and many more. This seminar gathered the different communities (ML, NLP, IR, DB) who work on this topic to discuss the challenges & long-term vision of this field. From the organizers: Carsten Binnig, Julian Eisenschlos, Madelon Hulsebos, Frank Hutter.

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Carsten Binnig, Julian Martin Eisenschlos, Madelon Hulsebos, and Frank Hutter. Challenges and Opportunities of Table Representation Learning (Dagstuhl Seminar 25182). In Dagstuhl Reports, Volume 15, Issue 4, pp. 126-138, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@Article{binnig_et_al:DagRep.15.4.126,
  author =	{Binnig, Carsten and Eisenschlos, Julian Martin and Hulsebos, Madelon and Hutter, Frank},
  title =	{{Challenges and Opportunities of Table Representation Learning (Dagstuhl Seminar 25182)}},
  pages =	{126--138},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2025},
  volume =	{15},
  number =	{4},
  editor =	{Binnig, Carsten and Eisenschlos, Julian Martin and Hulsebos, Madelon and Hutter, Frank},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.15.4.126},
  URN =		{urn:nbn:de:0030-drops-252531},
  doi =		{10.4230/DagRep.15.4.126},
  annote =	{Keywords: applications of table representation learning, benchmarks and datasets for table representation learning, pre-trained (language) models for tables and databases, representation and generative learning for data management and analysis}
}

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Documents authored by Hutter, Frank

Bayesian Optimisation (Dagstuhl Seminar 25451)

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Challenges and Opportunities of Table Representation Learning (Dagstuhl Seminar 25182)

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