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Documents authored by Van den Broeck, Guy

Document
DOI: 10.4230/DagRep.13.6.1
Scalable Analysis of Probabilistic Models and Programs (Dagstuhl Seminar 23241)

Authors: Sebastian Junges, Joost-Pieter Katoen, Scott Sanner, Guy Van den Broeck, and Bahare Salmani

Published in: Dagstuhl Reports, Volume 13, Issue 6 (2024)

Abstract
This report documents the program and the outcomes of Dagstuhl Seminar 23241 "Scalable Analysis of Probabilistic Models and Programs". The seminar brought together researchers from probabilistic graphical models, verification of probabilistic programming languages, and probabilistic planning. The communities bring vastly different perspectives on the methods and goals of inference under uncertainty. In this seminar, we worked towards a common understanding of how the different angles yield subtle differences in the problem statements and how the different methods provide different strengths and weaknesses. The report describes the different areas, the activities during the seminar including hot topics that were vividly discussed, and an overview of the technical talks.
Cite as

Sebastian Junges, Joost-Pieter Katoen, Scott Sanner, Guy Van den Broeck, and Bahare Salmani. Scalable Analysis of Probabilistic Models and Programs (Dagstuhl Seminar 23241). In Dagstuhl Reports, Volume 13, Issue 6, pp. 1-21, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@Article{junges_et_al:DagRep.13.6.1,
  author =	{Junges, Sebastian and Katoen, Joost-Pieter and Sanner, Scott and Van den Broeck, Guy and Salmani, Bahare},
  title =	{{Scalable Analysis of Probabilistic Models and Programs (Dagstuhl Seminar 23241)}},
  pages =	{1--21},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2024},
  volume =	{13},
  number =	{6},
  editor =	{Junges, Sebastian and Katoen, Joost-Pieter and Sanner, Scott and Van den Broeck, Guy and Salmani, Bahare},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.13.6.1},
  URN =		{urn:nbn:de:0030-drops-196362},
  doi =		{10.4230/DagRep.13.6.1},
  annote =	{Keywords: model counting, probabilistic inference, probabilistic model checking, probabilistic planning, probabilistic programs}
}
Document
DOI: 10.4230/DagRep.10.2.76
SE4ML - Software Engineering for AI-ML-based Systems (Dagstuhl Seminar 20091)

Authors: Kristian Kersting, Miryung Kim, Guy Van den Broeck, and Thomas Zimmermann

Published in: Dagstuhl Reports, Volume 10, Issue 2 (2020)

Abstract
Multiple research disciplines, from cognitive sciences to biology, finance, physics, and the social sciences, as well as many companies, believe that data-driven and intelligent solutions are necessary. Unfortunately, current artificial intelligence (AI) and machine learning (ML) technologies are not sufficiently democratized - building complex AI and ML systems requires deep expertise in computer science and extensive programming skills to work with various machine reasoning and learning techniques at a rather low level of abstraction. It also requires extensive trial and error exploration for model selection, data cleaning, feature selection, and parameter tuning. Moreover, there is a lack of theoretical understanding that could be used to abstract away these subtleties. Conventional programming languages and software engineering paradigms have also not been designed to address challenges faced by AI and ML practitioners. In 2016, companies invested $26–39 billion in AI and McKinsey predicts that investments will be growing over the next few years. Any AI/ML-based systems will need to be built, tested, and maintained, yet there is a lack of established engineering practices in industry for such systems because they are fundamentally different from traditional software systems. This Dagstuhl Seminar brought together two rather disjoint communities together, software engineering and programming languages (PL/SE) and artificial intelligence and machine learning (AI-ML) to discuss open problems on how to improve the productivity of data scientists, software engineers, and AI-ML practitioners in industry.
Cite as

Kristian Kersting, Miryung Kim, Guy Van den Broeck, and Thomas Zimmermann. SE4ML - Software Engineering for AI-ML-based Systems (Dagstuhl Seminar 20091). In Dagstuhl Reports, Volume 10, Issue 2, pp. 76-87, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)

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@Article{kersting_et_al:DagRep.10.2.76,
  author =	{Kersting, Kristian and Kim, Miryung and Van den Broeck, Guy and Zimmermann, Thomas},
  title =	{{SE4ML - Software Engineering for AI-ML-based Systems (Dagstuhl Seminar 20091)}},
  pages =	{76--87},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2020},
  volume =	{10},
  number =	{2},
  editor =	{Kersting, Kristian and Kim, Miryung and Van den Broeck, Guy and Zimmermann, Thomas},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.10.2.76},
  URN =		{urn:nbn:de:0030-drops-130603},
  doi =		{10.4230/DagRep.10.2.76},
  annote =	{Keywords: correctness / explainability / traceability / fairness for ml, data scientist productivity, debugging/ testing / verification for ml systems}
}
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