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Documents authored by Kersting, Kristian

Document
DOI: 10.4230/DagRep.12.4.13
Recent Advancements in Tractable Probabilistic Inference (Dagstuhl Seminar 22161)

Authors: Priyank Jaini, Kristian Kersting, Antonio Vergari, and Max Welling

Published in: Dagstuhl Reports, Volume 12, Issue 4 (2022)

Abstract
In several real-world scenarios, decision making involves advanced reasoning under uncertainty, i.e. the ability to answer probabilistic queries. Typically, it is necessary to compute these answers in a limited amount of time. Moreover, in many domains, such as healthcare and economical decision making, it is crucial that the result of these queries is reliable, i.e. either exact or comes with approximation guarantees. In all these scenarios, tractable probabilistic inference and learning are becoming increasingly important. Research on representations and learning algorithms for tractable inference embraces very different fields, each one contributing its own perspective. These include automated reasoning, probabilistic modeling, statistical and Bayesian inference and deep learning. Among the many recent emerging venues in these fields there are: tractable neural density estimators such as autoregressive models and normalizing flows; deep tractable probabilistic circuits such as sum-product networks and sentential decision diagrams; approximate inference routines with guarantees on the quality of the approximation. Each of these model classes occupies a particular spot in the continuum between tractability and expressiveness. That is, different model classes might offer appealing advantages in terms of efficiency or representation capabilities while trading-off other of these aspects. So far, clear connections and a deeper understanding of the key differences among them have been hindered by the different languages and perspectives adopted by the different "souls" that comprise the tractable probabilistic modeling community. This Dagstuhl Seminar brought together experts from these sub-communities and provided the perfect venue to exchange perspectives, deeply discuss the recent advancements and build strong bridges that can greatly propel interdisciplinary research.
Cite as

Priyank Jaini, Kristian Kersting, Antonio Vergari, and Max Welling. Recent Advancements in Tractable Probabilistic Inference (Dagstuhl Seminar 22161). In Dagstuhl Reports, Volume 12, Issue 4, pp. 13-25, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@Article{jaini_et_al:DagRep.12.4.13,
  author =	{Jaini, Priyank and Kersting, Kristian and Vergari, Antonio and Welling, Max},
  title =	{{Recent Advancements in Tractable Probabilistic Inference (Dagstuhl Seminar 22161)}},
  pages =	{13--25},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2022},
  volume =	{12},
  number =	{4},
  editor =	{Jaini, Priyank and Kersting, Kristian and Vergari, Antonio and Welling, Max},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DagRep.12.4.13},
  URN =		{urn:nbn:de:0030-drops-172785},
  doi =		{10.4230/DagRep.12.4.13},
  annote =	{Keywords: approximate inference with guarantees, deep generative models, probabilistic circuits, Tractable inference}
}
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-dev.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}
}
Document
DOI: 10.4230/DagRep.9.9.1
Logic and Learning (Dagstuhl Seminar 19361)

Authors: Michael Benedikt, Kristian Kersting, Phokion G. Kolaitis, and Daniel Neider

Published in: Dagstuhl Reports, Volume 9, Issue 9 (2020)

Abstract
The goal of building truly intelligent systems has forever been a central problem in computer science. While logic-based approaches of yore have had their successes and failures, the era of machine learning, specifically deep learning is also coming upon significant challenges. There is a growing consensus that the inductive reasoning and complex, high-dimensional pattern recognition capabilities of deep learning models need to be combined with symbolic (even programmatic), deductive capabilities traditionally developed in the logic and automated reasoning communities in order to achieve the next step towards building intelligent systems, including making progress at the frontier of hard problems such as explainable AI. However, these communities tend to be quite separate and interact only minimally, often at odds with each other upon the subject of the ``correct approach'' to AI. This report documents the efforts of Dagstuhl Seminar 19361 on ``Logic and Learning'' to bring these communities together in order to: (i) bridge the research efforts between them and foster an exchange of ideas in order to create unified formalisms and approaches that bear the advantages of both research methodologies; (ii) review and analyse the progress made across both communities; (iii) understand the subtleties and difficulties involved in solving hard problems using both perspectives; (iv) make attempts towards a consensus on what the hard problems are and what the elements of good solutions to these problems would be. The three focal points of the seminar were the strands of ``Logic for Machine Learning'', ``Machine Learning for Logic'', and ``Logic vs. Machine Learning''. The seminar format consisted of long and short talks, as well as breakout sessions. We summarise the motivations and proceedings of the seminar, and report on the abstracts of the talks and the results of the breakout sessions.
Cite as

Michael Benedikt, Kristian Kersting, Phokion G. Kolaitis, and Daniel Neider. Logic and Learning (Dagstuhl Seminar 19361). In Dagstuhl Reports, Volume 9, Issue 9, pp. 1-22, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)

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@Article{benedikt_et_al:DagRep.9.9.1,
  author =	{Benedikt, Michael and Kersting, Kristian and Kolaitis, Phokion G. and Neider, Daniel},
  title =	{{Logic and Learning (Dagstuhl Seminar 19361)}},
  pages =	{1--22},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2020},
  volume =	{9},
  number =	{9},
  editor =	{Benedikt, Michael and Kersting, Kristian and Kolaitis, Phokion G. and Neider, Daniel},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DagRep.9.9.1},
  URN =		{urn:nbn:de:0030-drops-118425},
  doi =		{10.4230/DagRep.9.9.1},
  annote =	{Keywords: Artificial Intelligence, Automated reasoning, Databases, Deep Learning, Inductive Logic Programming, Logic, Logic and Learning, Logic for Machine Learning, Logic vs. Machine Learning, Machine Learning, Machine Learning for Logic, Neurosymbolic methods}
}
Document
DOI: 10.4230/DagSemProc.07161.1
07161 Abstracts Collection – Probabilistic, Logical and Relational Learning - A Further Synthesis

Authors: Luc De Raedt, Thomas Dietterich, Lise Getoor, Kristian Kersting, and Stephen H. Muggleton

Published in: Dagstuhl Seminar Proceedings, Volume 7161, Probabilistic, Logical and Relational Learning - A Further Synthesis (2008)

Abstract
From April 14 – 20, 2007, the Dagstuhl Seminar 07161 ``Probabilistic, Logical and Relational Learning - A Further Synthesis'' was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available.
Cite as

Luc De Raedt, Thomas Dietterich, Lise Getoor, Kristian Kersting, and Stephen H. Muggleton. 07161 Abstracts Collection – Probabilistic, Logical and Relational Learning - A Further Synthesis. In Probabilistic, Logical and Relational Learning - A Further Synthesis. Dagstuhl Seminar Proceedings, Volume 7161, pp. 1-21, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2008)

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@InProceedings{deraedt_et_al:DagSemProc.07161.1,
  author =	{De Raedt, Luc and Dietterich, Thomas and Getoor, Lise and Kersting, Kristian and Muggleton, Stephen H.},
  title =	{{07161 Abstracts Collection – Probabilistic, Logical and Relational Learning - A Further Synthesis}},
  booktitle =	{Probabilistic, Logical and Relational Learning - A Further Synthesis},
  pages =	{1--21},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2008},
  volume =	{7161},
  editor =	{Luc de Raedt and Thomas Dietterich and Lise Getoor and Kristian Kersting and Stephen H. Muggleton},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DagSemProc.07161.1},
  URN =		{urn:nbn:de:0030-drops-13885},
  doi =		{10.4230/DagSemProc.07161.1},
  annote =	{Keywords: Artificial Intelligence, Uncertainty in AI, Probabilistic Reasoning, Knowledge Representation, Logic Programming, Relational Learning, Inductive Logic Programming, Graphical Models, Statistical Relational Learning, First-Order Logical and Relational Probabilistic Languages}
}
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