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Documents authored by Getoor, Lise

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Invited Talk
DOI: 10.4230/LIPIcs.ICDT.2019.2
The Power of Relational Learning (Invited Talk)

Authors: Lise Getoor

Published in: LIPIcs, Volume 127, 22nd International Conference on Database Theory (ICDT 2019)

Abstract
We live in a richly interconnected world and, not surprisingly, we generate richly interconnected data. From smart cities to social media to financial networks to biological networks, data is relational. While database theory is built on strong relational foundations, the same is not true for machine learning. The majority of machine learning methods flatten data into a single table before performing any processing. Further, database theory is also built on a bedrock of declarative representations. The same is not true for machine learning, in particular deep learning, which results in black-box, uninterpretable and unexplainable models. In this talk, I will introduce the field of statistical relational learning, an alternative machine learning approach based on declarative relational representations paired with probabilistic models. I’ll describe our work on probabilistic soft logic, a probabilistic programming language that is ideally suited to richly connected, noisy data. Our recent results show that by building on state-of-the-art optimization methods in a distributed implementation, we can solve very large relational learning problems orders of magnitude faster than existing approaches.
Cite as

Lise Getoor. The Power of Relational Learning (Invited Talk). In 22nd International Conference on Database Theory (ICDT 2019). Leibniz International Proceedings in Informatics (LIPIcs), Volume 127, p. 2:1, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)

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@InProceedings{getoor:LIPIcs.ICDT.2019.2,
  author =	{Getoor, Lise},
  title =	{{The Power of Relational Learning}},
  booktitle =	{22nd International Conference on Database Theory (ICDT 2019)},
  pages =	{2:1--2:1},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-101-6},
  ISSN =	{1868-8969},
  year =	{2019},
  volume =	{127},
  editor =	{Barcelo, Pablo and Calautti, Marco},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ICDT.2019.2},
  URN =		{urn:nbn:de:0030-drops-103048},
  doi =		{10.4230/LIPIcs.ICDT.2019.2},
  annote =	{Keywords: Machine learning, Probabilistic soft logic, Relational model}
}
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.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}
}
Document
DOI: 10.4230/DagSemProc.05051.1
05051 Abstracts Collection – Probabilistic, Logical and Relational Learning - Towards a Synthesis

Authors: Luc De Raedt, Tom Dietterich, Lise Getoor, and Stephen H. Muggleton

Published in: Dagstuhl Seminar Proceedings, Volume 5051, Probabilistic, Logical and Relational Learning - Towards a Synthesis (2006)

Abstract
From 30.01.05 to 04.02.05, the Dagstuhl Seminar 05051 ``Probabilistic, Logical and Relational Learning - Towards a 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, Tom Dietterich, Lise Getoor, and Stephen H. Muggleton. 05051 Abstracts Collection – Probabilistic, Logical and Relational Learning - Towards a Synthesis. In Probabilistic, Logical and Relational Learning - Towards a Synthesis. Dagstuhl Seminar Proceedings, Volume 5051, pp. 1-27, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2006)

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@InProceedings{deraedt_et_al:DagSemProc.05051.1,
  author =	{De Raedt, Luc and Dietterich, Tom and Getoor, Lise and Muggleton, Stephen H.},
  title =	{{05051 Abstracts Collection – Probabilistic, Logical and Relational Learning - Towards a Synthesis}},
  booktitle =	{Probabilistic, Logical and Relational Learning - Towards a Synthesis},
  pages =	{1--27},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2006},
  volume =	{5051},
  editor =	{Luc De Raedt and Thomas Dietterich and Lise Getoor and Stephen H. Muggleton},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.05051.1},
  URN =		{urn:nbn:de:0030-drops-4303},
  doi =		{10.4230/DagSemProc.05051.1},
  annote =	{Keywords: Statistical relational learning, probabilistic logic learning, inductive logic programming, knowledge representation, machine learning, uncertainty in artificial intelligence}
}
Document
DOI: 10.4230/DagSemProc.05051.2
05051 Executive Summary – Probabilistic, Logical and Relational Learning - Towards a Synthesis

Authors: Luc De Raedt, Tom Dietterich, Lise Getoor, and Stephen H. Muggleton

Published in: Dagstuhl Seminar Proceedings, Volume 5051, Probabilistic, Logical and Relational Learning - Towards a Synthesis (2006)

Abstract
A short report on the Dagstuhl seminar on Probabilistic, Logical and Relational Learning – Towards a Synthesis is given.
Cite as

Luc De Raedt, Tom Dietterich, Lise Getoor, and Stephen H. Muggleton. 05051 Executive Summary – Probabilistic, Logical and Relational Learning - Towards a Synthesis. In Probabilistic, Logical and Relational Learning - Towards a Synthesis. Dagstuhl Seminar Proceedings, Volume 5051, pp. 1-5, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2006)

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@InProceedings{deraedt_et_al:DagSemProc.05051.2,
  author =	{De Raedt, Luc and Dietterich, Tom and Getoor, Lise and Muggleton, Stephen H.},
  title =	{{05051 Executive Summary – Probabilistic, Logical and Relational Learning - Towards a Synthesis}},
  booktitle =	{Probabilistic, Logical and Relational Learning - Towards a Synthesis},
  pages =	{1--5},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2006},
  volume =	{5051},
  editor =	{Luc De Raedt and Thomas Dietterich and Lise Getoor and Stephen H. Muggleton},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.05051.2},
  URN =		{urn:nbn:de:0030-drops-4121},
  doi =		{10.4230/DagSemProc.05051.2},
  annote =	{Keywords: Reasoning about Uncertainty, Relational and Logical Represenations, Statistical Relational Learning, Inductive Lgoic Programmign}
}
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