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Invited Talk

DOI: 10.4230/LIPIcs.CP.2023.4

Coupling CP with Deep Learning for Molecular Design and SARS-CoV2 Variants Exploration (Invited Talk)

Authors: Thomas Schiex

Published in: LIPIcs, Volume 280, 29th International Conference on Principles and Practice of Constraint Programming (CP 2023)

Abstract

The use of discrete optimization, including Constraint Programming, for designing objects that we completely understand is quite usual. In this talk, I'll show how designing specific biomolecules (proteins) raises new challenges, requiring solving problems that combine precise design targets, approximate laws, and design rules that can be deep-learned from data.

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Thomas Schiex. Coupling CP with Deep Learning for Molecular Design and SARS-CoV2 Variants Exploration (Invited Talk). In 29th International Conference on Principles and Practice of Constraint Programming (CP 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 280, pp. 4:1-4:3, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@InProceedings{schiex:LIPIcs.CP.2023.4,
  author =	{Schiex, Thomas},
  title =	{{Coupling CP with Deep Learning for Molecular Design and SARS-CoV2 Variants Exploration}},
  booktitle =	{29th International Conference on Principles and Practice of Constraint Programming (CP 2023)},
  pages =	{4:1--4:3},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-300-3},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{280},
  editor =	{Yap, Roland H. C.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2023.4},
  URN =		{urn:nbn:de:0030-drops-190415},
  doi =		{10.4230/LIPIcs.CP.2023.4},
  annote =	{Keywords: graphical models, deep learning, constraint programming, cost function networks, random Markov fields, decision-focused learning, protein design}
}

Document

Tutorial

DOI: 10.4230/LIPIcs.STACS.2020.4

Graphical Models: Queries, Complexity, Algorithms (Tutorial)

Authors: Martin C. Cooper, Simon de Givry, and Thomas Schiex

Published in: LIPIcs, Volume 154, 37th International Symposium on Theoretical Aspects of Computer Science (STACS 2020)

Abstract

Graphical models (GMs) define a family of mathematical models aimed at the concise description of multivariate functions using decomposability. We restrict ourselves to functions of discrete variables but try to cover a variety of models that are not always considered as "Graphical Models", ranging from functions with Boolean variables and Boolean co-domain (used in automated reasoning) to functions over finite domain variables and integer or real co-domains (usual in machine learning and statistics). We use a simple algebraic semi-ring based framework for generality, define associated queries, relationships between graphical models, complexity results, and families of algorithms, with their associated guarantees.

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Martin C. Cooper, Simon de Givry, and Thomas Schiex. Graphical Models: Queries, Complexity, Algorithms (Tutorial). In 37th International Symposium on Theoretical Aspects of Computer Science (STACS 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 154, pp. 4:1-4:22, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)

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@InProceedings{cooper_et_al:LIPIcs.STACS.2020.4,
  author =	{Cooper, Martin C. and de Givry, Simon and Schiex, Thomas},
  title =	{{Graphical Models: Queries, Complexity, Algorithms}},
  booktitle =	{37th International Symposium on Theoretical Aspects of Computer Science (STACS 2020)},
  pages =	{4:1--4:22},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-140-5},
  ISSN =	{1868-8969},
  year =	{2020},
  volume =	{154},
  editor =	{Paul, Christophe and Bl\"{a}ser, Markus},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.STACS.2020.4},
  URN =		{urn:nbn:de:0030-drops-118654},
  doi =		{10.4230/LIPIcs.STACS.2020.4},
  annote =	{Keywords: Computational complexity, tree decomposition, graphical models, submodularity, message passing, local consistency, artificial intelligence, valued constraints, optimization}
}

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Documents authored by Schiex, Thomas

Coupling CP with Deep Learning for Molecular Design and SARS-CoV2 Variants Exploration (Invited Talk)

Abstract

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Graphical Models: Queries, Complexity, Algorithms (Tutorial)

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