Výsledky vyhledávání - "Olivier Goudet"

A massively parallel evolutionary algorithm for the partial Latin square extension problem

Autor: Olivier Goudet, Jin-Kao Hao

The partial Latin square extension problem is to fill as many as possible empty cells of a partially filled Latin square. This problem is a useful model for a wide range of applications in diverse domains. This paper presents the first massively para

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8b23d17ac1bbbe14d5b94e8674f14fe3
http://arxiv.org/abs/2103.10453

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Population-based gradient descent weight learning for graph coloring problems

Autor: Jin-Kao Hao, Béatrice Duval, Olivier Goudet

Publikováno v: Knowledge-Based Systems
Knowledge-Based Systems, Elsevier, 2021, 212, pp.106581-. ⟨10.1016/j.knosys.2020.106581⟩

Graph coloring involves assigning colors to the vertices of a graph such that two vertices linked by an edge receive different colors. Graph coloring problems are general models that are very useful to formulate many relevant applications and, howeve

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f546a74dae01c9593de345b03dae37d2
https://hal.archives-ouvertes.fr/hal-03493613

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A deep learning guided memetic framework for graph coloring problems

Autor: Olivier Goudet, Cyril Grelier, Jin-Kao Hao

Given an undirected graph $G=(V,E)$ with a set of vertices $V$ and a set of edges $E$, a graph coloring problem involves finding a partition of the vertices into different independent sets. In this paper we present a new framework that combines a dee

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7ff3cbd3341f10ae9b35b8fdb0a94462

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Causal Discovery Toolbox: Uncovering causal relationships in Python

Autor: Diviyan Kalainathan, Olivier Goudet, Ritik Dutta

Publikováno v: Journal of Machine Learning Research
Journal of Machine Learning Research, 2020, 21, pp.1-5
Journal of Machine Learning Research, Microtome Publishing, 2020, 21, pp.1-5
HAL

International audience; This paper presents a new open source Python framework for causal discovery from observational data and domain background knowledge, aimed at causal graph and causal mechanism modeling. The Cdt package implements an end-to-end

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::ef3b4eb44543392d136b0f458ce7d1d3
https://univ-angers.hal.science/hal-02945539/document

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WorkSim: An Agent-Based Model of Labor Markets

Autor: Olivier Goudet, Jean-Daniel Kant, Gérard Ballot

Publikováno v: Journal of Artificial Societies and Social Simulation. 23

In this paper, we provide an overview of the WorkSim model, an agent-based framework designed to study labor markets. The first objective of this model was to reproduce, within rigorous stock-flow accounting, the gross flows of individuals between im

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::04aac645d2dd7c474d53f87e07ba4dcc
https://doi.org/10.18564/jasss.4396

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Evaluation Methods of Cause-Effect Pairs

Autor: Isabelle Guyon, Olivier Goudet, Diviyan Kalainathan

Publikováno v: Cause Effect Pairs in Machine Learning
Guyon, Isabelle; Statnikov, Alexander; Batu, Berna Bakir. Cause Effect Pairs in Machine Learning, Springer Verlag, pp.27-99, 2019, The Springer Series on Challenges in Machine Learning, 978-3-030-21809-6. ⟨10.1007/978-3-030-21810-2_2⟩
Cause Effect Pairs in Machine Learning ISBN: 9783030218096

International audience; This chapter addresses the problem of benchmarking causal models or validating particular putative causal relationships, in the limited setting of cause-effect pairs, when empirical “observational” data are available. We d

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f36aa2cad0b0d772947449604bc1dea4
https://hal.inria.fr/hal-02433198

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Discriminant Learning Machines

Autor: Olivier Goudet, Isabelle Guyon, Diviyan Kalainathan, Michèle Sebag

Publikováno v: Cause Effect Pairs in Machine Learning
Guyon, Isabelle; Statnikov, Alexander; Batu, Berna Bakir. Cause Effect Pairs in Machine Learning, Springer Verlag, pp.155-189, 2019, The Springer Series on Challenges in Machine Learning, 978-3-030-21809-6. ⟨10.1007/978-3-030-21810-2_4⟩
Cause Effect Pairs in Machine Learning ISBN: 9783030218096

International audience; The cause-effect pair challenge has, for the first time, formulated the cause-effect problem as a learning problem in which a causation coefficient is trained from data. This can be thought of as a kind of meta learning. This

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::576f894a565b8689bba7b78038148ef9
https://hal.inria.fr/hal-02433203

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Learning Bivariate Functional Causal Models

Autor: Michèle Sebag, Isabelle Guyon, Diviyan Kalainathan, Olivier Goudet

Publikováno v: Cause Effect Pairs in Machine Learning
Guyon, Isabelle; Statnikov, Alexander; Batu, Berna Bakir. Cause Effect Pairs in Machine Learning, Springer Verlag, pp.101-153, 2019, The Springer Series on Challenges in Machine Learning, 978-3-030-21809-6. ⟨10.1007/978-3-030-21810-2_3⟩
Cause Effect Pairs in Machine Learning ISBN: 9783030218096

International audience; Finding the causal direction in the cause-effect pair problem has been addressed in the literature by comparing two alternative generative models X → Y and Y → X. In this chapter, we first define what is meant by generativ

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6014dc4ea2264a99af8fd3305b1a1aa0
https://hal.inria.fr/hal-02433201/document

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