no code implementations • 14 Feb 2024 • Jean Pinsolle, Olivier Goudet, Cyrille Enderli, Sylvain Lamprier, Jin-Kao Hao
In this paper, we propose a new deinterleaving method for mixtures of discrete renewal Markov chains.
1 code implementation • 24 Apr 2023 • Cyril Grelier, Olivier Goudet, Jin-Kao Hao
This work investigates the Monte Carlo Tree Search (MCTS) method combined with dedicated heuristics for solving the Weighted Vertex Coloring Problem.
1 code implementation • 3 Feb 2022 • Cyril Grelier, Olivier Goudet, Jin-Kao Hao
This work presents the first study of using the popular Monte Carlo Tree Search (MCTS) method combined with dedicated heuristics for solving the Weighted Vertex Coloring Problem.
no code implementations • 13 Sep 2021 • 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.
no code implementations • 18 Mar 2021 • 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.
1 code implementation • 3 Sep 2020 • Mikael Escobar-Bach, Olivier Goudet
In the presence of right-censored data with covariates, the conditional Kaplan-Meier estimator (also known as the Beran estimator) consistently estimates the conditional survival function of the random follow-up for the event of interest.
1 code implementation • 5 Sep 2019 • Olivier Goudet, Béatrice Duval, Jin-Kao Hao
Unlike existing methods for graph coloring that are specific to the considered problem, the presented work targets a generic objective by introducing a unified method that can be applied to different graph coloring problems.
3 code implementations • 6 Mar 2019 • Diviyan Kalainathan, Olivier Goudet
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.
1 code implementation • 13 Mar 2018 • Diviyan Kalainathan, Olivier Goudet, Isabelle Guyon, David Lopez-Paz, Michèle Sebag
A new causal discovery method, Structural Agnostic Modeling (SAM), is presented in this paper.
1 code implementation • ICLR 2018 • Olivier Goudet, Diviyan Kalainathan, Philippe Caillou, Isabelle Guyon, David Lopez-Paz, Michèle Sebag
We present Causal Generative Neural Networks (CGNNs) to learn functional causal models from observational data.
2 code implementations • 15 Sep 2017 • Olivier Goudet, Diviyan Kalainathan, Philippe Caillou, Isabelle Guyon, David Lopez-Paz, Michèle Sebag
We introduce a new approach to functional causal modeling from observational data, called Causal Generative Neural Networks (CGNN).