36 citations · 93 across the 11 of their papers we have counts for
13 papers
Stein Variational Black-Box Combinatorial Optimization
Thomas Landais, Olivier Goudet, Adrien Goëffon +2
Combinatorial black-box optimization in high-dimensional settings demands a careful trade-off between exploiting promising regions of the search space and preserving sufficient exp…
Black-Box Combinatorial Optimization with Order-Invariant Reinforcement Learning
Olivier Goudet, Quentin Suire, Adrien Goëffon +2
We introduce an order-invariant reinforcement learning framework for black-box combinatorial optimization. Classical estimation-of-distribution algorithms (EDAs) often rely on lear…
Discovering new robust local search algorithms with neuro-evolution
Mohamed Salim Amri Sakhri, Adrien Goëffon, Olivier Goudet +2
This paper explores a novel approach aimed at overcoming existing challenges in the realm of local search algorithms. Our aim is to improve the decision process that takes place wi…
Deinterleaving of Discrete Renewal Process Mixtures with Application to Electronic Support Measures
Jean Pinsolle, Olivier Goudet, Cyrille Enderli +2
In this paper, we propose a new deinterleaving method for mixtures of discrete renewal Markov chains. This method relies on the maximization of a penalized likelihood score. It exp…
Combining Monte Carlo Tree Search and Heuristic Search for Weighted Vertex Coloring
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. In addition to the basic MCTS…
On Monte Carlo Tree Search for Weighted Vertex Coloring
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. S…
A deep learning guided memetic framework for graph coloring problems
Olivier Goudet, Cyril Grelier, Jin-Kao Hao
Given an undirected graph with a set of vertices and a set of edges , a graph coloring problem involves finding a partition of the vertices into different independ…
A massively parallel evolutionary algorithm for the partial Latin square extension problem
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 applica…
Survival Estimation for Missing not at Random Censoring Indicators based on Copula Models
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 f…
Population-based Gradient Descent Weight Learning for Graph Coloring Problems
Olivier Goudet, Béatrice Duval, Jin-Kao Hao
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 th…
Causal Discovery Toolbox: Uncover causal relationships in Python
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 mode…
Structural Agnostic Modeling: Adversarial Learning of Causal Graphs
Diviyan Kalainathan, Olivier Goudet, Isabelle Guyon +2
A new causal discovery method, Structural Agnostic Modeling (SAM), is presented in this paper. Leveraging both conditional independencies and distributional asymmetries, SAM aims t…
Causal Generative Neural Networks
Olivier Goudet, Diviyan Kalainathan, Philippe Caillou +3
We present Causal Generative Neural Networks (CGNNs) to learn functional causal models from observational data. CGNNs leverage conditional independencies and distributional asymmet…