Search Results for author:
Found 11 papers, 8 papers with code
Deinterleaving of Discrete Renewal Process Mixtures with Application to Electronic Support Measures
In this paper, we propose a new deinterleaving method for mixtures of discrete renewal Markov chains.
Combining Monte Carlo Tree Search and Heuristic Search for Weighted Vertex Coloring
This work investigates the Monte Carlo Tree Search (MCTS) method combined with dedicated heuristics for solving the Weighted Vertex Coloring Problem.
On Monte Carlo Tree Search for Weighted Vertex Coloring
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.
A deep learning guided memetic framework for graph coloring problems
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.
A massively parallel evolutionary algorithm for the partial Latin square extension problem
The partial Latin square extension problem is to fill as many as possible empty cells of a partially filled Latin square.
Survival Estimation for Missing not at Random Censoring Indicators based on Copula Models
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.
Population-based Gradient Descent Weight Learning for Graph Coloring Problems
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.
Causal Discovery Toolbox: Uncover causal relationships in Python
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.
Structural Agnostic Modeling: Adversarial Learning of Causal Graphs
A new causal discovery method, Structural Agnostic Modeling (SAM), is presented in this paper.
Causal Generative Neural Networks
We present Causal Generative Neural Networks (CGNNs) to learn functional causal models from observational data.
Learning Functional Causal Models with Generative Neural Networks
We introduce a new approach to functional causal modeling from observational data, called Causal Generative Neural Networks (CGNN).
