4 citations · 12 across the 5 of their papers we have counts for
6 papers
Computing Abductive Explanations for Boosted Trees
Gilles Audemard, Jean-Marie Lagniez, Pierre Marquis +1
Boosted trees is a dominant ML model, exhibiting high accuracy. However, boosted trees are hardly intelligible, and this is a problem whenever they are used in safety-critical appl…
On the Explanatory Power of Decision Trees
Gilles Audemard, Steve Bellart, Louenas Bounia +3
Decision trees have long been recognized as models of choice in sensitive applications where interpretability is of paramount importance. In this paper, we examine the computationa…
Trading Complexity for Sparsity in Random Forest Explanations
Gilles Audemard, Steve Bellart, Louenas Bounia +3
Random forests have long been considered as powerful model ensembles in machine learning. By training multiple decision trees, whose diversity is fostered through data and feature…
On the Computational Intelligibility of Boolean Classifiers
Gilles Audemard, Steve Bellart, Louenas Bounia +3
In this paper, we investigate the computational intelligibility of Boolean classifiers, characterized by their ability to answer XAI queries in polynomial time. The classifiers und…
SAT Heritage: a community-driven effort for archiving, building and running more than thousand SAT solvers
Gilles Audemard, Loïc Paulevé, Laurent Simon
SAT research has a long history of source code and binary releases, thanks to competitions organized every year. However, since every cycle of competitions has its own set of rules…
Integrating Conflict Driven Clause Learning to Local Search
Gilles Audenard, Jean-Marie Lagniez, Bertrand Mazure +1
This article introduces SatHyS (SAT HYbrid Solver), a novel hybrid approach for propositional satisfiability. It combines local search and conflict driven clause learning (CDCL) sc…