Generalized Convergence Analysis of Tsetlin Machines: A Probabilistic Approach to Concept Learning

no code implementations • 3 Oct 2023 • Mohamed-Bachir Belaid, Jivitesh Sharma, Lei Jiao, Ole-Christoffer Granmo, Per-Arne Andersen, Anis Yazidi

Tsetlin Machines (TMs) have garnered increasing interest for their ability to learn concepts via propositional formulas and their proven efficiency across various application domains.

Interpretable Machine Learning

Solve Optimization Problems with Unknown Constraint Networks

no code implementations • 23 Nov 2021 • Mohamed-Bachir Belaid, Arnaud Gotlieb, Nadjib Lazaar

In this paper, we propose Learn&Optimize, a method to solve optimization problems with known objective function and unknown constraint network.

Scheduling

Frequent Itemset Mining with Multiple Minimum Supports: a Constraint-based Approach

no code implementations • 16 Sep 2021 • Mohamed-Bachir Belaid, Nadjib Lazaar

The problem of discovering frequent itemsets including rare ones has received a great deal of attention.

Computational Complexity of Three Central Problems in Itemset Mining

no code implementations • 4 Dec 2020 • Christian Bessiere, Mohamed-Bachir Belaid, Nadjib Lazaar

Itemset mining is one of the most studied tasks in knowledge discovery.

Opening the Software Engineering Toolbox for the Assessment of Trustworthy AI

no code implementations • 14 Jul 2020 • Mohit Kumar Ahuja, Mohamed-Bachir Belaid, Pierre Bernabé, Mathieu Collet, Arnaud Gotlieb, Chhagan Lal, Dusica Marijan, Sagar Sen, Aizaz Sharif, Helge Spieker

Trustworthiness is a central requirement for the acceptance and success of human-centered artificial intelligence (AI).