30e séminaire POC
SPOC 30 "Integer Optimization for Machine Learning"
Integer Optimization for Machine Learning
Tuesday, April 07th, 2026
At the CNAM, Paris
2 rue du Conté, Amphitheater Gaston Planté
L'entrée est gratuite, mais merci de vous inscrire en cliquant ici: Registration
See the list of the participants here
9h00 - 09h30 Accueil - Welcome
09h30 - 10h15 Diego Delle Donne (ESSEC business school)
Optimization models and algorithms for hyper-rectangular clustering problems
Machine Learning techniques are widely used to analyze large datasets and support prediction and decision-making. However, many models lack interpretability, as their underlying rules are difficult to explain. This has motivated increasing interest in explainable learning models. In unsupervised learning, clustering methods partition data into groups but rarely provide explicit reasons for point membership. Hyper-rectangle clustering addresses this limitation by defining each cluster as the smallest axis-aligned hyper-rectangle containing its points, thus providing clear geometric rules (coordinate-wise bounds) that explain cluster assignments. Given a set of points in the d-dimensional space, the goal is to find a hyper-rectangle clustering of minimum size. We first propose mixed-integer programming (MIP) formulations for the problem; a compact formulation and an extended one which is solved by means of a branch-and-price algorithm. Since these formulations become computationally limited for larger instances, we develop an incremental exact strategy that exploits their ability to optimally solve small instances. The method starts with a subset of points and iteratively adds points until all are covered; we prove that once full coverage is achieved, the solution is optimal for the original problem. Finally, computational experiments show that the proposed approach significantly extends the size of instances that can be solved to optimality.
10h15 - 10h45 Pause café - Coffe break
10h45 - 11h30 Margot Boyer (CEDRIC laboratory, CNAM)
Fast SDP certification of neural networks : towards large multiclass datasets
11h30 - 12h15 Mohamed Siala (LAAS laboratory)
Trustworthy Machine Learning via Combinatorial Optimization and Automated Reasoning
12h15 - 14h00 Repas - Lunch
14h00 - 14h45 Farnaz Farzadnia (Copenhagen business school)
Cluster Analysis of Bicycle Lane Safety: An Explainable Approach
14h45 - 15h15 Pause café - Coffe break
15h15 - 16h00 Ilaria Ciocci (Sapienza University of Rome)
Margin Optimal Trees for Nonlinear Regression
16h00 - 16h45 Thomas Halskov (Copenhagen business school)
Collective LIME: A Global View of Local Surrogate Models
Organizer: Zacharie Ales et Sebastien Martin