Learning

  • Fairness, Interpretablitity and Privacy in Machine Learning Models

    • Subject : Differentiel Privacy for Interpretable Machine Learing model

      • Participants: Julien Ferry, Ulrich Aivodji (UQAM), Sébastien Gambs (UQAM)
      • Internship: Timothée Ly (M2, 2023)
    • Subject:
      • Sensitive Attributes Reconstruction Attack
      • Combinatorial methods for learning Fair Rule Lists
      • Fairness Generalization based on Distributionnaly Robust Optimization
        • Participants: Ulrich Aivodji (UQAM), Sébastien Gambs (UQAM), Mohamed Siala (LAAS-CNRS, ROC)
        • Internship: Julien Ferry (M1, 2019)
        • PhD student: Julien Ferry (2020-)
    • Subject : Mixed Integer Linear Program for Fair Scoring Systems in Multiclass Classification
      • Participants: Julien Ferry, Ulrich Aivodji (UQAM), Sébastien Gambs (UQAM), Mohamed Siala (LAAS-CNRS, ROC)
      • Internship: Julien Rouzot (M2, 2022)
    • Subject: Fair Decision Trees with SAT and MAX Sat models
      • Participants: Ulrich Aivodji (UQAM), Sébastien Gambs (UQAM), Mohamed Siala (LAAS-CNRS, ROC)
      • Internship: Maxence Biers (M2, 2020)
    • Subject: Interpretable models via SAT and MaxSAT
      • Participants: Mohamed Siala (LAAS-CNRS, ROC), Emmanuel Hébrard (LAAS-CNRS, ROC)
      • PhD Student: Hao Hu (2019-2022)
  • Hybridation of combinatorial search and machine learning methods
    • Subject:
      • Leveraging Reinforcement Learning and Combinatorial methods - Routing and Scheduling problems
      • Monte-Carlo Tree Search for combinatorial optimization - Routing and Scheduling problems
    • Participants: Emmanuel Hébrard (LAAS-CNRS, ROC), Siham Essodaigui and Alain Nguyen (Renault)
    • PhD Student: Valentin Antuori (Renault / LAAS-CNRS)
  • Clustering
    • Subject: Identification of Sargassum Algae in satellite image
    • Participants: Gilles Trédan (LAAS-CNRS, TSF) / CLS, CNES
    • Post-Doc: Estèle Glize (2019)
    • Internship : Robin Montérémal (M1, 2019)