“Core-AI 2026” Thematic School

March

• March 11 — Nirupam Gupta (University of Copenhagen)
  Robustness, Privacy, and Fairness in Distributed Learning
  (co-organized by ML-MTP) – LIRMM Seminar Room, Building 4 – 4:00 p.m.
• March 17 — Arnak Dalalyan (CREST – ENSAE)
  Generative Models and GAN Theory – LIRMM Seminar Room, Building 4 – 4:00 p.m.
• March 25 — Axel Ngonga Ngomo (University of Leipzig)
  Knowledge Graphs – Jean-Jacques Moreau Lecture Hall, Building 2, St. Priest – 3:00 p.m.

April

• April 1 — Marie-Jeanne Lesot (LIP6, Sorbonne University)
  Explainable AI: Methods and Risks – LIRMM Seminar Room, Building 4 – 4:00 p.m.
• April 9 — Hadrien Hendrikx (Inria Grenoble)
  Distributed Optimization
  (co-organized by ML-MTP) – Jean-Jacques Moreau Lecture Hall, Building 2, St Priest – 4:00 p.m.
• April 15–16 — Mini-course (2 half-days)
  , Dino Ienco & Giuseppe Guarino (INRAE Montpellier)
  Semi-supervised learning and domain adaptation – Jean-Jacques Moreau Lecture Hall, Building 2, St Priest – 4:00 p.m.

May

• May 6 — Céline Robardet (INSA Lyon)
  : Understanding and Explaining Graph Neural Networks – Jean-Jacques Moreau Lecture Hall, Building 2, St Priest – 4:00 p.m.
• May 13 — Gérard Biau (Sorbonne University – French Academy of Sciences)
  -Learning with Physical Constraints (PINNs, Physics-Informed Kernel Learning)
  (session subject to extension) – Peytavin Lecture Hall at Polytech – 4:00 p.m.

About Core-AI

Core-AI highlights the fundamental principles, theoretical methods, and methodological approaches that underpin modern artificial intelligence.

The school offers a well-rounded science curriculum covering:

• Learning Theory
• Reliability and trust
• Distributed deep learning
• Generative models and kernels
• Large-scale data structures (graphs, computer vision, remote sensing)
• Explainability and Responsible AI

For more information, as well as to view the school’s other dates and events, please refer to the registration form.

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