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09:00-09:20 Session I: Introduction

Foreword by Jean-Paul Chabard (EDF) and Pascal Massart (FMJH)

Location: Auditorium
09:20-10:20 Session L1: Plenary lecture
Location: Auditorium
Teaching self-driving cars to be responsible: A mathematical formalism of the duty of care

ABSTRACT. An important element in making self-driving cars a real product, rather than a science project, is to provide clear safety guarantees. We argue that statistical guarantees give a very weak safety and propose instead a white-box, interpretable, mathematical model for safety assurance, which we call Responsibility-Sensitive Safety (RSS).

10:50-11:50 Session L2: Plenary lecture
Location: Auditorium
Distributional reinforcement learning

ABSTRACT. I'll talk about recent work related to the distributional reinforcement learning approach where the full return distribution is learnt instead of its expectation only. We generalize Bellman equations to this setting and describe a deep-learning approach for approximating those distributions. I will report experiments on Atari games.

11:50-12:00 Session C1: PGMO PhD Prize Ceremony

Presentation of the 2019 Prize, by Marianne Akian (SMAI-MODE), Anne Auger (Prize Committee) and Céline Gicquel (ROADEF)

Location: Auditorium
12:00-12:30 Session L3: Lecture by recipient of the PhD Prize
Location: Auditorium
Contributions to the mean field games theory

ABSTRACT. The PGMO Prize 2019 is awarded to Charles BERTUCCI for his PhD thesis entitled "Contributions à la théorie des jeux à champ moyen ». His PhD, completed at the Université Paris Dauphine under the supervision of Pierre-Louis Lions, is concerned with the study of several problems of significant interest arising in the theory of mean field games. His work has been recognized as an extremely important contribution to the theory with the introduction of deep and original techniques.

12:30-14:00Lunch Break
14:00-16:00 Session L4: Plenary lectures
Location: Auditorium
Integer optimization and machine learning, some recent developments

ABSTRACT. In recent years the interface between machine learning and integer optimization has quickly been developing. We will give some examples of recent developments in the use of machine learning for learning how to branch and cut, and the use of integer optimization in adversarial machine learning.

Dynamic Programming for IP

ABSTRACT. In this talk, I will survey some recent results on the complexity of integer programming in the setting that lends itself to dynamic programming approaches. These include the general integer programming problem in standard form with small coefficients and integer programming problems with block structure. The field of parameterised complexity has developed tools to provide lower bounds on the complexity of IP in these cases. The goal of this talk is to give an overview on recent progress and open problems. 

16:35-17:05 Session L5: Lecture by recipient of the PhD Prize
Location: Auditorium
Combinatorial Aspects of the Unit Commitment Problem

ABSTRACT. The PGMO Prize 2019 is awarded to Cécile ROTTNER for her PhD thesis entitled « Aspects combinatoires du Unit Commitment Problem ». Cécile ROTTNER obtained her PhD from Sorbonne Université under the supervision of Pascale Bendotti of EDF R&D and Pierre Fouihoux of the computer science lab LIP6. In her thesis, she investigates the Unit Commitment Problem from the point of view of combinatorial and discrete optimization. She is awarded the PGMO Prize 2019 in recognition of her impressive treatment of the Unit Commitment Problem which contains a broad spectrum of novel and relevant insights and approaches.

17:05-17:30 Session L6: Conference closure

Conference closure by Sandrine Charousset and Stéphane Gaubert

Location: Auditorium