PR[AI]RIE workshop

10 November 2021

Salle Raymond Aron, Université Paris Dauphine-PSL, Place du Maréchal de Lattre de Tassigny, 75016 Paris

Inscriptions closes / Registration closed

This is a physical workshop, no streaming is planned for this event.



08:50-09:00 Opening

Jean Ponce and Isabelle Ryl

09:00-10:40 Session I

09:00-09:20 Francis Bach (Inria, PRAIRIE), “Kernel sums of squares for optimization and beyond”

09:20-09:40 Aymeric Dieuleveut (Ecole Polytechnique, Hi! Paris), “Federated Learning with compression”

09:40-10:00 Gabriel Peyré (ENS-PSL, PRAIRIE), “Scaling optimal transport for high-dimensional Learning”

10:00-10:20 Edouard Oyallon (CNRS, SCAI), “Learning is boring: image classification with patches”

10:20-10:40 Rachel Bawden (Inria, PRAIRIE), “Handling Variation in Text with Machine Translation”

10:40-11:00 Coffee break

11:00-12:40 Session II

11:00-11:20 Thierry Poibeau (CNRS & ENS-PSL), “Poetry generation, around Oupoco”

11:20-11:40 Martial Hebert (Carnegie-Mellon University), “Robust AI”

11:40-12:00 Justin Carpentier (Inria, PRAIRIE), “Robotics – What should be really learned?”

12:00-12:20 Raphaël Porcher (Université de Paris, PRAIRIE), “Stochastic implementation of individualized treatment rules”

12:20-12:40 Nicholas Ayache (Inria, 3IA Côte-d’Azur, “AI for medical imaging – The role of  models”

12:40-14:10 Lunch and posters

14:10-15:50 Session III

14:10-14:30 Alexandre Gramfort (Inria, DATAIA), “Bridging the gap between neurosciences and machine learning”

14:30-14:50 Laura Cantini (CNRS, IBENS-ENS-PSL, PRAIRIE), “Single-cell multi-modal data integration”

14:50-15:10 Jean-Baptiste Masson (Institut Pasteur, PRAIRIE), “Physics-informed Bayesian learning: from random walks to fetus morphology”

15:10-15:30 Umut Simsekli (Inria, PRAIRIE), “Towards building a heavy-tailed theory of stochastic gradient descent for deep neural networks”

15:30-15:50 Julien Mairal (Inria, MIAI), “Lucas-Kanade reloaded: End-to-end super-resolution from raw image bursts”

15:50-16:10 Coffee break

16:10-17:30 Session IV

16:10-16:30 Cordelia Schmid (Inria, PRAIRIE), “Do you see what I see? Large-scale learning from multimodal videos”

16:30-16:50 Jérôme Lang (Dauphine-PSL, PRAIRIE), “AI for collective decision making”

16:50-17:10 Jérôme Bolte (TSE School of Economics, ANITI), “Conservative calculus: a variational calculus for nonsmooth algorithmic differentiation”

17:10-17:30 Clément Royer (Dauphine-PSL, PRAIRIE), “Black-box optimization based on probabilistic properties”

17:30-18:00 Keynote and closure

Yann LeCun (New York University and Facebook AI Research), “The future is self-supervised”