Program
2024
May 7th - P.L. Cauvin
TBA
April 30th - Tianjiao Li
A simple uniformly optimal method without line search for convex optimization
March 19th - Tam Le, Waïss Azizian
The interplay between geometry and convergence in Bregman proximal methods, Waïss Azizian
Nonsmoothness in Wasserstein distributionally robust models, Tam Le
February 20th - Florian Vincent
Robustify your model with SkWDRO
February 13th - Krishna Pillutla
Correlated Noise Provably Beats Independent Noise for DP Learning
February 8th - Vincent Beck
Fonction sous-modulaire : une brève introduction, quelques exemples et une application à la théorie des groupes.
January 16th - Michel Volle
Celebrating Moreau Heritage, Ep. 2: Moreau conjugacy and the extended S-procedure
January 9th - Gilles Bareilles
Stratifications of nonsmooth (nonconvex) functions
2023
December 12th - Tam Le
Nonsmooth nonconvex subgradient method: the differential inclusion approach.
November 29th - Hamza Ennaji
Stochastic Monotone Inclusion with Closed Loop Distributions
November 24th - Mathieu Besançon
Constrained Nonlinear Optimization with Frank-Wolfe
November 14th - Jérôme Malick
Celebrating Moreau Heritage, Ep. 1: non-convex stopping criteria and convergence.
October 24th - Yu-Guan Hsieh
Decision-Making in multi-agent systems: delays, adaptivity, and learning in games
October 17th - Yu-Guan Hsieh
Beyond Training's End: A Deep Dive into Stable Diffusion's Extended Journey
September 22nd - Yassine Laguel
A robust perspective on stochastic first-order methods.
September 14th - Yanis Bekri
On robust counterpart of linear inverse problems
June 29th - Michael Overton
Crouzeix’s Conjecture
June 6th - Pierre Louis Cauvin
The robustness of game dynamics under random perturbations
May 30th - Waïss Azizian
Statistical properties of Wasserstein distributionally Robust models
May 23rd - Charles Dapogny
Shape and topology optimization via a level-set based mesh evolution method
April 25th - Michael Arbel
Bilevel Optimization in Machine Learning: Beyond Strong Convexity
April 4th (3pm) - Anatole Gallouet
Strong c-concavity and staiblity in optimal transport
March 28th (3pm) - Gabriel Peyré
Scaling optimal transport with entropic regularization
March 21st (3pm) - Yu-Guan Hsieh
Score-based diffusion models, meta-learning for bandits, and Stable Diffusion
March 16th (3:30 pm) - Anton Rodomanov
Stochastic Gradient Methods for Minimization in Relative Scale
March 15th (10:30 am) - Yurii Nesterov
Primal subgradient methods with predefined stepsizes.
February 28th (10 am) - Tam Ngoc
Subgradient sampling for nonsmooth nonconvex minimization
February 23th (3:30 pm) - Hadrien Hendrikx
Principled Approaches for Practical Distributed Learning.
2022
December 7th - Victor Mercklé
Reformulation convexe d'un réseau de neurones ReLU
November 27th - Radu Dragomir
Quartic condition number
November 23rd - G. Bareilles
Newton methods for structured nonsmooth optimization
July 12th - Interns presentations
Antoine Obled - Calcul efficace des deux premières dérivées du logarithme matriciel dans les cas complexe, réel et symétrique
Ieva Petrulionyte - Estimation and prediction for "generalized" GLM's
Julien Prando - Distributionally Robust Optimization Problem
July 7th - Pierre Maréchal (with EDP seminar)
Forme variationnelle de la mollification: un tour d'horizon
July 1st - Y.G. Hsieh
Anticipating the future for better performance: Optimistic gradient algorithms for learning in games
June 9th - G. Bareilles
Conjuguer Newton et gradient-proximal pour l'optimisation non-lisse
May 19th - F. Bouchard
Comment dériver une fonction matricielle ? Une facon simple et efficace.
May 5th - Y.G. Hsieh
Robbins-Siegmund and Almost Sure Last-iterate Convergence
April 14th - Y. Nesterov
Cubic regularization of Newton's method
April 7th - N. Laurent
Mode Retrieval Based on Linear Chirp Approximation of Multicomponent Signals
March 31th - G. Bareilles
Proximity operator and structure detection for almost-prox-simple functions
March 24th - J. Malick
News on the newsvendor
March 17th - F. Iutzeler
Jérôme's choice: linear convergence and spectral radius
2021
December 10rd - J. Malick
Math Matshup
December 3rd - G. Bareilles
Newton acceleration on manifolds identified by proximal-gradient methods
November 26th - F. Iutzeler
An introduction to bundle methods
November 12nd - G. Bareilles
Some aspects of "An algorithm for nonsmooth composite optimization problems"
October 22nd - Y. Laguel
Fooling Deep Neural Networks via Carlini-Wagner type methods
October 15th - A. Delcourt
GPU-Accelerated CZT Detector Simulation with Charge build-up effects.
October 8th - D. Zhemchuzhnikov
6DCNN with roto-translational convolution filters for volumetric data processing.
October 1st - K. Olechnovič
Analyzing macromolecular structures using the Voronoi tessellation of atomic balls.
September 17th - M. Benlahsen
Sequential Quadratic Programming with the Julia language.
June 24th - A. Judistsky
Sparse recovery by reduced variance stochastic approximation.
June 17th - G. Bareilles
Characterizations of optimality of points for non-smooth functions.
June 10th - S. Grudinin
Deep learning entering the post-protein structure prediction era: new horizons for structural biology.
June 3rd - W. Azizian
Graph Neural Networks and Approximation.
May 28th - Y. Laguel
Variance Reduction via the method of control variates.
May 20th -Y-G. Hsieh
Decentralized (sub)gradients-based methods.
May 6th - B. Thibert
An introduction to semi-discrete optimal transport.
April 23th - P. Mertikopoulos
A powerful application of mirror descent.
April 16th - R. Hildebrand
Semi-definite representability of convex sets - Part II.
April 9th - R. Hildebrand
Semi-definite representability of convex sets - Part I.
April 2nd - F. Iutzeler
I gave a 6h-course on optimization for ML!
March 26th - Y. Laguel
Good practices for numerical experiments in learning and optimization.
March 19th - J. Malick
Smooth(ing) Criminals
March 12th - G. Bareilles
Solutions of sparse recovery problems with l1 norm
March 5th -P. Mertikopoulos
Stochastic approximation for the working mathematician (Part. II)
February 26th -P. Mertikopoulos
Stochastic approximation for the working mathematician
February 10th -R. Hildebrand
Self-associated cones
January 22th -Y-G. Hsieh
Optimistic mirror descent with adaptative learning rate
January 15th -Y. Laguel
Learning Fairness for Fairness Learning
2020
December 18th -A. Ivanova
Numerical methods for allocation in Networks
December 11th - M. Danilova
Stochastic Optimization wih Heavy-Tailed Noise via Accelerated Gradient Clipping
November 27th - S. Tanji
Unified analysis of optimization algorithms using Lyapunov functions
November 20th - F. Iutzeler
Nonsmooth regularizations in Machine Learning: structure of the solutions, identification, and applications
November 13th - G. Bareilles
Proximity operator of nonsmooth, non-convex functions
October 16th - R. Hildebrand
A tutorial on Interior Point Methods
October 9th - Y. Laguel
Devices Heterogeneity in Federated Learning : a superquantile approach
October 2nd - F. Iutzeler
Kurdyka-Lojasiewicz inequality for the convergence and rate of optimization methods
September 29th - D. Grischenko:
Proximal optimization with automatic dimension reduction for large scale learning
September 18th - Y-G. Hsieh:
Dual averaging for online optimization, delay, and perturbed iterate analysis
September 11th - P. Mertikopoulos:
Introduction to game theory and online learning
September 4th - [Special Session]:
Preparation of Smai-Mode talks
July 17th - W. Azizian:
Convergence of the last iterates of proximal methods for strongly monotone variational inequalities
July 10th - Y. Laguel:
Fast Smoothing Procedures for a class of Support functions
July 3rd - G. Bareilles:
Introduction to Partial Smoothness
February 28th - G. Bareilles:
Smooth strongly convex interpolation and exact worst-case performance of first-order methods
February 14th - J. Malick:
Operational Intelligence
February 7th - R. Hildebrand:
Optimal Control, Methods and Applications (Part. II)
January 24th - R. Hildebrand:
Optimal Control, Methods and Applications
January 17th - K. Antonakopoulos:
A descent lemma beyond Lipschitz gradient continuity: First-order methods revisited and applications
January 10th - Y-G. Hsieh:
Saddle-point problems, Variational inequality, and Extra-gradient
2019
December 7th - Y. Laguel:
Convergence proof for local SGD in the non-IID setting
November 29th - F. Iutzeler:
A tutorial on splitting methods
November 22th - G. Bareilles:
Newton methods for optimization over unconstrained spaces and manifolds
November 15th - Y. Laguel:
Penalty methods, bilevel problems and their connection with chance constrained problems
November 8th - J. Malick:
Genericity in optimization : beauties and craps said on its behalf
October 17th - D. Grischenko:
Developments on personal research projects
October 11th - Y-G. Hsieh:
Constant step-size stochastic gradient descent for strongly convex functions
September 20th - Y. Laguel:
An analysis of Nesterov accelerated gradient with ODE theory