The seminar is normally held on Mondays at 14:00 (2:00 PM CET).
Most sessions are held in-person, usually in room 3001. Exceptionally, some sessions may be held remotely, in which case Zoom links are circulated in the mailing list.
The seminar is open; in order to be added to the mailing list, please send an email to Leyla Marzuk (first [dot] last [at] ensae [dot] fr).
Vincent Divol (CREST, ENSAE)
Anna Korba (CREST, ENSAE)
Jaouad Mourtada (CREST, ENSAE)
Monday, December 1, 2:00pm
Antonio Ocello (CREST, ENSAE)
Convergence Analysis of Diffusion Models in Wasserstein distance: a log-concave story
Generative models are attracting growing attention across various applied domains, including insurance and finance. Their potential lies in their capacity to capture and reproduce complex data patterns, crucial for realistic modeling and decision-making under uncertainty. Among the available methods, Score-Based Generative Models (SGMs), also known as diffusion models, offer a flexible framework to sample from complex, high-dimensional distributions. However, a key challenge lies in rigorously understanding their convergence.
In this talk, I will present recent advances in the theoretical analysis of SGMs. First, I will provide explicit bounds on Wasserstein-2 distance under the log-concave assumption of the target data distribution. Second, I will generalize this bound beyond the log-concave settings—such as for mixtures of Gaussians.
This talk is based on joint work with Stanislas Strasman, Claire Boyer, Sylvain Le Corff, and Vincent Lemaire (TMLR 2024 – https://openreview.net/forum?id=BlYIPa0Fx1), as well as a recent collaboration with Marta Gentiloni-Silveri (ICML 2025 – https://arxiv.org/pdf/2501.02298).
Monday, December 8, 2:00pm
Sinho Chewi (Yale University)
Monday, January 5, 2:00pm
Olga Klopp (ESSEC)
Monday, January 19, 2:00pm
Elsa Cazelles (CNRS, IRIT)
Past sessions from 2025-26 academic year. See other tabs for sessions from previous years.
Monday, September 15, 2:00pm
Vianney Perchet (CREST, ENSAE)
Last Iterate Convergence for Uncoupled Learning in Zero-Sum Games with Bandit Feedback
In this talk, I will introduce the problem of learning in zero-sum game, and especially for the problem of "last-iterate" convergence, unlike the traditional literature that looks at the average convergence (we argue it makes more sense). The interesting property is that the optimal rate is T^{-1/4} which is quite unusual (and unexpected) in this literature.
Monday, September 29, 2:00pm
Pierre Marion (INRIA Paris)
Large Stepsizes Accelerate Gradient Descent for (Regularized) Logistic Regression
Deep learning practitioners usually use large stepsizes when training neural networks. To understand the impact of large stepsizes on training dynamics, we consider the simplified setting of gradient descent (GD) applied to logistic regression with linearly separable data, where the stepsize is so large that the loss initially oscillates. We study the training dynamics, and show convergence and acceleration compared to using stepsizes that satisfy the descent lemma. I will show some key ideas from the proof and, if time allows, discuss what happens when adding a regularization term.
Monday, October 6, 2:00pm
Raphaël Berthier (INRIA, Sorbonne Université)
Diagonal linear networks and the lasso regularization path
Diagonal linear networks are neural networks with linear activation and diagonal weight matrices. The interest in this extremely simple neural network structure is theoretical: its implicit regularization can be rigorously analyzed. In this talk, I will show that the training trajectory of diagonal linear networks is closely related to the lasso regularization path, even when no explicit sparse penalization is used. In this connection, the training time plays the role of an inverse regularization parameter. As a consequence, an earlier stopping time leads to a sparser linear model.
Monday, November 3, 2:00pm
Christophe Giraud (Université Paris Saclay)
Computational barriers in learning: high-dimensional phenomenon
In many high-dimensional problems, the best polynomial-time estimators fall short of the information-theoretic limits that are provably attainable without computational constraints. The low-degree polynomial framework has emerged as a powerful tool for analyzing the fundamental capabilities of polynomial-time algorithms. Building on our recent advances in the study of low-degree lower bounds, we will explore two notable high-dimensional phenomena:
1. Complex structures – We will show that polynomial-time algorithms may fail to leverage certain low-dimensional yet complex structures.
2. Beyond predictions from statistical physics – We will highlight cases where the computational barriers suggested by tools from statistical physics do not remain valid in specific high-dimensional regimes.
Monday, November 10, 2:00pm
Marc Jourdan (EPFL)
Advances in Pure Exploration in Bandits: Non-Asymptotic and Private
In pure exploration problems for stochastic multi-armed bandits, the goal is to answer a question about a set of unknown distributions (for example, the efficacy of a treatment) by strategically sampling from them, while providing guarantees on the returned answer. The archetypal example is the best arm identification problem, where the task is to find the arm with the largest mean. Top Two algorithms, which select the next arm to sample from among a leader and a challenger, have received significant attention in recent years due to their simplicity and interpretability.
In this talk, I will present recent advances on two complementary aspects of pure exploration: achieving non-asymptotic guarantees and ensuring differential privacy. First, we propose a Top Two algorithm which has an asymptotically optimal expected sample complexity, and also provides anytime guarantees on the probability of misidentifying a sufficiently good arm. Second, we show how the Top Two principle can be combined with differential privacy mechanisms, leading to algorithms that preserve near-optimal efficiency while ensuring privacy guarantees. These results not only deepen our theoretical understanding but also enable more practical and privacy-aware bandit algorithms.
Monday, November 24, 2:00pm
Arthur Stéphanovitch (CREST, ENSAE)
Regularity of the score and convergence rates of generative diffusion models
We show that in generative modeling with diffusion processes, the score function naturally inherits the regularity of the target distribution. This adaptive behavior provides a concise proof that these models attain minimax optimal rates for density estimation.