Thematic team Statistics, data, algorithms

## Statistics seminar

#### Day, hour and place

Tuesday at 09:30, Sophie Germain en salle 1013 / Jussieu en salle 15-16.201

#### Contact(s)

### Next talk

Statistics seminar

Tuesday December 6, 2022, 9:30AM, Jussieu en salle 15-16.201

**Vianney Perchet** (ENSAE) *To be announced.*

#### Talks calendar

To add the talks calendar to your agenda, subscribe to this calendar by using this link.

### Previous talks

#### Year 2022

Statistics seminar

Tuesday November 22, 2022, 9:30AM, Sophie Germain en salle 1013 / Jussieu en salle 15-16.201

**Morgane Austern** (Harvard University) *To split or not to split that is the question: From cross validation to debiased machine learning.*

Statistics seminar

Tuesday November 8, 2022, 9:30AM, Jussieu en salle 15-16.201

**Arshak Minasyan** (CREST-ENSAE) *All-In-One Robust Estimator of sub-Gaussian Mean*

Statistics seminar

Thursday October 20, 2022, 11AM, Jussieu en salle 15-16.201

**Misha Belkin** (University of California) *Neural networks, wide and deep, singular kernels and Bayes optimality*

Statistics seminar

Tuesday October 11, 2022, 9:30AM, Jussieu en salle 15-16.201 et retransmission

**Yifan Cui** (Zhejiang University) *Instrumental Variable Approaches To Individualized Treatment Regimes Under A Counterfactual World*

Statistics seminar

Tuesday September 27, 2022, 9:30AM, Jussieu en salle 15-16.201

**Emilie Kaufmann** (CNRS) *Exploration non paramétrique dans les modèles de bandits*

Statistics seminar

Tuesday May 31, 2022, 9:30AM, Sophie Germain en salle 1013 / Jussieu en salle 15-16.201

**Elsa Cazelles** (IRIT) *A novel notion of barycenter for probability distributions based on optimal weak mass transport*

Statistics seminar

Tuesday May 10, 2022, 9:30AM, Sophie Germain en salle 1013 / Jussieu en salle 15-16.201

**Guillaume Lecué** (CREST) *A geometrical viewpoint on the benign overfitting property of the minimum $\ell_2$-norm interpolant estimator.*

[1] Mikhail Belkin, Daniel Hsu, Siyuan Ma, and Soumik Mandal. Reconciling modern machine-learning practice and the classical bias-variance trade-off. Proc. Natl. Acad. Sci. USA, 116(32):15849–15854, 2019.

[2] Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, and Oriol Vinyals. Understanding deep learning (still) requires rethinking generalization. Commun. ACM, 64(3):107–115, 2021.

[3] Peter L. Bartlett, Philip M. Long, Gabor Lugosi, and Alexander Tsigler. Benign overfitting in linear regression. Proc. Natl. Acad. Sci. USA, 117(48):30063–30070, 2020.

[4] Peter L. Bartlett, Andreas Montanari, and Alexander Rakhlin. Deep learning: a statistical viewpoint. To appear in Acta Numerica, 2021.

[5] Mikhail Belkin. Fit without fear: remarkable mathematical phenomena of deep learning through the prism of interpolation. To appear in Acta Numerica, 2021.

[6] Alexander Tsigler and Peter L. Bartlett. Benign overfitting in ridge regression. 2021.

Statistics seminar

Tuesday April 19, 2022, 9:30AM, Sophie Germain en salle 1013 / Jussieu en salle 15-16.201

**Clément Marteau** (Université Lyon 1) *Supermix : régularisation parcimonieuse pour des modèles de mélange*

Statistics seminar

Tuesday April 5, 2022, 9:30AM, Sophie Germain en salle 1013 / Jussieu en salle 15-16.201

**Fabrice Grela** (Université de Nantes) *Minimax detection and localisation of an abrupt change in a Poisson process*

Statistics seminar

Tuesday March 22, 2022, 9:30AM, Sophie Germain en salle 1013 / Jussieu en salle 15-16.201

**Aymeric Dieuleveut** (Polytechnique) *Federated Learning and optimization: from a gentle introduction to recent results*

Refs:Mainly: Differentially Private Federated Learning on Heterogeneous Data, M Noble, A Bellet, A Dieuleveut, Aistats 2022, Link Preserved central model for faster bidirectional compression in distributed settings C Philippenko, A Dieuleveut, Neurips 2021 LinkIf time allows it (unlikely): Federated Expectation Maximization with heterogeneity mitigation and variance reduction, A Dieuleveut, G Fort, E Moulines, G Robin, Neurips 2021 Link

Statistics seminar

Tuesday March 8, 2022, 9:30AM, Sophie Germain en salle 1013 / Jussieu en salle 15-16.201

**Lihua Lei** (Stanford University) *Testing for outliers with conformal p-values*

Statistics seminar

Tuesday February 8, 2022, 9:30AM, Sophie Germain en salle 1013 / Jussieu en salle 15-16.201

**Élisabeth Gassiat** *Deconvolution with unknown noise distribution*

Statistics seminar

Tuesday January 25, 2022, 9:30AM, Sophie Germain en salle 1013 / Jussieu en salle 15-16.201

**Nicolas Verzelen** (Université de Montpellier) *Optimal ranking in crowd-sourcing problem*

This talk is based on a joint ongoing work with Alexandra Carpentier and Emmanuel Pilliat.

#### Year 2021

Statistics seminar

Tuesday December 14, 2021, 9:30AM, Sophie Germain en salle 1013 / Jussieu en salle 15-16.201

**Julie Delon** (Université de Paris) *Some perspectives on stochastic models for Bayesian image restoration*

Statistics seminar

Tuesday November 30, 2021, 9:30AM, Sophie Germain en salle 1013 / Jussieu en salle 15-16.201

**Frédéric Chazal** (INRIA) *A framework to differentiate persistent homology with applications in Machine Learning and Statistics*

However, the approaches proposed in the literature are usually

anchored to a specific application and/or topological construction, and do not come with theoretical guarantees.

In this talk, we will study the differentiability of a general map associated with the most common topological construction, that is, the persistence map. Building on real analytic geometry arguments, we propose a general framework that allows to define and compute gradients for persistence-based functions in a very simple way. As an application, we also provide a simple, explicit and sufficient condition for convergence of stochastic subgradient methods for such functions. If time permits, as another application, we will also show how this framework combined with standard geometric measure theory arguments leads to results on the statistical behavior of persistence diagrams of filtrations built on top of random point clouds.

Statistics seminar

Tuesday November 23, 2021, 9:30AM, Sophie Germain en salle 1013 / Jussieu en salle 15-16.201

**Yannick Baraud** (Université de Luxembourg) *Comment construire des lois a posteriori robustes à partir de tests ?*

Statistics seminar

Tuesday November 9, 2021, 9:30AM, Sophie Germain en salle 1013 / Jussieu en salle 15-16.201

**Alessandro Rudi** (INRIA) *PSD models for Non-convex optimization and beyond*

Statistics seminar

Tuesday October 19, 2021, 9:30AM, Sophie Germain en salle 1013

**Antoine Marchina** (Université de Paris) *Concentration inequalities for suprema of unbounded empirical processes*

Statistics seminar

Tuesday October 5, 2021, 9:30AM, Jussieu en salle 15-16.201

**Judith Rousseau** (Oxford) *Semiparametric and nonparametric Bayesian inference in hidden Markov models*

Joint work with D. Moss (Oxford).