Data Analytics Research Seminar

Incoming seminars

Céline Duval (Université de Lille)

Peter Radchenko (University of Sydney)

Gilles Stoltz ((Laboratoire de mathématiques d'Orsay, CNRS - Université Paris-Saclay & HEC Paris))

Past seminars 2023-2024

Sirio Legramanti (University of Bergamo)

Motivated by real-world data about subscriptions to the public transportation system of Bergamo (Italy) and its surroundings, we propose a method to incorporate properly transformed spatial covariates into a state-of-the-art stochastic block model, while inferring the weight of covariates. (Joint work with Valentina Ghidini and Raffaele Argiento)

Badr-Eddine Chérief-Abdellatif (LPSM, CNRS)

Nikolaus Schweizer (Tilburg University)

(Joint work with Anne Balter and Johannes M. Schumacher)

Artem Prokhorov (University of Sidney) 

Gábor Lugosi (Universitat Pompeu Fabra)

Claire Boyer (LPSM, Sorbonne Université)

The direct implementation of physics-informed kernel estimators can be tedious, and practitioners often resort to physics-informed neural networks (PINNs) instead. We offer some food for thought and statistical insight into the proper use of PINNs.

Matteo Barigozzi (Università di Bologna)

Matteo Barigozzi and Luca Trapin

Davide La Vecchia (University of Geneva)

Vincent Fortuin (Helmholtz AI/TUM)

Gérard Ben Arous (NYU)

The next step is to understand how the system finds these “summary statistics”.  This is done in the last work with the same authors and with Jiaoyang Huang (Wharton, U-Penn). This is based on a dynamical spectral transition of Random Matrix Theory: along the trajectory of the optimization path, the Gram matrix or the Hessian matrix develop outliers which carry these effective dynamics.

I will naturally first come back to the Random Matrix Tools needed here (the behavior of the edge of the spectrum and the BBP transition).

And then illustrate the use of this point of view on a few central examples of ML:  classification for Gaussian mixtures, and the XOR task.

References:  NeurIPS 2022, Best paper award, CPAM March 2024, ICLR May 2024, and Arxiv 2310.03010.

Archive 2013-2023



Nicolas Schreuder (Genova University) 

- A minimax framework for quantifying risk-fairness trade-off in regression (with E. Chzhen), Ann. Statist. 50(4): 2416-2442(Aug.2022).

- Fair learning with Wasserstein barycenters for non-decomposable performance measures (with S. Gaucher and E. Chzhen), arXiv preprint arXiv:2209.00427.

Alfred Galichon (NYU)  



Guillaume A. Pouliot (The University of Chicago)  

Dion Bongaert (RSM Erasmus University)  

Cesare Robotti (Warwick Business School)  



Prof. Taoufik Bouezmarni (Laval University)

Extended Lorenz curves for general random variables

Prof. Matei Demetrescu (Kiel University)

Nonlinear Predictability of Stock Returns? Parametric vs. non parametric inference in predictive regressions 

Prof. Arijit Chakrabarty (Indian Statistical Institute, Kolkata)

Spectra of Adjacency and Laplacian Matrices of inhomogeneous Erdös-Rényi Graphs

2017-2018 Program:

Organizers: Prof. Luc Bauwens, CORE - UCL, Fellow of the Institute of Advanced Studies UCP Université Paris-Seine, Guillaume Chevillon, ESSEC Business School, Prof. Jeroen Rombouts, ESSEC Business School

Prof. Xavier D’haultfoeuille (ENSAE - CREST)

Testing Rational Expectations Using Data Combination

Prof. Artem Prokhorov (University of Sydney)

On Semiparametric Estimation using Bernstein Copulas

2016-2017 Program: 

Prof. Aurore Delaigle (University of Melbourne)                                               

Analyzing Partially  Observed Functional Data

Prof. Valentina Corradi (University of Surrey)

Improved Tests for robust forecast comparison

Prof. Jean-David Fermanian (CREST)

The behavior of dealers and clients on the European corporate bond market: the case of Multi-dealer-to-client platforms

Prof. Bas Werker (Tilburg University)

Arbitrage Pricing Theory for Idiosyncratic Variance Factors

Prof. Karim ABADIR (Imperial College London)

Macro and financial markets: The memory of an elephant

Prof. Joerg Breitung (University of Cologne)

Multivariate tests for asset price bubbles

Internet of Things & Predictive Analytics                                                                

Reda Gomery (Deloitte), Marc Van Der Laan (AT&T), Thomas Watteyne (INRIA), Georges Uzbelger (IBM)

Prof. Juhyun Park, (Lancaster University)

Estimation of functional sparsity in nonparametric varying coefficient models

Yu-Wei Hsieh (University of Southern California)                                                                    

Seminar on the Econometrics of  Matching models                                                                           

2015-2016 Program

Prof. Christophe CROUX (Katholieke Universiteit Leuven)
Sparse Cointegration
Prof. Nikolay GOSPODINOV (Federal Reserve Bank of Atlanta)
Spurious Inference in Reduced-Rank Asset-Pricing Models
Prof. Otilia BOLDEA (Tilburg University)

Break-point Estimation in Panel data with fixed effects 

Prof. Cristina DAVINO (Università de Macerata, Italy) -Quantile Regression an overview of properties and applications

2014-15 Program