Data Analytics Research Seminar

2022-2023

  • 20 October 2022 from 10.30am to 11.45am (1h15 per talk including 30 minutes of broad introduction and 15 min questions)

Lu Yu (CREST-ENSAE)

    • Title: Mirror Descent Strikes Again: Optimal Stochastic Convex Optimization under Infinite Noise Variance

    • Abstract: We study stochastic convex optimization under infinite noise variance. Specifically, when the stochastic gradient is unbiased and has uniformly bounded (1 + κ)-th moment, for some κ ∈ (0, 1], we quantify the convergence rate of the Stochastic Mirror Descent algorithm with a particular class of uniformly convex mirror maps, in terms of the number of iterations, dimensionality and related geometric parameters of the optimization problem. Interestingly this algorithm does not require any explicit gradient clipping or normalization, which have been extensively used in several recent empirical and theoretical works. We complement our convergence results with information-theoretic lower bounds showing that no other algorithm using only stochastic first-order oracles can achieve improved rates. Our results have several interesting consequences for devising online/streaming stochastic approximation algorithms for problems arising in robust statistics and machine learning.


  • 3 November 2022 from 10.30am to 11.45am (1h15 per talk including 30 minutes of broad introduction and 15 min questions)

Nicolas Schreuder (Genova University)

    • Title: Fair statistical learning: a study of the Demographic Parity constraint

    • Abstract: In various domains, statistical algorithms trained on personal data take pivotal decisions which influence our lives on a daily basis. Recent studies show that a naive use of these algorithms in sensitive domains may lead to unfair and discriminating decisions, often inheriting or even amplifying biases present in data. In the first part of the talk, I will introduce and discuss the question of fairness in machine learning through concrete examples of biases coming from the data and/or from the algorithms. In a second part, I will demonstrate how statistical learning theory can help us better understand and overcome some of those biases. In particular, I will present a selection of recent results from two of my papers on the Demographic Parity constraint:

- 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.

  • 17 November 2022 from 10.30am to 11.45am (1h15 per talk including 30 minutes of broad introduction and 15 min questions)

Alfred Galichon (NYU)

    • Title: Estimating Matching Models: from theory to empirics

    • Abstract: I will review a methodology for the estimation of models of matching, with a focus on family economics. The theoretical foundations, the econometrics toolbox, and some empirical results will be discussed. This talk is partly a review of the existing literature, and partly based on two new papers:

- https://arxiv.org/abs/2204.00362.

- http://humcap.uchicago.edu/RePEc/hka/wpaper/Chiappori_Fiorio_Galichon_etal_2022_assortative-matching-income.pdf.

  • 8 December 2022 from 10.30am to 11.45am (1h15 per talk including 30 minutes of broad introduction and 15 min questions)

Guillaume A. Pouliot (The University of Chicago)


    • Title: TBA

    • Abstract: TBA

Archive 2013-2022

2021-2022

  • 9 June 2022 from 2.00 to 4.00pm (45 minutes per talk plus a 30 minutes coffee break) in Room N517

    • Alexandra Carpentier (Universität Potsdam).

    • Karim Lounici (Ecole Polytechnique).


  • 12 May 2022 from 2.00 to 4.00pm (45 minutes per talk plus a 30 minutes coffee break)

    • Victor-Emmanuel Brunel (ENSAE).

    • George Deligiannidis (University of Oxford).


  • 12 April 2022 from 2.00 to 4.00pm (45 minutes per talk plus a 30 minutes coffee break) in Room N517

    • Gilles Stupfler (ENSAI). Asymmetric least squares techniques for extreme risk assessment

    • Robert Adamek (Maastricht University). Local Projection Inference in High Dimensions


  • 3 March 2022 from 2.00 to 4.00pm (45 minutes per talk plus a 30 minutes coffee break) on Zoom

    • Giacomo Zanella (Bocconi University). Robust leave-one-out cross-validation for high-dimensional Bayesian models

    • Matthew Graham (University College London). Manifold MCMC methods for Bayesian inference in diffusion models


  • 13 December 2021 from 2.30 to 4.30pm (45 minutes per talk plus a 30 minutes coffee break) in Room N517

    • Christian Brownlees (Universitat Pompeu Fabra). Empirical Risk Minimization for Time Series: Nonparametric Performance Bounds for Prediction

    • Anders Kock (University of Oxford). Consistency of p-norm based tests in high dimensions: characterization, monotonicity, domination


  • 24 November 2021 from 2.30 to 4.30pm (45 minutes per talk plus a 30 minutes coffee break) in Room N517

    • Umut Simsekli (INRIA). Towards Building a Heavy-Tailed Theory of Stochastic Gradient Descent for Deep Neural Networks

    • Valentin De Bortoli (University of Oxford). Diffusion Schrödinger Bridge with Applications to Score-Based Generative Modeling

2018-2019

  • November 22, 2018 - 1:00 pm to 3:00 pm - ESSEC Cergy (N305)

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

  • October 16, 2018 - 5:15 pm to 6:15 pm - ESSEC LA Défense (CNIT), s. 344

Prof. Arijit Chakrabarty (Indian Statistical Institute, Kolkata)

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

2017-2018 Program:

  • TIME SERIES WORKSHOP 2018, Wednesday April 11 - 2018, 2:30 pm to 5:40 pm, Room N516

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:

  • July 4, 2017 - from 10:30 am to 12:00 pm - Cergy Room N105:

Prof. Aurore Delaigle (University of Melbourne)

Analyzing Partially Observed Functional Data

  • April 21, 2017 - from 1:00 pm to 4:00 pm - Cergy Room N305:

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

  • February 24, 2017 - from 2:00 pm to 5:00 pm - IBM Bois Colombes :

Internet of Things & Predictive Analytics

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

  • November 25, 2016 - from 11:45 am to 1:15 - Cergy, Room N405:

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

  • September 24, 2015:

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

2014-15 Program

SUBPAGES (1): 2013-14 PROGRAM OF ECONOMETRICS & STATISTICS SEMINARS