The Stats-Machine Learning-Econometrics Seminar. Time: 14:00 pm - 15:00 pm Date: 9th of January 2019 Place: Room 3001. Pierre Alquier (CREST) - "Introduction to Online Prediction" Aim of the talk: Introductory lecture that will be divided into three main parts: relevance of the approach in Econometrics, summary of main results in the field, presentation of […]
The Stats-Machine Learning-Econometrics Seminar. Time: 14:00 pm - 15:00 pm Date: 6th of February 2019 Place: Room 3001. Victor-Emmanuel Brunel (CREST) - "Different Forms of Negative Dependence in Statistics" Aim of the talk: Introductory lecture. Sponsors: CREST
The Stats-Machine Learning-Econometrics Seminar. Time: 10:30 am - 11:30 am Date: 21st of May 2019 Place: Room 3001. Jérémy Fouliard (LBS) - "Answering the Queen : online machine learning and financial crises", joint work with Hélène Rey (LBS) Abstract: Predicting systemic financial crises has been elusive. It is however a key input for macroprudential policy. […]
The Stats-Machine Learning-Econometrics Seminar. Time: 14:00 pm - 15:00 pm Date: 6th of November 2019 Place: Room 3001. Badr-Eddine CHERIEF-ABDELLATIF(CREST) - "Theoretical Study of Variational Inference" Abstract : Bayesian inference provides an attractive learning framework to analyze and to sequentially update knowledge on streaming data, but is rarely computationally feasible in practice. In the recent years, […]
Statistical Seminar: Every Monday at 2:00 pm. Time: 2:00 pm - 3:00 pm Date: 20th November 2023 Place: 3001 Anindya De (University of Pennsylvania) - TO BE ANNOUNCED Abstract: Organizers: Cristina BUTUCEA (CREST), Karim LOUNICI (CMAP) , Jaouad MOURTADA (CREST) Sponsors: CREST-CMAP
Statistical Seminar: Every Monday at 2:00 pm. Time: 2:00 pm - 3:00 pm Date: 27th November 2023 Place: 3001 Zacharie Naulet (Université Paris-Saclay) "Frontiers to the learning (and clustering) of Hidden Markov Models" Abstract: Hidden Markov models (HMMs) are flexible tools for clustering dependent data coming from unknown populations, allowing nonparametric identifiability of the population […]
Statistical Seminar: Every Monday at 2:00 pm. Time: 3:00 pm - 4:00 pm Date: 4th December 2023 Place: ZOOM Anindya De (University of Pennsylvania) "Testing convex truncation" Abstract: We study the basic statistical problem of testing whether normally distributed n-dimensional data has been truncated, i.e. altered by only retaining points that lie in some unknown truncation […]
Statistical Seminar: Every Monday at 2:00 pm. Time: 3:00 pm - 4:00 pm Date: 22th January 2024 Place: 3001 Eugène NDIAYE (Apple Research) "Finite Sample Confidence Sets with "Minimal" Assumptions on the Distribution" Abstract: If you predict a label y of a new object with $hat{y}$, how confident are you that ”y = $hat{y}$”? The […]
Statistical Seminar: Every Monday at 2:00 pm. Time: 2:00 pm - 3:15 pm Date: 25th March 2024 Place : 3001 Vincent DIVOL (Université Paris Dauphine) -"Entropic estimation of optimal transport maps in the semi-discrete case" Abstract: We study the question of estimating the optimal transport map T between two distributions P and Q, based on […]
Statistical Seminar: Every Monday at 2:00 pm. Time: 2:00 pm - 3:15 pm Date: 28th March 2024 Place : 3001 Antoine MAILLARD (ETH Zurich) - From the theory of disordered systems to the mathematics of data science Abstract: I will introduce a connection between statistical learning or high-dimensional inference, and models of statistical mechanics […]
Statistical Seminar: Every Monday at 2:00 pm. Time: 2:00 pm - 3:15 pm Date: 3rd April 2024 Place : 3001 Baptiste Goujaud (Ecole Polytechnique) - t.b.a Abstract: Organizers: Jaouad MOURTADA (CREST), Anna KORBA (CREST), Karim LOUNICI (CMAP) Sponsors: CREST-CMAP
Statistical Seminar: Every Monday at 2:00 pm. Time: 2:00 pm - 3:15 pm Date: 27th May 2024 Place: Room 3001 Nicolas Schreuder (CNRS, Université Gustave Eiffel) - Efficient estimation of kernel mean embeddings Abstract: Kernel mean embeddings are a powerful tool to represent probability distributions over arbitrary spaces as single points in a Hilbert […]