Nicolas Chopin is Professor of Data Sciences/Statistics/Machine Learning at ENSAE, Institut Polytechnique de Paris. He will present his recent work on which he collaborated with with Théo Voldoire (Harvard) and Robin Ryder (Imperial).

This talk is hosted by the UCL AI Centre and ELLIS (European Laboratory for Learning and Intelligent Systems).

You can attend in person or online. Lunch will be available at 12:30. Please note: we regret that this venue is not accessible for wheelchair users.

Talk title:

"Saddlepoint Monte Carlo and its application to exact ecological inference"

Talk abstract:

Assuming X is a random vector and A a non-invertible matrix, one sometimes need to perform inference while only having access to samples of Y = AX. The corresponding likelihood is typically intractable. One may still be able to perform exact Bayesian inference using a pseudo-marginal sampler, but this requires an unbiased estimator of the intractable likelihood.
We propose saddlepoint Monte Carlo, a method for obtaining an unbiased estimate of the density of Y with very low variance, for any model belonging to an exponential family. Our method relies on importance sampling of the characteristic function, with insights brought by the standard saddlepoint approximation scheme with exponential tilting. We show that saddlepoint Monte Carlo makes it possible to perform exact inference on particularly challenging problems and datasets. We focus on the ecological inference problem, where one observes only aggregates at a fine level. We present in particular a study of the carryover of votes between the two rounds of various French elections, using the finest available data (number of votes for each candidate in about 60,000 polling stations over most of the French territory).
We show that existing, popular approximate methods for ecological inference can lead to substantial bias, which saddlepoint Monte Carlo is immune from. We also present original results for the 2024 legislative elections on political centre-to-left and left-to-centre conversion rates when the far-right is present in the second round. Finally, we discuss other exciting applications for saddlepoint Monte Carlo, such as dealing with aggregate data in privacy or inverse problems.

Speaker bio:

Nicolas Chopin (PhD, Université Pierre et Marie Curie, Paris, 2003) has been Professor of Statistics at ENSAE, Paris, since 2006. He was previously a lecturer at Bristol University (UK).

NC is a fellow of the IMS, and a current or former associate editor for Annals of Statistics, Biometrika, Journal of the Royal Statistical Society, Statistics and Computing, and Statistical Methods & Applications. He has served as a member (2013-14) and secretary (2015-16) of the research section committee of the Royal Statistical Society. He received a Savage Award for his doctoral dissertation in 2002.

His research interests include computational statistics, Bayesian inference, and probabilistic machine learning.

UCL AI Centre/ELLIS Seminar - Professor Nicolas Chopin