Date: Thursday, 15 February 2024, at 12:30pm (Paris) and 7:30pm (Singapore)

Dual format: ESSEC Paris La Défense (CNIT), Room TBA
and via Zoom, please click here

Fitting a model to data typically involves applying an iterative algorithm to minimize an empirical risk. However, given a particular empirical risk minimization problem, the process of algorithm selection is often performed via either expensive trial-and-error or appeal to (potentially) conservative worst case efficiency estimates and it is unclear how to compare and contrast algorithms in a principled and meaningful manner. In this talk, we present one potential avenue to obtain fine-grained, principled comparisons between iterative algorithms. We provide a framework—based on Gaussian comparison inequalities—to characterize the trajectory of an iterative algorithm run with sample-splitting on a set of nonconvex model-fitting problems with Gaussian data. We use this framework to demonstrate concrete separations in the convergence behavior of several algorithms as well as to reveal some nonstandard convergence phenomena.

Kind regards,
Jeremy Heng, Olga Klopp, Roberto Reno, and Marie Kratz
https://crear.essec.edu/crear-events/working-group-on-risk
and Riada Djebbar (Singapore Actuarial Society - ERM)