STAMPS@CMU presents with ISSI:

Likelihood-Free Frequentist Inference: Confidence Sets with Correct Conditional Coverage

by Ann Lee (Department of Statistics & Data Science at Carnegie Mellon University)

Online webinar June 16, 2022 at 11:30-12:30 PM ET.
For connection information, please use the following link provided by the ISSI here.

Many areas of science make extensive use of computer simulators that implicitly encode likelihood functions of complex systems. Classical statistical methods are poorly suited for these so-called likelihood-free inference (LFI) settings, outside the asymptotic and low-dimensional regimes. Although new machine learning methods, such as normalizing flows, have revolutionized the sample efficiency and capacity of LFI methods, it remains an open question whether they produce confidence sets with correct conditional coverage. In this talk, I will describe our group’s recent and ongoing research on developing scalable and modular procedures for (i) constructing Neyman confidence sets with finite-sample guarantees of nominal coverage, and for (ii) computing diagnostics that estimate conditional coverage over the entire parameter space. We refer to our framework as likelihood-free frequentist inference (LF2I). Any method that defines a test statistic, like the likelihood ratio, can be adapted to LF2I to create valid confidence sets and diagnostics, without costly Monte Carlo samples at fixed parameter settings. In my talk, I will discuss where we stand with LF2I and challenges that still remain. (Part of these efforts are joint with Niccolo Dalmasso, Rafael Izbicki, Luca Masserano, Tommaso Dorigo, Mikael Kuusela, and David Zhao. The original LF2I framework is described in with a recent version in


Ann Lee is a a professor in the Department of Statistics & Data Science at Carnegie Mellon University (CMU), with a joint appointment in the Machine Learning Department. Dr Lee's interests are in developing statistical methodology for complex data and problems in the physical and environmental sciences. She co-directs the Statistical Methods for the Physical Sciences (STAMPS) research group at CMU, and is senior personnel in the NSF AI Planning Institute for Data-Driven Discovery in Physics at CMU.

Prior to joining CMU in 2005, Dr. Lee was the J.W. Gibbs Assistant Professor in the Department of Mathematics at Yale University, and before that she served a year as a visiting research associate at the Department of Applied Mathematics at Brown University. She received her PhD degree in Physics at Brown University, and her BSc/MS degree in Engineering Physics at Chalmers University of Technology in Sweden.