Agenda

Speaker: Omiros Papaspiliopoulos, Università Bocconi Milano

The talk will also be available via Zoom https://unive.zoom.us/j/85153268624
Meeting ID: 851 5326 8624
Passcode: SanMarco2

Abstract:
Generalized linear mixed models are the workhorse of applied Statistics. In modern applications, from political science to electronic marketing, it is common to have categorical factors with large number of levels. This arises naturally when considering interaction terms in survey-type data, or in recommender-system type of applications. In such contexts it is important to have a scalable computational framework, that is one whose complexity scales linearly with the number of observations $n$ and parameters $p$ in the model. Popular implementations, such as those in lmer, although highly optimized they involve costs that scale polynomially with $n$ and $p$. We adopt a Bayesian approach (although the essence of our arguments applies more generally) for inference in such contexts and design families of variational approximations for approximate Bayesian inference with provable scalability. We also provide guarrantees for the resultant approximation error and in fact link that to the rate of convergence of the numerical schemes used to obtain the variational approximation.
This is joint work with Giacomo Zanella (Bocconi) and Max Goplerud (Pittsburgh)

Bio Sketch:
Omiros Papaspiliopoulos is Full Professor in Statistics at the Department of Decision Sciences of the Bocconi University. Previously, Omiros held positions at the Universitat Pompeu Fabra, the University of Warwick, the University of Oxford University and Lancaster University. Omiros's research is primarily in computational Statistics, but his work spans a wide range of topics in the intersection of Statistics, Machine Learning, Probability and Applied Mathematics. Omiros is currently co-editor of the prestigious Biometrika.