Optimization-Centric Generalizations of Bayesian Inference

13.05.2022 11:15 – 12:15

RESEARCH CENTER FOR STATISTICS SEMINAR / ABSTRACT

I summarize a recent line of research and advocate for an optimization-centric generalisation of Bayesian inference. The main thrust of this argument relies on identifying the tension between the assumptions motivating the Bayesian posterior and the realities of modern large-scale Bayesian Machine Learning. Our generalisation is a useful conceptual device, but also has methodological merit: it can address various challenges that arise when the standard Bayesian paradigm is deployed in a Machine Learning context—including robustness to model misspecification, robustness to poorly chosen priors, or inference in intractable likelihood models.

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