BlackHC's Adventures

Why is the Bayesian model average (BMA) often hailed as the optimal choice for rational actors making predictions under uncertainty? Is this claim justified, and if so, what’s the underlying logic? [Read More]

In the first part of this two-part series on Function-Space Variational Inference (FSVI), we looked at the Data Processing Inequality (DPI). In this second part, we finally look at the relationship between FSVI, a method focusing on the Bayesian predictive posterior rather than the parameter space, and the DPI. We... [Read More]

In information theory, the data processing inequality (DPI) is a powerful concept. Informally, it tells us that processing data cannot increase the amount of contained information. In this two-part blog post, we will explore the DPI and its applications to function-space variational inference (FSVI). [Read More]

In this blog post, following the previous blog post1 on “Bayesian Appropriation: General Likelihood for Loss Functions”, we will examine and better understand parts of the paper “PACTran: PAC-Bayesian Metrics for Estimating the Transferability of Pretrained Models to Classification Tasks”2 (“PACTran”), which was presented as an oral at the ECCV... [Read More]

In this blog post, we explore how some losses could be rewritten as a Bayesian objective using ideas from variational inference—hence, the tongue-in-cheek “Bayesian Appropriation.” This can make it easier to see connections between loss functions and Bayesian methods (e.g. by spotting similar patterns in the wild). We will first provide... [Read More]