• November 3, 2017

    What does it mean to be Bayesian?

    Bayesian statistics provides powerful theoretical tools, but it is also sometimes viewed as a philosophical framework. This has lead to rich academic debates over what statistical learning is and how it should be done. Academic debates are healthy when their content is precise and independent issues are not conflated. In this post, I argue that it is not always meaningful to consider the merits of Bayesian learning directly, because the fundamental questions surrounding it encompass not one issue, but several, that are best understood independently. These can be viewed informally as follows.

  • August 16, 2017

    Deep Learning with function spaces

    Deep learning is perhaps the single most important breakthrough in statistics, machine learning, and artificial intelligence that has been popularized in recent years. It has allowed us to classify images - for decades a challenging problem - with nowadays usually better-than-human accuracy. It has solved Computer Go, which for decades was the classical example of a board game that was exceedingly difficult for computers to play. But what exactly is deep learning?

  • July 5, 2017

    Bayesian Learning - by example

    Welcome to my blog! For my first post, I decided that it would be useful to write a short introduction to Bayesian learning, and its relationship with the more traditional optimization-theoretic perspective often used in artificial intelligence and machine learning, presented in a minimally technical fashion. We begin by introducing an example.