What are some good blogs for Mathematical Statistics and Machine Learning? I am looking for  blogs that focus on the mathematical theory of Statistics and Machine Learning, ideally at a research or "advanced" level.
The blog doesn't have to be solely about these topics but ideally most of the posts would be exposing the mathematical theory of an idea/concept/algorithm that is either directly or closely related to them. Here is an example of what I'm looking for, and I will add another one as an answer (Disclaimer : I have no affiliation with any of the authors of the blogs I link):

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*Gregory Gundersen's blog neatly presents the theory of many well-known (and some lesser-known) algorithms and results in Statistics, such as Conjugate Gradient Descent, Ordinary Least Squares, Hidden Markov Models... Some of the posts contain illustration with available source code

Please limit your answer to less than 2-3 links and provide a short description for each blog, as above.
 A: Francis Bach's Machine Learning Research blog is an "easy to digest" introduction to some of his research works and related topics ("easy" as in easier than reading the original papers).
It contains many excellent in-depth writings about kernel methods, optimization algorithms, linear algebra and highlights how these topics interact with each other as well as their applications in Machine Learning/Statistical Learning Theory.
A: Andrew Gelman: https://statmodeling.stat.columbia.edu. Gelman is a professor of statistics and political science at Columbia, and has co-authored several statistics books, including Bayesian Data Analysis and Regression and Other Stories. I strongly disagree with most of his politics, but his statistics is generally sound.
A: https://statisticaloddsandends.wordpress.com/ reminds me of Gunderson blog, nicely written with code and clear explanations.
A: ICLR recently introduced its Blog Track and its taken inspiration from some blogs like Bach's. Best thing is that it's peer-reviewed and contains diverse topics from diverse authors (often a group of authors).
A: In the last couple of years I have warmed up to using geometry to understand deep learning models, and indeed various types of statistical models. While I recommend the book Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges, you can also find a list of blogs related to the topic.
A: An Outsider's Tour of Reinforcement Learning by Ben Recht gives a short introduction into RL and draws connection to control theory.
A: This is neither really a blog nor just about statistics and many times very basic, but I found many good advices and ideas in there so I decided to add it as an answer
https://chrisalbon.com/#code_statistics
A: Towards data science a collection of articles focussing on data science, machine learning, artificial intelligence and programming. It is written by various authors. The articles often focus on explaining some technique or area.
A quick search finds some links on the website here  but possibly there are more indirect links.
