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Defining statistical models in JAX? statmodeling.stat.columbia.edu/2024/10/08/def…

I wrote about one of my favourite algorithms — the Bayesian Online Changepoint Detection (BOCD) — using the example of detecting changepoints in a non-stationary stream of coin tosses. gerdm.github.io/posts/bocd-coi…

How much einsum is enough?

A little late, but happy to announce that our paper on Rough Transformers ⛰️ has been accepted at NeurIPS Conference! We present a way to make Transformers for temporal data more efficient and robust to irregular sampling through path signatures! Read on! #neurips2024 (1/7)

I'm happy to announce our new review paper on methods for online prediction (and segmentation) in the presence of non-stationarity, covering both gradual and sudden changes. arxiv.org/abs/2411.10153 Led by Gerardo Duran-Martin with Leandro Sánchez-Betancourt and Alex Shestopaloff.

Part I of a Bayesian’s take on (Kalman) filtering—without Bayes. I will cover filtering and applications—from classical tracking problems to time-series forecasting, sequential learning for neural networks, and fully-online reinforcement learning gerdm.github.io/posts/filterin…

We are excited to announce that registration for the inaugural post-Bayes workshop on May 15./16. at UCL is now open! Website: postbayes.github.io/workshop2025/ Registration link: tinyurl.com/postBayesWorks…

Part II of a Bayesian’s take on (Kalman) filtering—without Bayes. All about the basics of state-space models and their measures of uncertainty. grdm.io/posts/filterin…

Happy to announce that our paper on (generalised) Bayesian online learning in non-stationary environments has been accepted at Transactions on Machine Learning Research! 🦴 This is joint work with Leandro Sánchez-Betancourt, A. Shestopaloff, and Kevin Patrick Murphy. arxiv.org/abs/2411.10153

A Bayesian’s take on filtering without Bayes. Part III: The Kalman filter. In this post, we walk through the derivation of the Kalman filter without priors or posteriors and explore its application to time-series forecasting and online learning. grdm.io/posts/filterin…

The next seminar is this Friday (June 6th) and starts at 12pm midday UK time! Gerardo Duran Martin from Oxford is going to talk about “A unifying framework for generalized Bayesian online learning in non-stationary environment”! ucl.zoom.us/j/99748820264 This seminar is hybrid.

My PhD thesis is online! I explore how a classical framework—Bayesian filtering—can tackle modern challenges in online continual learning, bandits, and one-step-ahead forecasting. Enables adaptivity, robustness, and scalability. arxiv.org/abs/2505.07267 grdm.io/posts/bayesian…

Some thoughts on Gaussian Processes, uncertainty quantification, and whether some methods are inherently "Bayesian". grdm.io/posts/bayes-fr…

Announcing the new "Sensorimotor AI" Journal Club — please share/repost! w/ Kaylene Stocking, Tommaso Salvatori, and Eli Sennesh Details + sign up link in the first reply 👇

Had a great time presenting our BONE paper at the Sensorimotor AI Journal Club! If you’re interested in the foundations of learning, these sessions are definitely worth checking out. Excited for the upcoming RL debate series!