Gerardo Duran-Martin

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

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…

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.…

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…

Happy to announce that our paper on (generalised) Bayesian online learning in non-stationary environments has been accepted at @TmlrOrg! 🦴 This is joint work with @l_sbetancourt, A. Shestopaloff, and @sirbayes. arxiv.org/abs/2411.10153

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…

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 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…

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 @grrddm with Leandro Sánchez-Betancourt and Alex Shestopaloff.

A little late, but happy to announce that our paper on Rough Transformers ⛰️ has been accepted at @NeurIPSConf! 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)

How much einsum is enough?

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…

Excited to be at #ICML2024 this week! I'll be presenting our outlier-robust Kalman filter, WoLF. To commemorate this, I wrote a blog post on how to make a common use-case of the KF more robust using WolF: the exponential weighted moving average (EWMA). gerdm.github.io/posts/wolf-ewm…