Jeremias Knoblauch

If you are looking for a PhD position at the intersection of Bayesian statistics and machine learning, consider getting in touch with me! I'm building a research group on generalised Bayesian methods with applications in ML at UCL over the coming years.

𝗣𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝘃𝗲𝗹𝘆 𝗢𝗿𝗶𝗲𝗻𝘁𝗲𝗱 𝗣𝗼𝘀𝘁𝗲𝗿𝗶𝗼𝗿𝘀 define a new statistical principle that derives model uncertainty from predictive accuracy. This predictively dominates classical approaches and avoids epistemic uncertainty from collapsing. arxiv.org/abs/2510.01915

@iammteah @probnstat Thanks!

@probnstat ++Credits to @LauchLab

Bayesian machine learning is an approach to modeling and inference that treats unknown parameters and predictions as random variables and updates beliefs using Bayes’ rule as new data arrives. Instead of producing single best guesses, it produces full probability distributions that quantify uncertainty. In probability theory, Bayesian ML builds directly on conditional probability, likelihoods, and prior distributions, providing a coherent framework for learning from data. In machine learning, it powers methods such as Bayesian neural networks, Gaussian processes, and probabilistic graphical models, enabling robust prediction, uncertainty estimation, and principled model comparison. In real life, Bayesian ML is used in medicine, finance, robotics, and recommendation systems, where decisions must be made under uncertainty and models must adapt as evidence accumulates. Image: share.google/R4mi0AVfeKLeZb…

I'm on the academic job market! I design and analyze probabilistic machine-learning methods---motivated by real-world scientific constraints, and developed in collaboration with scientists in biology, chemistry, and physics. A few highlights of my research areas are:

I am pleased to share our new NeurIPS paper for online Bayesian inference for neural networks. Instead of focusing on updating the parameter posterior, we work with the predictive posterior (which makes much more sense for non-identifiable models, and gives us more algorithmic freedom for developing faster methods).

The application for a research fellowship at the Flatiron Institute in the Center for Computational Math is now live! This includes positions for ML and stats. The deadline is Dec 1. Links below with more details.

Today is the day of the pre-ICML event at UCL! Come check out the exciting work from academics, industry researchers, postdocs and PhD students from around London: sites.google.com/view/pre-icml-… @stats_UCL @uclcsml

Videos from the recent UCL workshop on ‘Advances in Post-Bayesian methods’ are now available on YouTube! Check these out if you couldn’t make it: tinyurl.com/AdvPostBayes @LauchLab @matialtamiranom

There’s been lots of interest in gradient flows, but I’ve been a bit sceptical as I wasn’t clear on the advantages. This paper shows that MMD gradient flows can integrate a large class of functions exactly without needing to converge to a global minimum: stationarity is enough!

🆕 The ELLIS Unit London (@ucl) will host the first workshop uniting disparate subfields of post-Bayesian methods, including PAC Bayes, generalized Bayes, predictive resampling, Martingale posteriors, and online learning. 📍 London 🏴󠁧󠁢󠁥󠁮󠁧󠁿 📅 May 15-16 🔗 bit.ly/3GgwZ0G

Don't miss the very first workshop on post-Bayesian methods at UCL on May 15-16. Registration is now open!

Looking forward to speaking at the Post-Bayes workshop in May!

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…

@tarantulae Thank you for the kind words!

Excellent talk by @LauchLab on post-Bayesian Machine Learning: jeremiasknoblauch.github.io/talk/post-baye…, kudos to @LauchLab for recording and sharing it.

Why posteriors MUST be modified, and a simple technic for setting them up. Dr. French's Modified Posteriors (1937) @ISBA_events abebooks.com/Dr-Frenchs-Mod…

Congratulations to @UCL / @stats_UCL's @matialtamiranom on being one of the 2024-2025 @Bloomberg #DataScience Ph.D. Fellows! Learn more about Matías’ research focus and our latest cohort of Ph.D. Fellows: bloom.bg/4itDDiN #AI #ML #NLProc #NeurIPS2024

Super happy and grateful to be awarded the Bloomberg fellowship. Huge thanks to my amazing supervisors @LauchLab and @fx_briol , as well as all my collaborators for their support!

@Chau9991 congrats! Looking forward to seeing your group grow :)

🔔Excited to join the College of Computing & Data Science@NTU Singapore 🇸🇬 as Assistant Professor of Statistical ML in May 2025! My research aims to advance uncertainty-awareness, explainability, and preference alignment in ML methods. Recruiting PhD students soon—stay tuned! 🙌

🥳🥳🥳Massive congratulations to my PhD student @matialtamiranom for winning one of the 3 Bloomberg Fellowships for his work on robust & efficient post-Bayesian methods! I cannot think of anyone more deserving!😊 Read more at tinyurl.com/MatiasBloomberg

@sirbayes thanks for the plug, Kevin! :)

The achiles heel of Bayes is that it is only optimal if the model is the true DGP, which it never is. (That, plus all the computational issues). Generalized Bayes to the rescue! (Attaching a nice summary slide from @lauchlab - if you like it, sign up for the talk series :)

📢 Post-Bayesian online seminar series coming!📢 To stay posted, sign up at tinyurl.com/postBayes We'll discuss cutting-edge methods for posteriors that no longer rely on Bayes Theorem. (e.g., PAC-Bayes, generalised Bayes, Martingale posteriors, ...) Pls circulate widely!