Big career news: I'm leaving academia - and moving to the San Francisco Bay Area to explore something new. I've written a short blog post with a few reflections on the end of this chapter. If you'd like to catch up, now is the time to reach out! avt.im/blog/the-roa...

Happy to share a major milestone: after years of development, we are officially launching Version 1.0 of the GeometricKernels library! To top it off, our accompanying paper has just been published in JMLR (MLOSS)! 🎉 github.com/geometric-ke...

I wrote a long, detailed blog post on the state of AI and machine learning. The post's purpose is to sharpen my thinking and help ensure I work on the right things over the next few years. You might find parts of it interesting. Comments are welcome. avt.im/blog/where-a...

After a delayed flight and missing my first poster, I have officially arrived at NeurIPS! I’ll present another poster tomorrow at CDE 606 from 11-2. I’ll post more on this soon. If you’re interested in meeting up, let’s get in touch!

Today, I gave a talk at the INFORMS Job Market Showcase! If you're interested, here are the slides - link below! presentations.avt.im/2025-10-26-A...

I am hiring a fully-funded #PhD in #ML to work at the University of Edinburgh on 𝐠𝐞𝐨𝐦𝐞𝐭𝐫𝐢𝐜 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠 and 𝐮𝐧𝐜𝐞𝐫𝐭𝐚𝐢𝐧𝐭𝐲 𝐪𝐮𝐚𝐧𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧. Application deadline: 31 Dec '25. Starts May/Sep '26. Details in the reply. Pls RT and share with anyone interested!

Paper update: our recent work on Thompson sampling has a shiny new - and I hope much better - name! This new name does much better job of emphasizing what we actually do. Joint work with Jeff Negrea. Thread below!

At ICML, we're presenting a paper on uncertainty-aware surface reconstruction! Compared to previous approaches, we are able to completely remove the need for recursive linear solves for reconstruction and interpolation, using geometric GP machinery. Check it out!

This week’s virtual seminar on Bayesian Decision-making and Uncertainty is happening now! Noémie Jaquier (KTH Royal Institute of Technology) On Riemannian Latent Variable Models and Pullback Metrics Livestream link: www.youtube.com/watch?v=61Be...

We've got a major update to our preprint on adversarial regret guarantees for Thompson sampling! As before, I think this is one of the most important projects I've worked on due to new algorithmic primitives that it - in principle - unlocks. Thread below on what's new!

We’d like to announce next week’s virtual seminar on Bayesian Decision-making and Uncertainty! Noémie Jaquier (KTH Royal Institute of Technology) On Riemannian Latent Variable Models and Pullback Metrics Sign-up link: gp-seminar-series.github.io

This week’s virtual seminar on Bayesian Decision-making and Uncertainty is happening now! Marvin Pförtner (University of Tübingen) Computation-Aware Kalman Filtering and Smoothing YouTube livestream: www.youtube.com/watch?v=0tG2...

We’d like to announce next week’s virtual seminar on Bayesian Decision-making and Uncertainty! Marvin Pförtner (University of Tübingen) Computation-Aware Kalman Filtering and Smoothing Sign-up link: gp-seminar-series.github.io

This week’s virtual seminar on Bayesian Decision-making and Uncertainty is happening now! Yingzhen Li (Imperial College London) On "Modernising" Sparse Gaussian Processes YouTube livestream: www.youtube.com/watch?v=VbGW...

We’d like to announce next week’s virtual seminar on Bayesian Decision-making and Uncertainty! Yingzhen Li (Imperial College London) On Modernising Sparse Gaussian Processes Sign-up link below!

This week’s virtual seminar on Bayesian Decision-making and Uncertainty is happening now! Sebastian Ament (Meta) Unexpected Improvements to Expected Improvement for Bayesian Optimization YouTube livestream: www.youtube.com/watch?v=B71O...

We’d like to announce next week’s virtual seminar on Bayesian Decision-making and Uncertainty! Sebastian Ament (Meta) Unexpected Improvements to Expected Improvement for Bayesian Optimization Sign-up link: gp-seminar-series.github.io

This week’s virtual seminar on Bayesian Decision-making and Uncertainty is happening now! Paul Jensen (University of Michigan) Exploring phenotypes and genotypes with a robot scientist YouTube livestream: www.youtube.com/watch?v=UJQ2...

We’d like to announce next week’s virtual seminar on Bayesian Decision-making and Uncertainty! Paul Jensen (University of Michigan) Exploring phenotypes and genotypes with a robot scientist Sign-up link below! gp-seminar-series.github.io

PSA: If your GitHub Pro account expires - for instance due to a slow edu discount renewal - your GitHub Pages will go offline ***and might redirect to a SPAM website***! I have no idea technically how. If you land in this situation, move your DNS off GH pages immediately!

Online learning is like magic

This week’s virtual seminar on Bayesian Decision-making and Uncertainty is happening now! Qian Xie (Cornell University) Cost-aware Bayesian Optimization via the Pandora's Box Gittins Index www.youtube.com/watch?v=T2IT... gp-seminar-series.github.io

We’d like to announce next week’s virtual seminar on Bayesian Decision-making and Uncertainty! Qian Xie (Cornell) Cost-aware Bayesian Optimization via the Pandora's Box Gittins Index Sign-up link here: gp-seminar-series.github.io

This week’s virtual seminar on Bayesian Decision-making and Uncertainty is happening now! Frank Hutter (University of Freiburg) Accurate predictions on small data (and time series) with the tabular foundation model TabPFN YouTube: www.youtube.com/watch?v=SOXK... gp-seminar-series.github.io

We’d like to announce next week’s virtual seminar on Bayesian Decision-making and Uncertainty! Frank Hutter · University of Freiburg Accurate predictions on small data (and time series) with the tabular foundation model TabPFN Sign-up link below! gp-seminar-series.github.io

Excited to announce our new work on reasoning and retrieval evaluation in LLMs! 😊 Check it out!

New preprint! This is a hardcore technical paper on Thompson sampling - as a strategy for the so-called online learning game. I think it's one of the most long-term important things I have ever worked on due to what it makes possible. That needs explaining: thread below! arxiv.org/abs/2502.14790

I think every designer should write a love letter to a font at least once in their lifetime. This is mine: A 150-year-old font you have likely never heard of, and one you probably saw earlier today. aresluna.org/the-hardest-...

Excited to share our ICLR 2025 oral "Residual Deep Gaussian Processes on Manifolds"! With @vabor112.bsky.social & @arkrause.bsky.social, we introduce manifold-to-manifold GPs that can be composed together, generalising deep GPs to manifolds. Applications include wind prediction & Bayes opt! 1/n

Good news! GeometricKernels now supports the new 𝑯𝒐𝒅𝒈𝒆-𝒄𝒐𝒎𝒑𝒐𝒔𝒊𝒕𝒊𝒐𝒏𝒂𝒍 𝒌𝒆𝒓𝒏𝒆𝒍𝒔 for flow-type data on graphs (thnx Maosheng Yang). Example notebook: geometric-kernels.github.io/GeometricKer.... For the theory behind, see arxiv.org/abs/2310.19450. Some details below.

I will not be taking any questions at this time

not really related I know but just had a look at the article metrics and here's some decent evidence science twitter has moved fully to bsky

Trying to make sense of o3's recent score (25%) on the FrontierMath dataset.

I have been saying for years: getting into deep learning to chase high-salary AI jobs where you can do what you want is a fool’s errand. There’s a lot of competition, differentiating yourself except by luck or connections is hard, and most companies want direct benefit from the salaries they pay.

I'm looking for an emergency reviewer or two for an AISTATS submission in the area of Bayesian ML theory, which at present has the misfortune of three low-quality reviews. If you're able to help, please DM and let me know! Please link your Google Scholar if I don't already know you personally.

Given the hype, I'd like to see Google's genAI search results become a lot better. I recently rented a car, and searched whether it has wireless CarPlay. Google's genAI results say "yes it does" while manufacturer's website says no. LLMs getting facts right is still a problem.

Check out the workshop "Uncertainty in multivariate, non-Euclidean, and functional spaces: theory and practice" Where: Isaac Newton Institute, Cambridge When: 6-9 May 2025 Application deadline: 12 Jan 2025. Link in reply (contributed talks/posters are welcome) I will be there!

i recently gave a talk about questions like this at a workshop organized by the amazing "learning theory alliance" (let-all.com), and several students reached out to tell me how much my talk resonated with them. link to the slides here: docs.google.com/presentation...

my most controversial take is that the whitespace margin provided by your LaTeX template is often totally reasonable and you don't need to add negative \vspace everywhere by default out of habit

At the NeurIPS optimization workshop. In my opinion, the “most creative poster design” award should go to these folks:

Virginia Aglietti is the last invited speaker at our workshop in Bayesian decision making and uncertainty #NeurIPS2024 join us in East room 8 to hear her talk on FunBO, a new way to discover acquisition functions for Bayesian optimization!

Our next invited speaker is Jacob Gardner talking about how Bayesian optimization needs better deep learning! #NeurIPS2024

We are super excited to have Roman Garnett telling us what he learned while writing his Bayesian optimisation book at our workshop in Bayesian decision making and uncertainty! Come join us in East room 8 or online #NeurIPS2024

Our last contributed talk is "Variational Search Distributions" by Rafael Oliveira! #NeurIPS2024

Joachim Schaeffer is the next contributed speaker at our Bayesian decision making and uncertainty workshop #NeurIPS2024, talking about how to use spatiotemporal Gaussian processes for Lithium-Ion battery system health monitoring!

Patrick O’Hara is now talking about "Distributionally Robust Optimisation with Bayesian Ambiguity Sets" at the Bayesian decision making and uncertainty workshop #NeurIPS2024

Freddie Bickford Smith is now talking about "Rethinking Aleatoric and Epistemic Uncertainty" at our workshop in Bayesian decision making at uncertainty!

Ruda Zhang is the next contributed speaker at our Bayesian decision making at uncertainty workshop! He is presenting work joint with his PhD student Taiwo Adebiyi on Gaussian process Thompson sampling via rootfinding.

@miniapeur.bsky.social is now giving the first contributed talk for our Bayesian decision making and uncertainty workshop #NeurIPS2024! Join us to hear about how to convert graphs into Hodgelet spectral features and do classification using Gaussian processes!

@estherrolf.bsky.social is the next speaker at the #NeurIPS2024 workshop in Bayesian decision making at uncertainty -- come to East Room 8 now or online to hear why we need more uncertainty based modeling in geospatial machine learning!