Clearly a must read for anyone even remotely interested in numerical integration (be it stochastic and deterministic). Toni has been (hyper)active in studying these methods both theoretically and practically. arxiv.org/abs/2602.16218

The UCL IMSS Annual Lecture will take place on the 27th April with a keynote from @lestermackey.bsky.social. The theme is 'Computational Statistics and Machine Learning' and we'll have talks from Alessandro Barp, Paula Cordero Encinar & Po-Ling Loh. imss2026.github.io @statisticsucl.bsky.social

Very excited to announce the ProbAI Theory of Scaling Laws Workshop (warwick.ac.uk/fac/sci/stat...) at @warwickstats.bsky.social, 22-24 June! (1/4)

Accurate and thorough representation of prior and related work is one of the cornerstones of good research. It is shocking to me that so many published NeurIPS papers, even from top institutions, have fabricated references. I recommend reading the original report: gptzero.me/news/neurips/

Mathematical Colloquium (at King's College London): A duality in the foundations of probability and statistics through history by Vladimir Vovk www.kcl.ac.uk/events/mathe...

How do large language models interpret words relating to probability like “unlikely,” “probably,” or “almost certain"? The below shows what happens when we compare judgements from different models to a benchmark dataset of human judgments (data from: github.com/zonination/p...).

Usual MCMC algorithms are typically guaranteed to work well when used to sample from target distributions for which i) mass is reasonably well-concentrated in the centre of the state space, and ii) the log-density is smooth and of moderate growth. Outside of this setting, things can go poorly.

The recording of my talk on 'Multilevel neural simulation-based inference' at the 'One World Approximate Bayesian Inference' seminar series is now available on YouTube. Link: www.youtube.com/watch?v=hBWd...

Preferential Sampling refers to scenarios where observation locations are confounded by the field of interest which the same observations are used to infer. This recent arxiv (arxiv.org/abs/2511.03158) looked at how harmful ignoring preferential sampling would be - not much, according to the paper.

I’ll be giving a talk on a recently accepted NeurIPS paper at the next OWABI seminar on Thursday. The talk will cover simulation-based inference and how you can enhance accuracy when you have cheap approximate simulators at hand.

"The Principles of Diffusion Models" by Chieh-Hsin Lai, Yang Song, Dongjun Kim, Yuki Mitsufuji, Stefano Ermon. arxiv.org/abs/2510.21890 It might not be the easiest intro to diffusion models, but this monograph is an amazing deep dive into the math behind them and all the nuances

Let me advertise a bit our Online Monte Carlo seminar: This coming Tuesday, we have Giorgos Vasdekis speaking on some very interesting recent work. Moreover, we have confirmed our speaker line-up through until December - very exciting! See sites.google.com/view/monte-c... for further details.

The first talk of the season will be this coming Tuesday (23 September), given by Alexandre Bouchard-Côté from UBC. Alex is a great speaker, so do join if you have the chance! See sites.google.com/view/monte-c... for details, links, and so on.

Returning soon - stay tuned! sites.google.com/view/monte-c...

Join us online for a discussion on “Statistical exploration of the Manifold Hypothesis” and an opportunity to explore the intersection of geometry, statistics and machine learning. 📅 Wed 08 Oct | 🕓 4–6pm UK 🔗 Register + download the paper: rss.org.uk/training-eve...

“Everyone knows” what an autoencoder is… but there's an important complementary picture missing from most introductory material. In short: we emphasize how autoencoders are implemented—but not always what they represent (and some of the implications of that representation).🧵

Gearing up for this workshop next week, with the finalised schedule attached! For those who are unable to attend in person, but are interested in watching the talks, they will be streamed live on MS Teams. Please do get in touch with me if you'd like to stay informed about the stream.

An announcement, which might be of some interest: In the period 2022-2024, myself and a number of other postdocs on the "CoSInES" and "Bayes4Health" EPSRC grants were involved in organising a number of internal tutorial workshops, on topics relevant to researchers in computational statistics.

Very cool!

New paper on arXiv! And I think it's a good'un 😄 Meet the new Lattice Random Walk (LRW) discretisation for SDEs. It’s radically different from traditional methods like Euler-Maruyama (EM) in that each iteration can only move in discrete steps {-δₓ, 0, δₓ}.

Just finished delivering a course on 'Robust and scalable simulation-based inference (SBI)' at Greek Stochastics. This covered an introduction to SBI, open challenges, and some recent contributions from my own group. The slides are now available here: fxbriol.github.io/pdfs/slides-....

📣 Please share: We invite submissions to the 29th International Conference on Artificial Intelligence and Statistics (#AISTATS 2026) and welcome paper submissions at the intersection of AI, machine learning, statistics, and related areas. [1/3]

Liwen Xue, Axel Finke, Adam M. Johansen: Online Rolling Controlled Sequential Monte Carlo https://arxiv.org/abs/2508.00696 https://arxiv.org/pdf/2508.00696 https://arxiv.org/html/2508.00696

Really enjoyed listening to this interview with Mike Giles. Only knew him from his multilevel Monte Carlo work, and it was quite a nice surprise to learn about his contributions to CFD and experiences with industrial collaborations!

we're out here simulating, visualising, thriving

We've written a monograph on Gaussian processes and reproducing kernel methods (with @philipphennig.bsky.social, @sejdino.bsky.social and Bharath Sriperumbudur). arxiv.org/abs/2506.17366

Is #rstats dead? I don’t think so.

Is it just me or does Google Scholar forbid searches via Avanti’s WiFi?

The talks from the Post-Bayes workshop are now available online here - youtube.com/playlist?lis... - do take a look!

In the interim, I wanted to advertise our YouTube channel - youtube.com/@montecarlos... - which contains recordings for the bulk of our talks so far (sites.google.com/view/monte-c..., sites.google.com/view/monte-c...). I encourage you to catch up and enjoy them over the intervening months!

Starting from last October, we (@OnlineMCSeminar on Twitter, sites.google.com/view/monte-c...) have been running an online seminar on all aspects of Monte Carlo methods, with about ~30 talks so far. We are currently paused for the summer, expecting to return in September 2025.

Luke Hardcastle, Samuel Livingstone, Gianluca Baio Diffusion piecewise exponential models for survival extrapolation using Piecewise Deterministic Monte Carlo https://arxiv.org/abs/2505.05932

Demo for the sampler from our recent paper discourse.julialang.org/t/ann-a-non-...

Keen to read this: arxiv.org/abs/2504.13322 'Foundations of locally-balanced Markov processes' - Samuel Livingstone, Giorgos Vasdekis, Giacomo Zanella

Modern science wouldn’t exist without the online research repository known as arXiv. Three decades in, its creator still can’t let it go.

Our next OWABI talk will be given by Meïli Baragatti (Assistant Professor Institut Agro Montpellier) on Approximate Bayesian Computation with Deep Learning and Conformal prediction on Thursday the 27th March 2025. More info will follow.

We have taken our Artificial Intelligence Forecasting System (AIFS) into operations today to run side by side with our physics-based weather forecasts. The #AIFS outperforms traditional models for many measures, at a fraction of energy use. ➡️ www.ecmwf.int/en/about/med... #AI #MachineLearning

📢PSA: #NeurIPS2024 recordings are now publicly available! The workshops always have tons of interesting things on at once, so the FOMO is real😵‍💫 Luckily it's all recorded, so I've been catching up on what I missed. Thread below with some personal highlights🧵

My collaborator @ayushbharti.bsky.social will be presenting our recently accepted AISTATS paper on 'cost-aware simulation-based inference' at the next One World ABI Seminar on the 27th February. Full details of the seminar series: warwick.ac.uk/fac/sci/stat... arXiv paper: arxiv.org/abs/2410.07930

A touch of self-advertisement: I'll be giving a research talk next Tuesday at the Online Monte Carlo Seminar (sites.google.com/view/monte-c...) on one of my favourite topics: the convergence properties of the Random Walk Metropolis algorithm. Please join us!

I'm very excited to share notes on Probabilistic AI that I have been writing with @arkrause.bsky.social 🥳 arxiv.org/pdf/2502.05244 These notes aim to give a graduate-level introduction to probabilistic ML + sequential decision-making. I'm super glad to be able to share them with all of you now!

Jeremias Knoblauch and other colleagues and friends in UCL launched a "Post-Bayes seminar series". Their will be many blocks of talks, including a series on PAC-Bayes (probably starting in September 2025). The first talk (by Jeremias) is next week. Please have a look: postbayes.github.io/seminar/

Really excited about this! We note a connection between diffusion/flow models and neural/latent SDEs. We show how to use this for simulation-free learning of fully flexible SDEs. We refer to this as SDE Matching and show speed improvements of several orders of magnitude. arxiv.org/abs/2502.02472

A week on from Storm Éowyn, we look back at the storm, its impacts and how the forecast developed. Read our blog post 👉 www.metoffice.gov.uk/blog/2025/a-...

Really enjoyed this talk by @mattivihola.bsky.social on some theories behind adaptive MCMC - his presentation is really accessible. The recording can be found at youtu.be/FRoia-lszVM?.... Highly recommend if you care about MCMC!!

I'm nothing special, but I have tutored maths in Oxford for almost twenty years. And, I'm telling you—these AI reasoning models are better maths tutors than me! They know more, they're more patient and they will answer questions at 2 in the morning. Higher Ed is not ready for what's coming