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François-Xavier Briol @fxbriol.bsky.social · Aug 28

Thanks Robin! And of course your very nice review of ABC was very helpful (and can be found in the references for those interested!).

François-Xavier Briol @fxbriol.bsky.social · Aug 28

Thanks for the interest! I'll send you a separate copy without the breaks via email, but I don't think it'll be better because I tend to make various parts of the slide appear/disappear, so removing breaks will make things look very messy.

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François-Xavier Briol @fxbriol.bsky.social · Aug 28

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-....

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François-Xavier Briol @fxbriol.bsky.social · Jul 3

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-icm... @statisticsucl.bsky.social @ucl-csml-ellis.bsky.social

Pre ICML @ London 2025

The goal of this meetup is to bring together students, researchers, and engineers from the greater London area to discuss machine learning research presented at International Conference on Machine Lea...

sites.google.com

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Reposted by François-Xavier Briol

Pierre Alquier @pierrealquier.bsky.social · Jun 15

I'll give a talk at the BayesComp workshop on 'Bayesian Computation and Inference with Misspecified Models' Tuesday. Come and say "hi!" if you are attending BayesComp!

Workshop website and schedule:
postbayes.github.io/BayesMisspec...

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Reposted by François-Xavier Briol

Arthur Gretton @arthurgretton.bsky.social · Jun 5

Composite Goodness-of-fit Tests with Kernels, now out in JMLR!

www.jmlr.org/papers/v26/2...

Test if your distribution comes from ✨any✨ member of a parametric family. Comes in MMD and KSD flavours, and with code.

@oscarkey.bsky.social @fxbriol.bsky.social Tamara Fernandez

Composite Goodness-of-fit Tests with Kernels

www.jmlr.org

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François-Xavier Briol @fxbriol.bsky.social · Jun 5

Looking forward to the pre-ICML event at UCL on the 3rd July: sites.google.com/view/pre-icm....

Registration and the call for talks/posters are now open!

Pre ICML @ London 2025

The goal of this meetup is to bring together students, researchers, and engineers from the greater London area to discuss machine learning research presented at International Conference on Machine Lea...

sites.google.com

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Reposted by François-Xavier Briol

UCL Mathematical & Physical Sciences @uclmaps.bsky.social · May 15

New review funded by @genomicsengland.bsky.social & led by Dr Brieuc Lehmann & PhD student Leandra Brauninger (@statisticsucl.bsky.social) explores how the analytical methods used to process & interpret #genomic #data play a critical role in promoting #health #equity: www.ucl.ac.uk/mathematical...

New review on statistical and machine learning tools to advance equity in genomic research

Review led by Dr Brieuc Lehmann and PhD student Leandra Brauninger (both UCL Statistical Science) explores how the analytical methods used to process and interpret genomic data play a critical role in...

www.ucl.ac.uk

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Reposted by François-Xavier Briol

European Commission @ec.europa.eu · May 8

Choose Science. Choose Europe.

A new Marie Skłodowska-Curie Actions Postdoctoral Fellowships 2025 call is now open.

With a budget of €404.3 million, it will support around 1,650 researchers from Europe and beyond.

Apply by 10 September → europa.eu/!fBTMgF

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François-Xavier Briol @fxbriol.bsky.social · May 8

Well done again to @hudsonchen.bsky.social on his very first ICML paper! 😎

François-Xavier Briol @fxbriol.bsky.social · May 8

Surprisingly, @hudsonchen.bsky.social was able to prove a very fast convergence rate! He showed an interpolation rate whereas it was previously believed that only a much slower noisy regression rate was feasible! 🤯

This improves on our prior work: arxiv.org/abs/2406.16530 and all competing methods.

Conditional Bayesian Quadrature

We propose a novel approach for estimating conditional or parametric expectations in the setting where obtaining samples or evaluating integrands is costly. Through the framework of probabilistic nume...

arxiv.org

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François-Xavier Briol @fxbriol.bsky.social · May 8

Our paper remedies the problem with a very simple algorithm which is a nesting of two kernel quadrature algorithms. This provably reduces the number of samples needed to obtain a given accuracy when the problem isn't too high dimensional and smooth.

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François-Xavier Briol @fxbriol.bsky.social · May 8

Unfortunately, existing methods such as nested Monte Carlo or multilevel Monte Carlo require a huge number of samples at each level of nesting to estimate these accurately! ☹️

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François-Xavier Briol @fxbriol.bsky.social · May 8

Why should you care? Nested expectations are a significant computational challenge in stats/ML: they arise in active learning, Bayesian optimisation, experimental design, but also other fields such as option pricing and health economics.

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François-Xavier Briol @fxbriol.bsky.social · May 8

New ICML 2025 paper: Nested expectations with kernel quadrature.

We propose an algorithm to estimate nested expectations which provides orders of magnitude improvements in low-to-mid dimensional smooth nested expectations using kernel ridge regression/kernel quadrature.

arxiv.org/abs/2502.18284

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François-Xavier Briol @fxbriol.bsky.social · May 6

Huge congrats to William Laplante (williamlaplante.github.io) on his first paper, completed in the first few months of his PhD!

And thanks to all collaborators including
@maltamiranomontero.bsky.social, Andrew Duncan and Jeremias Knoblauch.

William Laplante

williamlaplante.github.io

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François-Xavier Briol @fxbriol.bsky.social · May 6

The paper builds on the robust and scalable Gaussian process (RCGP) algorithm from our ICML 2024 paper (arxiv.org/abs/2311.00463). It shows that it can make use of common computational tricks in spatio-temporal settings, and also uses adaptive hyper parameter optimisation to improve calibration.

Robust and Conjugate Gaussian Process Regression

To enable closed form conditioning, a common assumption in Gaussian process (GP) regression is independent and identically distributed Gaussian observation noise. This strong and simplistic assumption...

arxiv.org

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François-Xavier Briol @fxbriol.bsky.social · May 6

New ICML 2025 paper: Robust Spatio-Temporal GP Regression!

We propose a new GP method with linear-in-time cost that is provably robust to outliers. Unlike competitors, our method is fully conjugate and requires no expensive variational inference!

📄 arxiv.org/abs/2502.02450

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François-Xavier Briol @fxbriol.bsky.social · May 2

If you are at AISTATS this week, check out our paper on cost-aware simulation-based inference!

Ayush Bharti @ayushbharti.bsky.social · May 2

"Cost-aware simulation-based inference" is accepted at AISTATS 2025.

Check out our poster #205 on Sunday May 4th in Hall A-E if you are in Phuket. Finland's rising star @huangdaolang.bsky.social will be there to assist you :D

arxiv.org/abs/2410.07930

@fxbriol.bsky.social @samikaski.bsky.social

Cost-aware simulation-based inference

Simulation-based inference (SBI) is the preferred framework for estimating parameters of intractable models in science and engineering. A significant challenge in this context is the large computation...

arxiv.org

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Reposted by François-Xavier Briol

Ayush Bharti @ayushbharti.bsky.social · May 2

"Cost-aware simulation-based inference" is accepted at AISTATS 2025.

Check out our poster #205 on Sunday May 4th in Hall A-E if you are in Phuket. Finland's rising star @huangdaolang.bsky.social will be there to assist you :D

arxiv.org/abs/2410.07930

@fxbriol.bsky.social @samikaski.bsky.social

Cost-aware simulation-based inference

Simulation-based inference (SBI) is the preferred framework for estimating parameters of intractable models in science and engineering. A significant challenge in this context is the large computation...

arxiv.org

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François-Xavier Briol @fxbriol.bsky.social · Apr 29

Thanks Pierre! I think it could be quite handy for MMD-Bayes and MMD estimators :).

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François-Xavier Briol @fxbriol.bsky.social · Apr 29

Ever used kernel mean embeddings or MMD? Wished you had a closed-form expression so you wouldn't need to estimate these? See our new paper with Alex Gessner, Toni Karvonen and Maren Mahsereci, which comes with an associated Python package. arxiv.org/abs/2504.18830

A Dictionary of Closed-Form Kernel Mean Embeddings

Kernel mean embeddings -- integrals of a kernel with respect to a probability distribution -- are essential in Bayesian quadrature, but also widely used in other computational tools for numerical inte...

arxiv.org

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Reposted by François-Xavier Briol

Serge Belongie @serge.belongie.com · Apr 3

So far, 2,135 people have responded to the poll Søren and I posted a few days ago. Of those, 94.4% replied “Yes” to being interested in officially presenting accepted @neuripsconf.bsky.social papers in Europe. (1/7)

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François-Xavier Briol @fxbriol.bsky.social · Apr 2

We also have excellent line-up of invited speakers which includes @pierrealquier.bsky.social, @eweinstein.bsky.social, Jeremias Knoblauch, David Frazier, Harita Dellaporta, Antonietta Mira, Jeremie Houssineau, Sonia Petrone, Edwin Fong and Aretha Teckentrup.

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François-Xavier Briol @fxbriol.bsky.social · Apr 2

The BayesComp workshop on 'Bayesian Computation and Inference with Misspecified Models' will take place in Singapore on the 16-17th June.

We have an open call for posters/contributed calls, with a deadline on the 1st May. More details on the website:
postbayes.github.io/BayesMisspec...

BayesComp Satellite Workshop on Bayesian Computation and Inference with Misspecified Models

postbayes.github.io

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