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Reposted by Motonobu Kanagawa

Philipp Hennig @philipphennig.bsky.social · 26d

Find out more at probnum2026.github.io

The deadline for Submissions will likely be in early March 2026.

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Reposted by Motonobu Kanagawa

Philipp Hennig @philipphennig.bsky.social · 26d

f you're looking for a place to share your ideas on computation and inference, with likeminded, friendly colleagues who give earnest, technically deep feedback, mark your calendars for 9-11 September 2026, because next year, we're going to Finnland, thanks to Toni Karvonen!

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Reposted by Motonobu Kanagawa

Philipp Hennig @philipphennig.bsky.social · 26d

The very first international conference on Probabilistic Numerics is now over. Huge thanks to @motonobu-kanagawa.bsky.social for hosting us at the French Riviera!

It was amazing to see so many contributed works across Europe. Proceedings at proceedings.mlr.press/v271/.

Read on for what's next!

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Motonobu Kanagawa @motonobu-kanagawa.bsky.social · 27d

Our new paper on kNNSampler fixes the widely used kNNImputer for missing value imputation, to recover the distribution of unknown missing values.

Theoretical convergence guarantees in terms of conditional RKHS embeddings.

arxiv.org/abs/2509.08366

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Reposted by Motonobu Kanagawa

arxiv stat.ML @arxiv-stat-ml.bsky.social · 28d

Parastoo Pashmchi, Jerome Benoit, Motonobu Kanagawa
kNNSampler: Stochastic Imputations for Recovering Missing Value Distributions
https://arxiv.org/abs/2509.08366

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Reposted by Motonobu Kanagawa

Sam Power @spmontecarlo.bsky.social · Sep 8

Quite funny:

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Motonobu Kanagawa @motonobu-kanagawa.bsky.social · Sep 7

Accepted papers at ProbNum 2025 have now been published as a volume in Proceedings of Machine Learning Research (PMLR). Thank you to all the authors for their beautiful papers and to the PMLR editor Prof Neil Lawrence @lawrennd.bsky.social for making this possible.

proceedings.mlr.press/v271/

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Motonobu Kanagawa @motonobu-kanagawa.bsky.social · Sep 4

Thank you very much @arthurgretton.bsky.social for your great talk!

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Motonobu Kanagawa @motonobu-kanagawa.bsky.social · Sep 4

ProbNum 2025 Keynote 2 ``Gradient Flows on the Maximum Mean Discrepancy'' by @arthurgretton.bsky.social ( @gatsbyucl.bsky.social and Google DeepMind.

Slides available here: probnum25.github.io/keynotes

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Motonobu Kanagawa @motonobu-kanagawa.bsky.social · Aug 29

Getting very much ready for ProbNum 2025!

probnum25.github.io

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Reposted by Motonobu Kanagawa

Agustinus Kristiadi @agustinus.kristia.de · Jul 4

📣 [Openings] I'm now an Assistant Prof @westernu.ca CS dept. Funded PhD & MSc positions available! Topics: large probabilistic models, decision-making under uncertainty, and apps in AI4Science. More on agustinus.kristia.de/openings/

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Motonobu Kanagawa @motonobu-kanagawa.bsky.social · Jun 24

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

Gaussian Processes and Reproducing Kernels: Connections and Equivalences

This monograph studies the relations between two approaches using positive definite kernels: probabilistic methods using Gaussian processes, and non-probabilistic methods using reproducing kernel Hilb...

arxiv.org

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Reposted by Motonobu Kanagawa

Motonobu Kanagawa @motonobu-kanagawa.bsky.social · Jun 4

Registration for ProbNum 2025, to be held at EURECOM in southern France from 1st to 3rd September, is now open (deadline: 10th August). A tentative schedule and accepted papers are now online.

probnum25.github.io

ProbNum25

Coming

probnum25.github.io

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Motonobu Kanagawa @motonobu-kanagawa.bsky.social · Jun 4

Registration for ProbNum 2025, to be held at EURECOM in southern France from 1st to 3rd September, is now open (deadline: 10th August). A tentative schedule and accepted papers are now online.

probnum25.github.io

ProbNum25

Coming

probnum25.github.io

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Motonobu Kanagawa @motonobu-kanagawa.bsky.social · Jun 2

Beautiful...

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Reposted by Motonobu Kanagawa

antonio vergari ⚔️ short-circuiting @nolovedeeplearning.bsky.social · May 22

Rio is looking forward to seeing all your works on #fast #reliable #tractable #models #circuits #tensor #networks #flows!

please share!

antonio vergari ⚔️ short-circuiting @nolovedeeplearning.bsky.social · Apr 16

the #TPM ⚡Tractable Probabilistic Modeling ⚡Workshop is back at @auai.org #UAI2025!

Submit your works on:

- fast and #reliable inference
- #circuits and #tensor #networks
- normalizing #flows
- scaling #NeSy #AI
...& more!

🕓 deadline: 23/05/25
👉 tractable-probabilistic-modeling.github.io/tpm2025/

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Motonobu Kanagawa @motonobu-kanagawa.bsky.social · May 12

Life goes on...

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Motonobu Kanagawa @motonobu-kanagawa.bsky.social · May 9

Bonjour...

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Reposted by Motonobu Kanagawa

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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Motonobu Kanagawa @motonobu-kanagawa.bsky.social · Apr 24

Why...?

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Motonobu Kanagawa @motonobu-kanagawa.bsky.social · Apr 24

"Donkeys and the Morning Sun" by Monet (2025)

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Motonobu Kanagawa @motonobu-kanagawa.bsky.social · Apr 24

I didn't know that :) What's the countermeasure you invented?

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Motonobu Kanagawa @motonobu-kanagawa.bsky.social · Apr 24

Why does the chain of sadness never end?

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Motonobu Kanagawa @motonobu-kanagawa.bsky.social · Apr 24

Bonjour...

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Reposted by Motonobu Kanagawa

Pietro Michiardi @michiardi.bsky.social · Feb 6

Happy to share that our paper, "Information Theoretic Text-to-Image Alignment," has been accepted at ICLR 2025! 🚀

Kudos to the team: Chao Wang, the PhD student leading this work, Alessandro Finamore, Max Gallo, and Giulio Franzese.

arxiv.org/abs/2405.20759

Information Theoretic Text-to-Image Alignment

Diffusion models for Text-to-Image (T2I) conditional generation have recently achieved tremendous success. Yet, aligning these models with user's intentions still involves a laborious trial-and-error ...

arxiv.org

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