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Christian A. Naesseth @canaesseth.bsky.social · Jul 16

📜 Monitoring Risks in Test-Time Adaptation
(ICML PUT Workshop Oral!)

Time: Fri 18 Jul 10 a.m. PDT
Location: West Meeting Room 220-222
Presenter: @monaschir.bsky.social

arxiv.org/abs/2507.08721

Monitoring Risks in Test-Time Adaptation

Encountering shifted data at test time is a ubiquitous challenge when deploying predictive models. Test-time adaptation (TTA) methods address this issue by continuously adapting a deployed model using...

arxiv.org

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Christian A. Naesseth @canaesseth.bsky.social · Jul 16

📜 Controlled Generation with Equivariant Variational Flow Matching

Time: Wed 16 Jul 11 a.m. PDT — 1:30 p.m. PDT
Location: East Exhibition Hall A-B #E-3309
Presenter: @eijkelboomfloor.bsky.social

arxiv.org/abs/2506.18340

Controlled Generation with Equivariant Variational Flow Matching

We derive a controlled generation objective within the framework of Variational Flow Matching (VFM), which casts flow matching as a variational inference problem. We demonstrate that controlled genera...

arxiv.org

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Christian A. Naesseth @canaesseth.bsky.social · Jul 16

📜 SDE Matching: Scalable and Simulation-Free Training of Latent Stochastic Differential Equations

Time: Thu 17 Jul 11 a.m. PDT — 1:30 p.m. PDT
Location: East Exhibition Hall A-B #E-2412
Presenter: @gbarto.bsky.social

arxiv.org/abs/2502.02472

https://arxiv.org/abs/2502.02472

t.co

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Christian A. Naesseth @canaesseth.bsky.social · Jul 16

At #ICML2025 this week?

Come check out our work on controlled generation, simulation-free latent SDEs, and risk monitoring in test-time adaptation, and chat with the awesome students that made it happen!

#SDE #Diffusion #FlowMatching #TTA #UncertaintyQuantification

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Christian A. Naesseth @canaesseth.bsky.social · Jun 30

Tomorrow, Tuesday (July 1st) from 4pm to 5pm (UK time).

“SDE Matching: Scalable and Simulation-Free Training of Latent Stochastic Differential Equations" (arxiv.org/abs/2502.02472) 🚀

Join via Zoom 🔥

t.co/N1C3UFukxd

https://us05web.zoom.us/j/7780256206?pwd=flsq8weBOvaZgAsr3ThNiHq9d1mXMS.1&omn=89044077993

t.co

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Christian A. Naesseth @canaesseth.bsky.social · Jun 30

🚨🚀
Come hear @gbarto.bsky.social talk about SDE Matching tomorrow!

SDE Matching is a highly efficient and scalable training framework for Latent/Neural SDEs.

You no longer have to discretize or simulate your SDE models when fitting them to data.

#SDE #Diffusion #FlowMatching #ML

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Christian A. Naesseth @canaesseth.bsky.social · May 14

Overleaf down 😅 #Overleaf #NeurIPS

a cartoon dog is sitting at a table with a cup of coffee in front of a fire with the words this is fine .

ALT: a cartoon dog is sitting at a table with a cup of coffee in front of a fire with the words this is fine .

media.tenor.com

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Christian A. Naesseth @canaesseth.bsky.social · May 11

Tack Oskar!

Christian A. Naesseth @canaesseth.bsky.social · May 7

Wow, I am floored! With the UAI results in, my lab with collaborators have achieved the #PerfectGame 🏆

100% acceptance rate across an entire #ML cycle! (5/5 #NeurIPS, #ICLR, 2/2 #AISTATS, 2/2 #ICML, 1/1 #UAI)

10 for 10. 🥳🍾🤩

#Science #AI #ElementalAI

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Reposted by Christian A. Naesseth

AMLab @amlab.bsky.social · May 6

Exciting news: AMLab is happy to have 7 papers accepted at #ICML2025! 🎉

See the thread below for the full list 📝 and meet us in Vancouver to discuss them further! 🇨🇦

🧵1 / 8

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Christian A. Naesseth @canaesseth.bsky.social · May 3

Oh, rip, the camera-ready PDF on Open Review is only "privately revealed". Sorry about that :(

proceedings.mlr.press/v258/chen25f...
proceedings.mlr.press/v258/timans2...

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Christian A. Naesseth @canaesseth.bsky.social · May 3

openreview.net/forum?id=1Yi...

openreview.net/forum?id=29c...

Check out the papers and/or the posters tomorrow (Sunday)!

#Statistics #SMC #ConformalPrediction #Testing #ML #Bayes

Max-Rank: Efficient Multiple Testing for Conformal Prediction

Multiple hypothesis testing (MHT) frequently arises in scientific inquiries, and concurrent testing of multiple hypotheses inflates the risk of Type-I errors or false positives, rendering MHT...

openreview.net

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Christian A. Naesseth @canaesseth.bsky.social · May 3

#AISTATS2025 happening in Phuket, Thailand! I have two papers at the conference:

1. Max-Rank: Efficient Multiple Testing for Conformal Prediction

2. Variational Combinatorial Sequential Monte Carlo for Bayesian Phylogenetics in Hyperbolic Space

Both at poster session 2!

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Christian A. Naesseth @canaesseth.bsky.social · Apr 30

Thanks Pierre! Was great meeting in person as well :)

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Christian A. Naesseth @canaesseth.bsky.social · Apr 30

Very excited that our work (together with my PhD student @gbarto.bsky.social and our collaborator Dmitry Vetrov) was recognized with a Best Paper Award at #AABI2025!

#ML #SDE #Diffusion #GenAI 🤖🧠

Tim G. J. Rudner @timrudner.bsky.social · Apr 29

Congratulations to the #AABI2025 Workshop Track Outstanding Paper Award recipients!

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Christian A. Naesseth @canaesseth.bsky.social · Apr 28

If you missed it and are attending #AABI at NTU today you can find me presenting it again at the afternoon poster session!

approximateinference.org

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Christian A. Naesseth @canaesseth.bsky.social · Apr 27

Paper: arxiv.org/abs/2502.02472
FPI workshop: sites.google.com/view/fpiwork...
DeLTa workshop: delta-workshop.github.io

Joint work with my PhD student
@gbarto.bsky.social and our collaborator Dmitry Vetrov.

SDE Matching: Scalable and Simulation-Free Training of Latent Stochastic Differential Equations

The Latent Stochastic Differential Equation (SDE) is a powerful tool for time series and sequence modeling. However, training Latent SDEs typically relies on adjoint sensitivity methods, which depend ...

arxiv.org

1

Christian A. Naesseth @canaesseth.bsky.social · Apr 27

Come check out SDE Matching at the #ICLR2025 workshops, a new simulation-free framework for training fully general Latent/Neural SDEs (generalisation of diffusion and bridge models).

FPI: Morning poster session
DeLTa: Afternoon poster session

#SDE #Bayes #GenAI #Diffusion #Flow

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Reposted by Christian A. Naesseth

ICLR Conference @iclr-conf.bsky.social · Apr 23

The calm before the storm #ICLR2025 🔥🔥🔥

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Christian A. Naesseth @canaesseth.bsky.social · Apr 21

Attending #ICLR2025 and #AABI2025. Presenting at the conference and workshops:

1. E-Valuating Classifier Two-Sample Tests, Friday, Hall 3 + Hall 2B #437
2. SDE Matching, Sunday-Tuesday, FPI/DeLTa/AABI

openreview.net/forum?id=dwF...
arxiv.org/abs/2502.02472

lmk if you want to chat!

E-Valuating Classifier Two-Sample Tests

We introduce a powerful deep classifier two-sample test for high-dimensional data based on E-values, called E-C2ST. Our test combines ideas from existing work on split likelihood ratio tests and...

openreview.net

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Reposted by Christian A. Naesseth

Sander Dieleman @sedielem.bsky.social · Apr 15

New blog post: let's talk about latents!
sander.ai/2025/04/15/l...

Generative modelling in latent space

Latent representations for generative models.

sander.ai

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Christian A. Naesseth @canaesseth.bsky.social · Apr 13

I'm not sure I followed this comment as I understood your earlier comment about disliking mandatory cites as leaning towards allowing more author discretion? But I understood this comment like an argument for less author discretion?

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Christian A. Naesseth @canaesseth.bsky.social · Apr 13

However, if you cite something that you think is actively bad/wrong I think that it is perfectly fine to argue that point in the related work/discussion section, or perhaps in an extended part of it in the supplementary/appendix.

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Christian A. Naesseth @canaesseth.bsky.social · Apr 13

Ah, I see. Perhaps I then misunderstood your comment about being opinionated about what is worth citing.

As I mentioned, my comment wasn't about this specific case as it is from my understanding quite a bit more complex than what was available on OpenReview.

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Christian A. Naesseth @canaesseth.bsky.social · Apr 13

Just to be extra clear, my comment was not (and is not) a comment about this specific case.

My comment was about whether it is ok in general to not cite relevant work because an author dislikes it and therefore doesn't think it is worth citing.

Of course relevance is to some degree subjective.

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