Cornelius Schröder
@coschroeder.bsky.social
📤 63
📥 183
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On the way to
#EurIPS
in Copenhagen. If you want to talk about SBI, ML for science or just have a chat you can find me at our poster. Or just send me a DM!
add a skeleton here at some point
3 days ago
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reposted by
Cornelius Schröder
Machine Learning in Science
13 days ago
Simulation-based inference (SBI) has transformed parameter inference across a wide range of domains. To help practitioners get started and make the most of these methods, we joined forces with researchers from many institutions and wrote a practical guide to SBI. 📄 Paper:
arxiv.org/abs/2508.12939
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Simulation-Based Inference: A Practical Guide
A central challenge in many areas of science and engineering is to identify model parameters that are consistent with prior knowledge and empirical data. Bayesian inference offers a principled framewo...
https://arxiv.org/abs/2508.12939
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velotest: finally on bioRxiv! check out our work on faithfulness of RNA velocity embeddings and apply it to your own data:
github.com/mackelab/vel...
work lead by
@sbischoff.bsky.social
add a skeleton here at some point
about 1 month ago
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reposted by
Cornelius Schröder
sbi - Simulation-based inference
3 months ago
From hackathon to release: sbi v0.25 is here! 🎉 What happens when dozens of SBI researchers and practitioners collaborate for a week? New inference methods, new documentation, lots of new embedding networks, a bridge to pyro and a bridge between flow matching and score-based methods 🤯 1/7 🧵
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reposted by
Cornelius Schröder
Dmitry Kobak
3 months ago
We spent a year writing this review of low-dim embeddings and arguing about things like epistemic roles and best practices :-) 20+ authors are all participants of the Dagstuhl seminar we held last year:
www.dagstuhl.de/24122
. Led by
@alexandr.bsky.social
and Cyril de Bodt.
arxiv.org/abs/2508.15929
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reposted by
Cornelius Schröder
Machine Learning in Science
4 months ago
New preprint: SBI with foundation models! Tired of training or tuning your inference network, or waiting for your simulations to finish? Our method NPE-PF can help: It provides training-free simulation-based inference, achieving competitive performance with orders of magnitude fewer simulations! ⚡️
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reposted by
Cornelius Schröder
ML for Science
7 months ago
We're super happy: Our Cluster of Excellence will continue to receive funding from the German Research Foundation
@dfg.de
! Here’s to 7 more years of exciting research at the intersection of
#machinelearning
and science! Find out more:
uni-tuebingen.de/en/research/...
#ExcellenceStrategy
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reposted by
Cornelius Schröder
sbi - Simulation-based inference
7 months ago
Great news! Our March SBI hackathon in Tübingen was a huge success, with 40+ participants (30 onsite!). Expect significant updates soon: awesome new features & a revamped documentation you'll love! Huge thanks to our amazing SBI community! Release details coming soon. 🥁 🎉
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Happy to see this finally out! My collaboration with
@pirta-palola.bsky.social
started at the ProbAI summer school in Trondheim and is a great example what can happen if you meet smart people with different backgrounds and spend a whole week together discussing science, coding and having fun!
add a skeleton here at some point
7 months ago
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reposted by
Cornelius Schröder
8 months ago
🥳Great news, our JOSS paper "sbi reloaded" has been accepted! 🎉 This community lead by the fine folks of
@sbi-devs.bsky.social
is very welcoming and super fun to work with! I learn with every discussion I have. paper:
joss.theoj.org/papers/10.21...
review:
github.com/openjournals...
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[REVIEW]: sbi reloaded: a toolkit for simulation-based inference workflows · Issue #7754 · openjournals/joss-reviews
Submitting author: @janfb (Jan Boelts) Repository: https://github.com/sbi-dev/sbi Branch with paper.md (empty if default branch): joss-submission-2024 Version: v0.24.0 Editor: @boisgera Reviewers: ...
https://github.com/openjournals/joss-reviews/issues/7754#issuecomment-2789593224
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