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Cornelius Schröder

@coschroeder.bsky.social
75 followers 200 following 4 posts
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Cornelius Schröder @coschroeder.bsky.social · 31/03/2026
This is a great opportunity to work at the intersection of ML and Biogeoscience! Based within the outstanding research community of Tübingen. Reach out if you are interested!
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Cornelius Schröder @coschroeder.bsky.social · 01/12/2025
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!
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Reposted by Cornelius Schröder
Machine Learning in Science @mackelab.bsky.social · 21/11/2025
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
arxiv.org
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...
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Cornelius Schröder @coschroeder.bsky.social · 28/10/2025
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
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Reposted by Cornelius Schröder
sbi - Simulation-based inference @sbi-devs.bsky.social · 09/09/2025
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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Dmitry Kobak @hippopedoid.bsky.social · 27/08/2025
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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Machine Learning in Science @mackelab.bsky.social · 23/07/2025
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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ML for Science @ml4science.bsky.social · 22/05/2025
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
The members of the Cluster of Excellence "Machine Learning: New Perspectives for Science" raise their glasses and celebrate securing another funding period.
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Reposted by Cornelius Schröder
sbi - Simulation-based inference @sbi-devs.bsky.social · 12/05/2025
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. 🥁 🎉
A wide shot of approximately 30 individuals standing in a line, posing for a group photograph outdoors. The background shows a clear blue sky, trees, and a distant cityscape or hills.
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Cornelius Schröder @coschroeder.bsky.social · 12/05/2025
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!
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Reposted by Cornelius Schröder
psteinb.bsky.social @psteinb.bsky.social · 11/04/2025
🥳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...
github.com
[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: ...
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