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Rickmer Schulte

@rickmer.bsky.social
72 followers 126 following 8 posts

PhD Student @ LMU Munich Munich Center for Machine Learning (MCML)

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Rickmer Schulte @rickmer.bsky.social · 12/05/2026
By examining what drives these recurring layer-wise ID patterns across neural network architectures, we offer a reinterpretation of this commonly observed phenomenon.
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Rickmer Schulte @rickmer.bsky.social · 12/05/2026
We revisit commonly reported layer-wise ID patterns and identify a notable mismatch between the theory and practice of ID estimation in this setting, showing that that common ID estimators do not track the true underlying ID of neural representations.
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Rickmer Schulte @rickmer.bsky.social · 12/05/2026
In this work, we take a closer look at intrinsic dimension (ID) estimation in neural representations. Motivated by ideas stemming from the manifold hypothesis, ID estimation has become a widely used tool for studying the internal representations of neural networks.
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Rickmer Schulte @rickmer.bsky.social · 12/05/2026
It was great to present and discuss our paper, “Rethinking Intrinsic Dimension Estimation in Neural Representations,” at #AISTATS 2026 last week🚀 📄 Paper: arxiv.org/abs/2604.20276 Joint work with @davidruegamer.bsky.social
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Rickmer Schulte @rickmer.bsky.social · 13/07/2025
Special thanks to my co-authors @davidruegamer.bsky.social and @tnagler.bsky.social—really enjoyed this collaboration!
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Rickmer Schulte @rickmer.bsky.social · 13/07/2025
In this work, we explore how pre-trained neural networks can be leveraged to adjust for confounding in treatment effect estimation involving complex data such as images or text—providing a principled approach to integrate non-tabular data into causal effect estimation.
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Rickmer Schulte @rickmer.bsky.social · 13/07/2025
Looking forward to presenting our paper "Adjustment for Confounding using Pre-Trained Representations" at #ICML2025 in Vancouver next week! 🎉🇨🇦 Feel free to check out our paper and reach out if you're attending or would like to discuss! 📄 Paper: arxiv.org/abs/2506.14329
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Munich Center for Machine Learning @munichcenterml.bsky.social · 11/07/2025
MCML researchers will be represented at #ICML2025 with more than 20 accepted paper 🎉 Check them out: mcml.ai/news/2025-07...
mcml.ai
MCML Researchers With 22 Papers at ICML 2025
We are happy to announce that MCML researchers are represented with 22 papers at ICML 2025.
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Rickmer Schulte @rickmer.bsky.social · 09/05/2025
It has been a great honour to presented our paper "Additive Model Boosting: New Insights and Path(ologie)s" at #AISTATS 2025!🚀 Joint work with @davidruegamer.bsky.social! Check out the paper and feel free to reach out: 🔗 arxiv.org/abs/2503.05538
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David Rügamer @davidruegamer.bsky.social · 01/05/2025
It seems that we have 3 accepted papers at ICML 2025 🔥
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Tim G. J. Rudner @timrudner.bsky.social · 29/04/2025
Congratulations to the #AABI2025 Workshop Track Outstanding Paper Award recipients!
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David Rügamer @davidruegamer.bsky.social · 23/04/2025
Arriving in Singapore this afternoon 🛬 I'll attend #ICLR2025, #AABI2025, and #AISTATS2025 together with many of my students and collaborators to present our 2 orals, 5 posters, and 14 workshop contributions 🚀 Feel free to drop by!
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David Rügamer @davidruegamer.bsky.social · 22/01/2025
🔥🔥 Two papers accepted at #AISTATS2025 💪 - Oral: *Additive Model Boosting: New Insights and Path(ologie)s* by @rickmer.bsky.social - Poster: *Paths and Ambient Spaces in Neural Loss Landscapes* from Daniel Dold, Julius Kobialka, Nicolai Palm, Emanuel Sommer, Oliver Dürr
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