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MLOLab

@mlolab.bsky.social

We are doing application and collaboration driven development of interpretable and statistically sound machine learning methods for understanding disease heterogeneity

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Reposted by MLOLab

Stegle Lab @steglelab.bsky.social · 07/11/2025

Are you using any of our factor models, such as MOFA? 🛵 You might’ve found it challenging to tailor them to your specific use cases - not anymore! Introducing MOFA-FLEX: a flexible, modular factor analysis framework designed for customizable modeling across diverse multi-omics data scenarios. 1/n

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MLOLab @mlolab.bsky.social · 07/11/2025

Working with multi-omic or spatial data? Meet MOFA-FLEX — a modular framework for interpretable factor analysis. Build custom models, select sparsity priors, leverage domain knowledge, integrate spatial information and explore results! Here’s a quick tutorial 👇

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MLOLab @mlolab.bsky.social · 20/02/2025

We got accepted at TMLR! What characteristics of the samples consistently alleviate catastrophic forgetting in memory-based online continual learning? @serpep.bsky.social, Ben Werner, and Florian Buettner investigated this question under an uncertainty lens. 📜: openreview.net/forum?id=dcz...

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MLOLab @mlolab.bsky.social · 14/02/2025

We got accepted at #ICLR2025! @serpep.bsky.social and Florian Buettner formalized a novel scenario for *online* federated continual learning and introduced an effective memory-based baseline that combines uncertainty-aware updates with random replay. 📄Read more: openreview.net/forum?id=f65...

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