#Girls’Day2026: On April 23, 2026, the #MCML, together with Deep Tech Collective and CreAITech , invites girls aged 14–16 to an interactive Girls’ Day at the Hochschule für Philosophie München. 💡 Find out more: www.girls-day.de/.oO/Show/mun...
Fiona K. Ewald
@fionaewald.bsky.social
PhD Student @ LMU Munich Munich Center for Machine Learning (MCML) Research in Interpretable ML / Explainable AI
Great news 🚀 www.linkedin.com/posts/tailor...
#ki #artificialintelligence #münchen #beratung #startup | Tailor-made AI Consulting
Wir haben den Schritt gewagt. 🚀 Wir haben uns dazu entschieden, unsere eigene KI-Beratung zu gründen. Viele Unternehmen sehen das riesige Potenzial von KI – und trotzdem schaffen es die wenigsten, di...
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Great experience presenting my work in progress on #RashomonSets for #interpretability and #performance analysis at MCML Munich AI Day last week! A fantastic chance to connect, learn, and share ideas - big thanks to the @munichcenterml.bsky.social for organizing. #AI #MachineLearning #IML #xAI
Thank you so much @daiichisankyo.bsky.social for welcoming me in your office in Munich!
Munich Center for Machine Learning@munichcenterml.bsky.social · last yr.Great visit to Daiichi Sankyo in Munich! Thanks to Felix Just & Roger Garriga for the warm welcome. We shared exciting work from Simon Schallmoser, Shanshan Bai & Fiona Ewald on treatment effects, synthetic datasets & feature importance. Looking forward to more collabs! #MCML
Feature importance measures can clarify or mislead. PFI, LOCO, and SAGE each answer a different question. Understand how to pick the right tool and avoid spurious conclusions: mcml.ai/news/2025-03... @fionaewald.bsky.social @ludwig-bothmann.bsky.social @giuseppe88.bsky.social @gunnark.bsky.social
mcml.ai
The @munichcenterml.bsky.social recently shared our work: mcml.ai/news/2025-03... 🙏🏻 #mcml #ai #interpretability #iml #xAI #blockpost
Beyond the Black Box: Choosing the Right Feature Importance Method
The team of Bernd Bischl created a clear guide to feature importance methods, helping researchers and practitioners interpret AI models effectively.
mcml.ai
Need an implementation of a conditional sampler, as required, e.g., in Conditional Feature Importance, for a project in R. Since I don't think it's efficient for everyone to implement their own, I'll ask around: Do you know a good implementation?
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Excited to be part of this platform! My research focuses on global model-agnostic feature importance. Please check our recent paper: "A Guide to Feature Importance Methods for Scientific Inference" (link.springer.com/chapter/10.1...).
A Guide to Feature Importance Methods for Scientific Inference
While machine learning (ML) models are increasingly used due to their high predictive power, their use in understanding the data-generating process (DGP) is limited. Understanding the DGP requires ins...
link.springer.com