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cs.LG2026
GAMformer: Bridging Tabular Foundation Models and Interpretable Machine Learning
Andreas Mueller, Julien Siems, Harsha Nori +4
While interpretability is crucial for machine learning applications in safety-critical domains and for regulatory compliance, existing tabular foundation models like TabPFN lack tr…
cs.LG2025
MotherNet: Fast Training and Inference via Hyper-Network Transformers
Andreas Müller, Carlo Curino, Raghu Ramakrishnan
Foundation models are transforming machine learning across many modalities, with in-context learning replacing classical model training. Recent work on tabular data hints at a simi…
cs.LG2025
Open Challenges in Time Series Anomaly Detection: An Industry Perspective
Andreas Mueller
Current research in time-series anomaly detection is using definitions that miss critical aspects of how anomaly detection is commonly used in practice. We list several areas that…