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stat.ML2026
Measuring Differences between Conditional Distributions using Kernel Embeddings
Peter Moskvichev, Siu Lun Chau, Dino Sejdinovic
Comparing conditional distributions is a fundamental challenge in statistics and machine learning, with applications across a wide range of domains. While proposed methods for meas…
stat.ML2025
All Models Are Miscalibrated, But Some Less So: Comparing Calibration with Conditional Mean Operators
Peter Moskvichev, Dino Sejdinovic
When working in a high-risk setting, having well calibrated probabilistic predictive models is a crucial requirement. However, estimators for calibration error are not always able…