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stat.ML2026
Entropic Projection Alignment: Estimating, Explaining, and Improving Model Performance Under Distribution Shift
Salim I. Amoukou, Emanuele Albini, Tom Bewley +2
We propose a unified framework for addressing three key challenges of distribution shift: (1) estimating a model's performance on an unlabeled target domain, (2) explaining the shi…
stat.ML2026
Correcting Split Selection in Online Decision Trees via Anytime-Valid Inference
Salim I. Amoukou, Saumitra Mishra, Manuela Veloso
Bagging-based ensembles, most notably Adaptive Random Forests, are among the strongest performers for learning from data streams. A common denominator across these methods is their…