4 citations · 6 across the 10 of their papers we have counts for
3 papers · 1 filter
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…
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…
ShapShift: Explaining Model Prediction Shifts with Subgroup Conditional Shapley Values
Tom Bewley, Salim I. Amoukou, Emanuele Albini +2
Changes in input distribution can induce shifts in the average predictions of machine learning models. Such prediction shifts may impact downstream business outcomes (e.g. a bank's…