71 citations · 163 across the 15 of their papers we have counts for
6 papers · 1 filter
Data-IQ: Characterizing subgroups with heterogeneous outcomes in tabular data
Nabeel Seedat, Jonathan Crabbé, Ioana Bica +1
High model performance, on average, can hide that models may systematically underperform on subgroups of the data. We consider the tabular setting, which surfaces the unique issue…
Concept Activation Regions: A Generalized Framework For Concept-Based Explanations
Jonathan Crabbé, Mihaela van der Schaar
Concept-based explanations permit to understand the predictions of a deep neural network (DNN) through the lens of concepts specified by users. Existing methods assume that the exa…
What is Flagged in Uncertainty Quantification? Latent Density Models for Uncertainty Categorization
Hao Sun, Boris van Breugel, Jonathan Crabbe +2
Uncertainty Quantification (UQ) is essential for creating trustworthy machine learning models. Recent years have seen a steep rise in UQ methods that can flag suspicious examples,…
Benchmarking Heterogeneous Treatment Effect Models through the Lens of Interpretability
Jonathan Crabbé, Alicia Curth, Ioana Bica +1
Estimating personalized effects of treatments is a complex, yet pervasive problem. To tackle it, recent developments in the machine learning (ML) literature on heterogeneous treatm…
Label-Free Explainability for Unsupervised Models
Jonathan Crabbé, Mihaela van der Schaar
Unsupervised black-box models are challenging to interpret. Indeed, most existing explainability methods require labels to select which component(s) of the black-box's output to in…
Data-SUITE: Data-centric identification of in-distribution incongruous examples
Nabeel Seedat, Jonathan Crabbé, Mihaela van der Schaar
Systematic quantification of data quality is critical for consistent model performance. Prior works have focused on out-of-distribution data. Instead, we tackle an understudied yet…