Robust Machine Learning

TRIAGE: Characterizing and auditing training data for improved regression
TRIAGE provides systematic data characterization for regression settings; compatible with a variety of regressors.
Nabeel Seedat, Jonathan Crabbé, Zhaozhi Qian, Mihaela van der Schaar
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TRIAGE: Characterizing and auditing training data for improved regression
Data-IQ: Characterizing subgroups with heterogeneous outcomes in tabular data
We introduce Data-IQ, a data-centric framework to identify ambiguous examples.
Nabeel Seedat, Jonathan Crabbé, Ioana Bica, Mihaela van der Schaar
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Data-IQ: Characterizing subgroups with heterogeneous outcomes in tabular data
Benchmarking Heterogeneous Treatment Effect Models through the Lens of Interpretability
We benchmark treatment effect models with interpretability tools.
Jonathan Crabbé, Alicia Curth, Ioana Bica, Mihaela van der Schaar
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Benchmarking Heterogeneous Treatment Effect Models through the Lens of Interpretability
Data-SUITE: Data-centric identification of in-distribution incongruous examples
We introduce Data-SUITE, a data-centric framework to identify incongruous examples.
Nabeel Seedat, Jonathan Crabbé, Mihaela van der Schaar
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Data-SUITE: Data-centric identification of in-distribution incongruous examples
Latent Density Models for Uncertainty Categorization
We introduce DAUX, an interpretability framework to interpret model uncertainty.
Hao Sun, Boris van Breugel, Jonathan Crabbé, Nabeel Seedat, Mihaela van der Schaar
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Latent Density Models for Uncertainty Categorization