Sean Kulinski
Sean Kulinski
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Distribution Shift
Towards Explaining Distribution Shifts
We answer the question: ‘‘What is a distribution shift explanation?’’ and introduce a novel framework for explaining distribution shifts via transportation maps between a source and target distribution which are either inherently interpretable or interpreted using post-hoc interpretability methods.
Sean Kulinski
,
David I. Inouye
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Towards Explaining Image-Based Distribution Shifts
Focusing on distributions shifts pertaining to images, we use interpretable transport maps between the latent image spaces of a source and a target distribution to explain how to align the source to the target distribution.
Sean Kulinski
,
David I. Inouye
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Feature Shift Detection: Localizing Which Features Have Shifted via Conditional Distribution Test
We formalize the problem of feature shift, and introduce a method for fast and simultaneous detection of domain shifts and localizing the shift to specific feature(s).
Sean Kulinski
,
Saurabh Bagchi
,
David I. Inouye
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