Interpretable Distribution Shift Detection using Optimal Transport
Machine Learning
2022-08-08 v1 Artificial Intelligence
Abstract
We propose a method to identify and characterize distribution shifts in classification datasets based on optimal transport. It allows the user to identify the extent to which each class is affected by the shift, and retrieves corresponding pairs of samples to provide insights on its nature. We illustrate its use on synthetic and natural shift examples. While the results we present are preliminary, we hope that this inspires future work on interpretable methods for analyzing distribution shifts.
Keywords
Cite
@article{arxiv.2208.02896,
title = {Interpretable Distribution Shift Detection using Optimal Transport},
author = {Neha Hulkund and Nicolo Fusi and Jennifer Wortman Vaughan and David Alvarez-Melis},
journal= {arXiv preprint arXiv:2208.02896},
year = {2022}
}
Comments
Presented at ICML 2022 DataPerf Workshop