ICML 2022spotlight29 citations
Context-Aware Drift Detection
Oliver Cobb, Arnaud Van Looveren
Abstract
When monitoring machine learning systems, two-sample tests of homogeneity form the foundation upon which existing approaches to drift detection build. They are used to test for evidence that the distribution underlying recent deployment data differs from that underlying the historical reference data. Often, however, various factors such as time-induced correlation mean that batches of recent deployment data are not expected to form an i.i.d. sample from the historical data distribution. Instead we may wish to test for differences in the distributions conditional on
BibTeX
@InProceedings{pmlr-v162-cobb22a,
title = {Context-Aware Drift Detection},
author = {Cobb, Oliver and Van Looveren, Arnaud},
booktitle = {Proceedings of the 39th International Conference on Machine Learning},
pages = {4087--4111},
year = {2022},
editor = {Chaudhuri, Kamalika and Jegelka, Stefanie and Song, Le and Szepesvari, Csaba and Niu, Gang and Sabato, Sivan},
volume = {162},
series = {Proceedings of Machine Learning Research},
month = {17--23 Jul},
publisher = {PMLR},
pdf = {https://proceedings.mlr.press/v162/cobb22a/cobb22a.pdf},
url = {https://proceedings.mlr.press/v162/cobb22a.html},
abstract = {When monitoring machine learning systems, two-sample tests of homogeneity form the foundation upon which existing approaches to drift detection build. They are used to test for evidence that the distribution underlying recent deployment data differs from that underlying the historical reference data. Often, however, various factors such as time-induced correlation mean that batches of recent deployment data are not expected to form an i.i.d. sample from the historical data distribution. Instead we may wish to test for differences in the distributions conditional on