Mandoline: Model Evaluation under Distribution Shift

Mayee Chen, Karan Goel, Nimit S Sohoni, Fait Poms, Kayvon Fatahalian, Christopher Re
Proceedings of the 38th International Conference on Machine Learning, PMLR 139:1617-1629, 2021.

Abstract

Machine learning models are often deployed in different settings than they were trained and validated on, posing a challenge to practitioners who wish to predict how well the deployed model will perform on a target distribution. If an unlabeled sample from the target distribution is available, along with a labeled sample from a possibly different source distribution, standard approaches such as importance weighting can be applied to estimate performance on the target. However, importance weighting struggles when the source and target distributions have non-overlapping support or are high-dimensional. Taking inspiration from fields such as epidemiology and polling, we develop Mandoline, a new evaluation framework that mitigates these issues. Our key insight is that practitioners may have prior knowledge about the ways in which the distribution shifts, which we can use to better guide the importance weighting procedure. Specifically, users write simple "slicing functions" {–} noisy, potentially correlated binary functions intended to capture possible axes of distribution shift {–} to compute reweighted performance estimates. We further describe a density ratio estimation framework for the slices and show how its estimation error scales with slice quality and dataset size. Empirical validation on NLP and vision tasks shows that Mandoline can estimate performance on the target distribution up to 3x more accurately compared to standard baselines.

Cite this Paper

BibTeX
@InProceedings{pmlr-v139-chen21i, title = {Mandoline: Model Evaluation under Distribution Shift}, author = {Chen, Mayee and Goel, Karan and Sohoni, Nimit S and Poms, Fait and Fatahalian, Kayvon and Re, Christopher}, booktitle = {Proceedings of the 38th International Conference on Machine Learning}, pages = {1617--1629}, year = {2021}, editor = {Meila, Marina and Zhang, Tong}, volume = {139}, series = {Proceedings of Machine Learning Research}, month = {18--24 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v139/chen21i/chen21i.pdf}, url = {https://proceedings.mlr.press/v139/chen21i.html}, abstract = {Machine learning models are often deployed in different settings than they were trained and validated on, posing a challenge to practitioners who wish to predict how well the deployed model will perform on a target distribution. If an unlabeled sample from the target distribution is available, along with a labeled sample from a possibly different source distribution, standard approaches such as importance weighting can be applied to estimate performance on the target. However, importance weighting struggles when the source and target distributions have non-overlapping support or are high-dimensional. Taking inspiration from fields such as epidemiology and polling, we develop Mandoline, a new evaluation framework that mitigates these issues. Our key insight is that practitioners may have prior knowledge about the ways in which the distribution shifts, which we can use to better guide the importance weighting procedure. Specifically, users write simple "slicing functions" {–} noisy, potentially correlated binary functions intended to capture possible axes of distribution shift {–} to compute reweighted performance estimates. We further describe a density ratio estimation framework for the slices and show how its estimation error scales with slice quality and dataset size. Empirical validation on NLP and vision tasks shows that Mandoline can estimate performance on the target distribution up to 3x more accurately compared to standard baselines.} }
Endnote
%0 Conference Paper %T Mandoline: Model Evaluation under Distribution Shift %A Mayee Chen %A Karan Goel %A Nimit S Sohoni %A Fait Poms %A Kayvon Fatahalian %A Christopher Re %B Proceedings of the 38th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2021 %E Marina Meila %E Tong Zhang %F pmlr-v139-chen21i %I PMLR %P 1617--1629 %U https://proceedings.mlr.press/v139/chen21i.html %V 139 %X Machine learning models are often deployed in different settings than they were trained and validated on, posing a challenge to practitioners who wish to predict how well the deployed model will perform on a target distribution. If an unlabeled sample from the target distribution is available, along with a labeled sample from a possibly different source distribution, standard approaches such as importance weighting can be applied to estimate performance on the target. However, importance weighting struggles when the source and target distributions have non-overlapping support or are high-dimensional. Taking inspiration from fields such as epidemiology and polling, we develop Mandoline, a new evaluation framework that mitigates these issues. Our key insight is that practitioners may have prior knowledge about the ways in which the distribution shifts, which we can use to better guide the importance weighting procedure. Specifically, users write simple "slicing functions" {–} noisy, potentially correlated binary functions intended to capture possible axes of distribution shift {–} to compute reweighted performance estimates. We further describe a density ratio estimation framework for the slices and show how its estimation error scales with slice quality and dataset size. Empirical validation on NLP and vision tasks shows that Mandoline can estimate performance on the target distribution up to 3x more accurately compared to standard baselines.
APA
Chen, M., Goel, K., Sohoni, N.S., Poms, F., Fatahalian, K. & Re, C.. (2021). Mandoline: Model Evaluation under Distribution Shift. Proceedings of the 38th International Conference on Machine Learning, in Proceedings of Machine Learning Research 139:1617-1629 Available from https://proceedings.mlr.press/v139/chen21i.html.

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