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title booktitle year volume series month publisher pdf url abstract layout issn id tex_title firstpage lastpage page order cycles bibtex_editor editor bibtex_author author date address container-title genre issued extras
Split Conformal Prediction under Data Contamination
Proceedings of the Thirteenth Symposium on Conformal and Probabilistic Prediction with Applications
2024
230
Proceedings of Machine Learning Research
0
PMLR
Conformal prediction is a non-parametric technique for constructing prediction intervals or sets from arbitrary predictive models under the assumption that the data is exchangeable. It is popular as it comes with theoretical guarantees on the marginal coverage of the prediction sets and the split conformal prediction variant has a very low computational cost compared to model training. We study the robustness of split conformal prediction in a data contamination setting, where we assume a small fraction of the calibration scores are drawn from a different distribution than the bulk. We quantify the impact of the corrupted data on the coverage and efficiency of the constructed sets when evaluated on clean test points, and verify our results with numerical experiments. Moreover, we propose an adjustment in the classification setting which we call Contamination Robust Conformal Prediction, and verify the efficacy of our approach using both synthetic and real datasets.
inproceedings
2640-3498
clarkson24a
Split Conformal Prediction under Data Contamination
5
27
5-27
5
false
Vantini, Simone and Fontana, Matteo and Solari, Aldo and Bostr\"{o}m, Henrik and Carlsson, Lars
given family
Simone
Vantini
given family
Matteo
Fontana
given family
Aldo
Solari
given family
Henrik
Boström
given family
Lars
Carlsson
Clarkson, Jason and Xu, Wenkai and Cucuringu, Mihai and Reinert, Gesine
given family
Jason
Clarkson
given family
Wenkai
Xu
given family
Mihai
Cucuringu
given family
Gesine
Reinert
2024-09-10
Proceedings of the Thirteenth Symposium on Conformal and Probabilistic Prediction with Applications
inproceedings
date-parts
2024
9
10