Year: 2024
Type: preprint
Abstract: 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.
Source: arXiv (Cornell University)
Authors: Clarkson, Jase, Xu, Wenkai, Cucuringu, Mihai, Swan, Yvik, Reinert, Gesine
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: Application driven Topological Data Analysis, FAIR: Framework for responsible adoption of Artificial Intelligence in the financial seRvices industry, G1562512020, The Alan Turing Institute 21/22 - Additional Funding, CHARMNET - Characterising Models for Networks, G5349420163, EXC 2064: Maschinelles Lernen: Neue Perspektiven für die Wissenschaft, G8556661049
OpenAlex ID: W4400601182