Margaux Zaffran — accepted AI-conference papers

2023

Conformal Prediction with Missing Values

Margaux Zaffran, Aymeric Dieuleveut, Julie Josse, Yaniv Romano

ICML 2023poster

Conformal prediction is a theoretically grounded framework for constructing predictive intervals. We study conformal prediction with missing values in the covariates -- a setting that brings new challenges to uncertainty quantification. We first show that the marginal coverage guarantee of conformal…

Cited by 24 Source ↗PDF ↗code ↗Scholar ↗

2022

Adaptive Conformal Predictions for Time Series

Margaux Zaffran, Olivier Feron, Yannig Goude, Julie Josse, Aymeric Dieuleveut

ICML 2022spotlight

Uncertainty quantification of predictive models is crucial in decision-making problems. Conformal prediction is a general and theoretically sound answer. However, it requires exchangeable data, excluding time series. While recent works tackled this issue, we argue that Adaptive Conformal Inference (…

Cited by 175 Source ↗PDF ↗code ↗Scholar ↗