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20202026
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stat.ML2026

Audited Conformal Prediction for Classification under Unknown Distribution Shift

Yanfei Zhou, Rizal Fathony, Nam H. Nguyen +1

We consider the problem of uncertainty quantification for a pretrained classification model deployed under unknown distribution shift. We propose Audited Conformal Prediction (ACP)…

stat.ML2025

Robust Conformal Outlier Detection under Contaminated Reference Data

Meshi Bashari, Matteo Sesia, Yaniv Romano

Conformal prediction is a flexible framework for calibrating machine learning predictions, providing distribution-free statistical guarantees. In outlier detection, this calibratio…

stat.ML2024

Conformal Classification with Equalized Coverage for Adaptively Selected Groups

Yanfei Zhou, Matteo Sesia

This paper introduces a conformal inference method to evaluate uncertainty in classification by generating prediction sets with valid coverage conditional on adaptively chosen feat…

stat.ML2024

Conformalized Adaptive Forecasting of Heterogeneous Trajectories

Yanfei Zhou, Lars Lindemann, Matteo Sesia

This paper presents a new conformal method for generating simultaneous forecasting bands guaranteed to cover the entire path of a new random trajectory with sufficiently high proba…

stat.ML2023

Conformal inference is (almost) free for neural networks trained with early stopping

Ziyi Liang, Yanfei Zhou, Matteo Sesia

Early stopping based on hold-out data is a popular regularization technique designed to mitigate overfitting and increase the predictive accuracy of neural networks. Models trained…