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Fedor Noskov

2 accepted papers

2024

Efficient Conformal Prediction under Data Heterogeneity

AISTATS 2024poster

Conformal prediction (CP) stands out as a robust framework for uncertainty quantification, which is crucial for ensuring the reliability of predictions. However, common CP methods heavily rely on the data exchangeability, a condition often violated in practice. Existing approaches for tackling non-e…

Cited by 4SourcePDFScholar
2022

Nonparametric Uncertainty Quantification for Single Deterministic Neural Network

NeurIPS 2022accept

This paper proposes a fast and scalable method for uncertainty quantification of machine learning models' predictions. First, we show the principled way to measure the uncertainty of predictions for a classifier based on Nadaraya-Watson's nonparametric estimate of the conditional label distribution.…