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20232025
most citedKoopman operator learning using invertible neural networks

25 citations · 27 across the 7 of their papers we have counts for

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6 papers · 1 filter

cs.LG2025

Conformal Prediction Meets Long-tail Classification

Shuqi Liu, Jianguo Huang, Luke Ong

Conformal Prediction (CP) is a popular method for uncertainty quantification that converts a pretrained model's point prediction into a prediction set, with the set size reflecting…

cs.LG2024

C-Adapter: Adapting Deep Classifiers for Efficient Conformal Prediction Sets

Kangdao Liu, Hao Zeng, Jianguo Huang +3

Conformal prediction, as an emerging uncertainty quantification technique, typically functions as post-hoc processing for the outputs of trained classifiers. To optimize the classi…

cs.LG2024★ 1 cited

Similarity-Navigated Conformal Prediction for Graph Neural Networks

Jianqing Song, Jianguo Huang, Wenyu Jiang +3

Graph Neural Networks have achieved remarkable accuracy in semi-supervised node classification tasks. However, these results lack reliable uncertainty estimates. Conformal predicti…

cs.LG2024★ 1 cited

TorchCP: A Python Library for Conformal Prediction

Jianguo Huang, Jianqing Song, Xuanning Zhou +2

Conformal prediction (CP) is a powerful statistical framework that generates prediction intervals or sets with guaranteed coverage probability. While CP algorithms have evolved bey…

cs.LG2024

Does confidence calibration improve conformal prediction?

Huajun Xi, Jianguo Huang, Kangdao Liu +2

Conformal prediction is an emerging technique for uncertainty quantification that constructs prediction sets guaranteed to contain the true label with a predefined probability. Pre…

cs.LG2023

Conformal Prediction for Deep Classifier via Label Ranking

Jianguo Huang, Huajun Xi, Linjun Zhang +3

Conformal prediction is a statistical framework that generates prediction sets containing ground-truth labels with a desired coverage guarantee. The predicted probabilities produce…