Uncertainty Quantification on Graph Learning: A Survey

23 April 2024

Papers citing "Uncertainty Quantification on Graph Learning: A Survey"

9 / 9 papers shown

Title
Confidence May Cheat: Self-Training on Graph Neural Networks under Distribution Shift Hongrui Liu Binbin Hu Xiao Wang Chuan Shi Zhiqiang Zhang Jun Zhou 90 52 0 27 Jan 2022
Be Confident! Towards Trustworthy Graph Neural Networks via Confidence Calibration Xiao Wang Hongrui Liu Chuan Shi Cheng Yang UQCV 93 113 0 29 Sep 2021
DEUP: Direct Epistemic Uncertainty Prediction Salem Lahlou Moksh Jain Hadi Nekoei V. Butoi Paul Bertin Jarrid Rector-Brooks Maksym Korablyov Yoshua Bengio PER UQLM UQCV UD 195 80 0 16 Feb 2021
Improving model calibration with accuracy versus uncertainty optimization R. Krishnan Omesh Tickoo UQCV 180 140 0 14 Dec 2020
Uncertainty Aware Semi-Supervised Learning on Graph Data Xujiang Zhao Feng Chen Shu Hu Jin-Hee Cho UQCV EDL BDL 102 130 0 24 Oct 2020
From Local Structures to Size Generalization in Graph Neural Networks Gilad Yehudai Ethan Fetaya E. Meirom Gal Chechik Haggai Maron GNN AI4CE 134 123 0 17 Oct 2020
Scalable MCMC for Mixed Membership Stochastic Blockmodels Wenzhe Li Sungjin Ahn Max Welling BDL 29 41 0 16 Oct 2015
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 247 9,042 0 06 Jun 2015
Cross-conformal predictors V. Vovk 110 194 0 03 Aug 2012