1 citations · 1 across the 7 of their papers we have counts for
9 papers
Fast Conformal Prediction using Conditional Interquantile Intervals
Naixin Guo, Rui Luo, Zhixin Zhou
We introduce Conformal Interquantile Regression (CIR), a conformal regression method that efficiently constructs near-minimal prediction intervals with guaranteed coverage. CIR lev…
Residual Reweighted Conformal Prediction for Graph Neural Networks
Zheng Zhang, Jie Bao, Zhixin Zhou +3
Graph Neural Networks (GNNs) excel at modeling relational data but face significant challenges in high-stakes domains due to unquantified uncertainty. Conformal prediction (CP) off…
Enhancing Adversarial Robustness with Conformal Prediction: A Framework for Guaranteed Model Reliability
Jie Bao, Chuangyin Dang, Rui Luo +2
As deep learning models are increasingly deployed in high-risk applications, robust defenses against adversarial attacks and reliable performance guarantees become paramount. Moreo…
Conditional Conformal Risk Adaptation
Rui Luo, Zhixin Zhou
Uncertainty quantification is becoming increasingly important in image segmentation, especially for high-stakes applications like medical imaging. While conformal risk control gene…
Volume-Sorted Prediction Set: Efficient Conformal Prediction for Multi-Target Regression
Rui Luo, Zhixin Zhou
We introduce Volume-Sorted Prediction Set (VSPS), a novel method for uncertainty quantification in multi-target regression that uses conditional normalizing flows with conformal ca…
Enhancing Trustworthiness of Graph Neural Networks with Rank-Based Conformal Training
Ting Wang, Zhixin Zhou, Rui Luo
Graph Neural Networks (GNNs) has been widely used in a variety of fields because of their great potential in representing graph-structured data. However, lacking of rigorous uncert…