1 citations · 1 across the 13 of their papers we have counts for
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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…
Adaptive Conformal Inference by Particle Filtering under Hidden Markov Models
Xiaoyi Su, Zhixin Zhou, Rui Luo
Conformal inference is a statistical method used to construct prediction sets for point predictors, providing reliable uncertainty quantification with probability guarantees. This…
Game-Theoretic Defenses for Robust Conformal Prediction Against Adversarial Attacks in Medical Imaging
Rui Luo, Jie Bao, Zhixin Zhou +1
Adversarial attacks pose significant threats to the reliability and safety of deep learning models, especially in critical domains such as medical imaging. This paper introduces a…
Conformalized Interval Arithmetic with Symmetric Calibration
Rui Luo, Zhixin Zhou
Uncertainty quantification is essential in decision-making, especially when joint distributions of random variables are involved. While conformal prediction provides distribution-f…
Trustworthy Classification through Rank-Based Conformal Prediction Sets
Rui Luo, Zhixin Zhou
Machine learning classification tasks often benefit from predicting a set of possible labels with confidence scores to capture uncertainty. However, existing methods struggle with…
Conformal Thresholded Intervals for Efficient Regression
Rui Luo, Zhixin Zhou
This paper introduces Conformal Thresholded Intervals (CTI), a novel conformal regression method that aims to produce the smallest possible prediction set with guaranteed coverage.…