7 citations · 7 across the 8 of their papers we have counts for
8 papers
Cross Paraphrastic Invariance Learning for Hallucination Detection
Shanshan Lin, Dongsheng Hong, Sibo Ju +3
Large language models (LLMs) frequently generate hallucinations, which are unsupported by a source document. To avoid costly LLM-as-evaluator pipelines and the heavy annotation dem…
Federated Conditional Conformal Prediction via Generative Models
Rui Xu, Xingyuan Chen, Wenxing Huang +4
Conformal Prediction (CP) provides distribution-free uncertainty quantification by constructing prediction sets that guarantee coverage of the true labels. This reliability makes C…
Wasserstein-regularized Conformal Prediction under General Distribution Shift
Rui Xu, Chao Chen, Yue Sun +2
Conformal prediction yields a prediction set with guaranteed coverage of the true target under the i.i.d. assumption, which may not hold and lead to a gap between and t…
Out-of-distribution Detection in Medical Image Analysis: A survey
Zesheng Hong, Yubiao Yue, Yubin Chen +10
Computer-aided diagnostics has benefited from the development of deep learning-based computer vision techniques in these years. Traditional supervised deep learning methods assume…
Uncertainty Quantification on Graph Learning: A Survey
Chao Chen, Chenghua Guo, Rui Xu +6
Graphical models have demonstrated their exceptional capabilities across numerous applications. However, their performance, confidence, and trustworthiness are often limited by the…
Incorporating Domain Differential Equations into Graph Convolutional Networks to Lower Generalization Discrepancy
Yue Sun, Chao Chen, Yuesheng Xu +3
Ensuring both accuracy and robustness in time series prediction is critical to many applications, ranging from urban planning to pandemic management. With sufficient training data…
Robust Conformal Prediction under Distribution Shift via Physics-Informed Structural Causal Model
Rui Xu, Yue Sun, Chao Chen +2
Uncertainty is critical to reliable decision-making with machine learning. Conformal prediction (CP) handles uncertainty by predicting a set on a test input, hoping the set to cove…
Implementing Recycling Methods for Linear Systems in Python with an Application to Multiple Objective Optimization
Ainara Garcia, Sihong Xie, Arielle Carr
Sequences of linear systems arise in the predictor-corrector method when computing the Pareto front for multi-objective optimization. Rather than discarding information generated w…