5 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…
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…
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…
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…