7 papers · 1 filter
Robust Conditional Conformal Prediction via Branched Normalizing Flow
Rui Xu, Xingyuan Chen, Wenxing Huang +4
Conformal prediction (CP) constructs prediction sets with marginal coverage guarantees under the assumption that the calibration and test distributions are identical. However, unde…
Attribution-Guided Continual Learning for Large Language Models
Yazheng Liu, Yuxuan Wan, Rui Xu +3
Large language models (LLMs) often suffer from catastrophic forgetting in continual learning: after learning new tasks sequentially, they perform worse on earlier tasks. Existing m…
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…
iFairy: the First 2-bit Complex LLM with All Parameters in
Feiyu Wang, Guoan Wang, Yihao Zhang +7
Quantization-Aware Training (QAT) integrates quantization into the training loop, enabling LLMs to learn robust low-bit representations, and is widely recognized as one of the most…
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…
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…
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…