7 papers
Inference-Time Conformal Reasoning with Valid Factuality Control for Large Language Models
Ting Wang, Yuanjie Shi, Yan Yan +1
Large language models (LLMs) increasingly perform multi-step reasoning, where intermediate claims form implicit directed acyclic graphs whose node correctness is structurally condi…
When to Plan, When to Polish: Noise Level as a Granularity Axis for Diffusion Language Models
Peihong Li, Yuanjie Shi, Yan Yan
Standard tokenwise diffusion LMs keep training corruption and inference commitment at token granularity throughout denoising. At high noise, this leaves scattered local fragments r…
Conformal Margin Risk Minimization: An Envelope Framework for Robust Learning under Label Noise
Yuanjie Shi, Peihong Li, Zijian Zhang +2
Most methods for learning with noisy labels require privileged knowledge such as noise transition matrices, clean subsets or pretrained feature extractors, resources typically unav…
Cost-Sensitive Conformal Training with Provably Controllable Learning Bounds
Xuesong Jia, Yuanjie Shi, Ziquan Liu +2
Conformal prediction (CP) is a general framework to quantify the predictive uncertainty of machine learning models that uses a set prediction to include the true label with a valid…
Provably Minimum-Length Conformal Prediction Sets for Ordinal Classification
Zijian Zhang, Xinyu Chen, Yuanjie Shi +3
Ordinal classification has been widely applied in many high-stakes applications, e.g., medical imaging and diagnosis, where reliable uncertainty quantification (UQ) is essential fo…
Direct Prediction Set Minimization via Bilevel Conformal Classifier Training
Yuanjie Shi, Hooman Shahrokhi, Xuesong Jia +3
Conformal prediction (CP) is a promising uncertainty quantification framework which works as a wrapper around a black-box classifier to construct prediction sets (i.e., subset of c…