12 papers
On the Pitfalls of Verbalized Confidence Priors for Calibrating Large Reasoning Models
Shuoyuan Wang, Beier Luo, Hao Zeng +5
Large reasoning models (LRMs) often suffer from overconfidence when expressing their uncertainty. Confidence-aware reinforcement learning (RL) offers a promising way to optimize ca…
Anytime Safe PAC Efficient Reasoning
Chengyao Yu, Hao Zeng, Youxin Zhu +3
Large Reasoning Models (LRMs) have demonstrated remarkable performance on complex tasks but suffer from high computational costs and latency. While selective thinking strategies im…
Model-agnostic Selective Labeling with Provable Statistical Guarantees
Huipeng Huang, Wenbo Liao, Huajun Xi +3
Obtaining high-quality labels for large datasets is expensive, requiring massive annotations from human experts. While AI models offer a cost-effective alternative by predicting la…
Provable Training Data Identification for Large Language Models
Zhenlong Liu, Hao Zeng, Weiran Huang +1
Identifying training data of large-scale models is critical for copyright litigation, privacy auditing, and ensuring fair evaluation. However, existing works typically treat this t…
On the Provable Performance Guarantee of Efficient Reasoning Models
Hao Zeng, Jianguo Huang, Bingyi Jing +2
Large reasoning models (LRMs) have achieved remarkable progress in complex problem-solving tasks. Despite this success, LRMs typically suffer from high computational costs during d…
Semi-Supervised Conformal Prediction With Unlabeled Nonconformity Score
Xuanning Zhou, Zihao Shi, Hao Zeng +3
Conformal prediction (CP) is a powerful framework for uncertainty quantification, generating prediction sets with coverage guarantees. Split conformal prediction relies on labeled…
Exploring Imbalanced Annotations for Effective In-Context Learning
Hongfu Gao, Feipeng Zhang, Hao Zeng +3
Large language models (LLMs) have shown impressive performance on downstream tasks through in-context learning (ICL), which heavily relies on the demonstrations selected from annot…
Parametric Scaling Law of Tuning Bias in Conformal Prediction
Hao Zeng, Kangdao Liu, Bingyi Jing +1
Conformal prediction is a popular framework of uncertainty quantification that constructs prediction sets with coverage guarantees. To uphold the exchangeability assumption, many c…
Exploring the Noise Robustness of Online Conformal Prediction
Huajun Xi, Kangdao Liu, Hao Zeng +2
Conformal prediction is an emerging technique for uncertainty quantification that constructs prediction sets guaranteed to contain the true label with a predefined probability. Rec…
Spatial-Aware Conformal Prediction for Trustworthy Hyperspectral Image Classification
Kangdao Liu, Tianhao Sun, Hao Zeng +3
Hyperspectral image (HSI) classification involves assigning unique labels to each pixel to identify various land cover categories. While deep classifiers have achieved high predict…
C-Adapter: Adapting Deep Classifiers for Efficient Conformal Prediction Sets
Kangdao Liu, Hao Zeng, Jianguo Huang +3
Conformal prediction, as an emerging uncertainty quantification technique, typically functions as post-hoc processing for the outputs of trained classifiers. To optimize the classi…
Transfer Learning for Spatial Autoregressive Models with Application to U.S. Presidential Election Prediction
Hao Zeng, Wei Zhong, Xingbai Xu
It is important to incorporate spatial geographic information into U.S. presidential election analysis, especially for swing states. The state-level analysis also faces significant…