8 citations · 12 across the 5 of their papers we have counts for
6 papers
Multi-Distribution Robust Conformal Prediction
Yuqi Yang, Ying Jin
In many fairness and distribution robustness problems, one has access to labeled data from multiple source distributions yet the test data may come from an arbitrary member or a mi…
Combining Discrepancy-Confusion Uncertainty and Calibration Diversity for Active Fine-Grained Image Classification
Yinghao Jin, Xi Yang
Active learning (AL) aims to build high-quality labeled datasets by iteratively selecting the most informative samples from an unlabeled pool under limited annotation budgets. Howe…
Ascend HiFloat8 Format for Deep Learning
Yuanyong Luo, Zhongxing Zhang, Richard Wu +16
This preliminary white paper proposes a novel 8-bit floating-point data format HiFloat8 (abbreviated as HiF8) for deep learning. HiF8 features tapered precision. For normal value e…
Conformal Alignment: Knowing When to Trust Foundation Models with Guarantees
Yu Gui, Ying Jin, Zhimei Ren
Before deploying outputs from foundation models in high-stakes tasks, it is imperative to ensure that they align with human values. For instance, in radiology report generation, re…
Confidence on the Focal: Conformal Prediction with Selection-Conditional Coverage
Ying Jin, Zhimei Ren
Conformal prediction builds marginally valid prediction intervals that cover the unknown outcome of a randomly drawn test point with a prescribed probability. However, in practice,…
SPA: A Graph Spectral Alignment Perspective for Domain Adaptation
Zhiqing Xiao, Haobo Wang, Ying Jin +4
Unsupervised domain adaptation (UDA) is a pivotal form in machine learning to extend the in-domain model to the distinctive target domains where the data distributions differ. Most…