1 citations · 1 across the 6 of their papers we have counts for
6 papers
Calibration-Aware Uncertainty Cascades for Efficient Heterogeneous Model Collaboration
Yilin Zhang, Han Jiang, Cai Xu +2
Heterogeneous model collaboration seeks to exploit the complementary strengths of different models to balance predictive performance and inference cost. Existing approaches typical…
Are Independently Estimated View Uncertainties Comparable? Unified Routing for Trusted Multi-View Classification
Yilin Zhang, Cai Xu, Haishun Chen +2
Trusted multi-view classification typically relies on a view-wise evidential fusion process: each view independently produces class evidence and uncertainty, and the final predicti…
Simple Yet Effective Selective Imputation for Incomplete Multi-view Clustering
Cai Xu, Jinlong Liu, Yilin Zhang +3
Incomplete Multi-view Clustering (IMC) has emerged as a significant challenge in multi-view learning. A predominant line for IMC is data imputation; however, indiscriminate imputat…
Target-Agnostic Calibration under Distribution Shift with Frequency-Aware Gradient Rectification
Yilin Zhang, Cai Xu, You Wu +2
Real-world model deployments inevitably encounter distribution shifts, rendering the confidence estimates of deep neural networks highly unreliable, posing severe risks in safety-c…
Fairness-Aware Multi-view Evidential Learning with Adaptive Prior
Haishun Chen, Cai Xu, Jinlong Yu +5
Multi-view evidential learning aims to integrate information from multiple views to improve prediction performance and provide trustworthy uncertainty esitimation. Most previous me…
Trusted Multi-view Learning under Noisy Supervision
Yilin Zhang, Cai Xu, Han Jiang +4
Multi-view learning methods often focus on improving decision accuracy while neglecting the decision uncertainty, which significantly restricts their applications in safety-critica…