7 papers
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…
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…
AdaMuS: Adaptive Multi-view Sparsity Learning for Dimensionally Unbalanced Data
Cai Xu, Changhao Sun, Ziyu Guan +1
Multi-view learning primarily aims to fuse multiple features to describe data comprehensively. Most prior studies implicitly assume that different views share similar dimensions. I…
Differentiable Geometric Indexing for End-to-End Generative Retrieval
Xujing Wang, Yufeng Chen, Boxuan Zhang +7
Generative Retrieval (GR) has emerged as a promising paradigm to unify indexing and search within a single probabilistic framework. However, existing approaches suffer from two int…
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…
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…