1 citations · 1 across the 6 of their papers we have counts for
6 papers
D2ACE: Multi-Label Batch Selection Guided by Dual Dynamics and Adaptive Correlation Enhancement
Bin Liu, Haoyu Peng, Zhijia Wei +2
Batch selection is crucial for improving both training efficiency and predictive performance in deep multi-label classification (MLC). Existing batch selection methods typically re…
ArGEnT: Arbitrary Geometry-encoded Transformer for Operator Learning
Wenqian Chen, Zhi-Feng Wei, Yucheng Fu +3
Learning solution operators on arbitrary geometries remains a central challenge in scientific machine learning, especially for many-query simulation, physics-informed learning, and…
Selective Conformal Risk Control
Yunpeng Xu, Wenge Guo, Zhi Wei
Reliable uncertainty quantification is essential for deploying machine learning systems in high-stakes domains. Conformal prediction provides distribution-free coverage guarantees…
Efficient Transformer-Inspired Variants of Physics-Informed Deep Operator Networks
Zhi-Feng Wei, Wenqian Chen, Panos Stinis
Operator learning has emerged as a promising tool for accelerating the solution of partial differential equations (PDEs). The Deep Operator Networks (DeepONets) represent a pioneer…
Conformal Risk Control for Ordinal Classification
Yunpeng Xu, Wenge Guo, Zhi Wei
As a natural extension to the standard conformal prediction method, several conformal risk control methods have been recently developed and applied to various learning problems. In…
Two-stage Risk Control with Application to Ranked Retrieval
Yunpeng Xu, Mufang Ying, Wenge Guo +1
Practical machine learning systems often operate in multiple sequential stages, as seen in ranking and recommendation systems, which typically include a retrieval phase followed by…