3 papers
cs.LG2026
Is Spurious Correlation Removal Always Learnable?
Yibo Zhou, Bo Li, Hai-Miao Hu +3
Invariant learning can fail even when the invariant structure is statistically identifiable. We show a conditional computational barrier: under a black-box samplable supervised spa…
cs.DC2026
FeLoG: Scalable and Efficient Distributed Graph Embedding with Feedback Loop Mechanism
Peng Fang, Arijit Khan, Ziqiang Wu +4
Graph embedding maps graph nodes into low-dimensional vectors to support applications such as recommendation, fraud detection, and graph-based retrieval-augmented generation (Graph…
cs.CV2024
Pedestrian Attribute Recognition as Label-balanced Multi-label Learning
Yibo Zhou, Hai-Miao Hu, Yirong Xiang +2
Rooting in the scarcity of most attributes, realistic pedestrian attribute datasets exhibit unduly skewed data distribution, from which two types of model failures are delivered: (…