32 citations · 39 across the 8 of their papers we have counts for
8 papers
SGFormer: Single-Layer Graph Transformers with Approximation-Free Linear Complexity
Qitian Wu, Kai Yang, Hengrui Zhang +2
Learning representations on large graphs is a long-standing challenge due to the inter-dependence nature. Transformers recently have shown promising performance on small graphs tha…
Transformers from Diffusion: A Unified Framework for Neural Message Passing
Qitian Wu, David Wipf, Junchi Yan
Learning representations for structured data with certain geometries (e.g., observed or unobserved) is a fundamental challenge, wherein message passing neural networks (MPNNs) have…
GeoMix: Towards Geometry-Aware Data Augmentation
Wentao Zhao, Qitian Wu, Chenxiao Yang +1
Mixup has shown considerable success in mitigating the challenges posed by limited labeled data in image classification. By synthesizing samples through the interpolation of featur…
Learning Divergence Fields for Shift-Robust Graph Representations
Qitian Wu, Fan Nie, Chenxiao Yang +1
Real-world data generation often involves certain geometries (e.g., graphs) that induce instance-level interdependence. This characteristic makes the generalization of learning mod…
Graph Out-of-Distribution Generalization via Causal Intervention
Qitian Wu, Fan Nie, Chenxiao Yang +2
Out-of-distribution (OOD) generalization has gained increasing attentions for learning on graphs, as graph neural networks (GNNs) often exhibit performance degradation with distrib…
Rethinking Cross-Domain Sequential Recommendation under Open-World Assumptions
Wujiang Xu, Qitian Wu, Runzhong Wang +5
Cross-Domain Sequential Recommendation (CDSR) methods aim to tackle the data sparsity and cold-start problems present in Single-Domain Sequential Recommendation (SDSR). Existing CD…
Supercharging Graph Transformers with Advective Diffusion
Qitian Wu, Chenxiao Yang, Kaipeng Zeng +1
The capability of generalization is a cornerstone for the success of modern learning systems. For non-Euclidean data, e.g., graphs, that particularly involves topological structure…
How Graph Neural Networks Learn: Lessons from Training Dynamics
Chenxiao Yang, Qitian Wu, David Wipf +2
A long-standing goal in deep learning has been to characterize the learning behavior of black-box models in a more interpretable manner. For graph neural networks (GNNs), considera…