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06/20/2023
GraphGLOW: Universal and Generalizable Structure Learning for Graph Neural Networks
Graph structure learning is a well-established problem that aims at opti...
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02/06/2023
Energy-based Out-of-Distribution Detection for Graph Neural Networks
Learning on graphs, where instance nodes are inter-connected, has become...
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01/23/2023
DIFFormer: Scalable (Graph) Transformers Induced by Energy Constrained Diffusion
Real-world data generation often involves complex inter-dependencies amo...
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12/18/2022
Graph Neural Networks are Inherently Good Generalizers: Insights by Bridging GNNs and MLPs
Graph neural networks (GNNs), as the de-facto model class for representa...
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10/24/2022
Geometric Knowledge Distillation: Topology Compression for Graph Neural Networks
We study a new paradigm of knowledge transfer that aims at encoding grap...
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10/24/2022
Towards Out-of-Distribution Sequential Event Prediction: A Causal Treatment
The goal of sequential event prediction is to estimate the next event ba...
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04/25/2022
Trading Hard Negatives and True Negatives: A Debiased Contrastive Collaborative Filtering Approach
Collaborative filtering (CF), as a standard method for recommendation wi...
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02/20/2022
Cross-Task Knowledge Distillation in Multi-Task Recommendation
Multi-task learning (MTL) has been widely used in recommender systems, w...
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10/09/2021
