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09/05/2023
T-SaS: Toward Shift-aware Dynamic Adaptation for Streaming Data
In many real-world scenarios, distribution shifts exist in the streaming...
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06/13/2023
Skill Disentanglement for Imitation Learning from Suboptimal Demonstrations
Imitation learning has achieved great success in many sequential decisio...
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01/07/2023
Faithful and Consistent Graph Neural Network Explanations with Rationale Alignment
Uncovering rationales behind predictions of graph neural networks (GNNs)...
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12/16/2022
TopoImb: Toward Topology-level Imbalance in Learning from Graphs
Graph serves as a powerful tool for modeling data that has an underlying...
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06/10/2022
Synthetic Over-sampling for Imbalanced Node Classification with Graph Neural Networks
In recent years, graph neural networks (GNNs) have achieved state-of-the...
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05/27/2022
On Consistency in Graph Neural Network Interpretation
Uncovering rationales behind predictions of graph neural networks (GNNs)...
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04/18/2022
A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability
Graph Neural Networks (GNNs) have made rapid developments in the recent ...
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02/23/2022
Exploring Edge Disentanglement for Node Classification
Edges in real-world graphs are typically formed by a variety of factors ...
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05/27/2021
Times Series Forecasting for Urban Building Energy Consumption Based on Graph Convolutional Network
The world is increasingly urbanizing and the building industry accounts ...
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04/29/2021
You Can Still Achieve Fairness Without Sensitive Attributes: Exploring Biases in Non-Sensitive Features
Though machine learning models are achieving great success, ex-tensive s...
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03/16/2021
Semi-Supervised Graph-to-Graph Translation
Graph translation is very promising research direction and has a wide ra...
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03/16/2021