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Graph Neural Networks (GNNs) with numerical node features and graph structure as inputs have demonstrated superior performance on various supervised learning tasks with graph data.
Stacked generalization
David H Wolpert · 1992
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Bagging predictors
Leo Breiman · 1996
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Iterative Solution Methods
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Reducing variance of committee prediction with resampling techniques
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Stacking bagged and dagged models
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Learning with local and global consistency
Dengyong Zhou, Olivier Bousquet, Thomas Navin Lal, Jason Weston, and Bernhard Schölkopf · 2004
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Ensemble selection from libraries of models
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Super learner
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The bellkor solution to the netflix grand prize, 2009
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Distributed representations of words and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean · 2013
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Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2016
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Inductive representation learning on large graphs
William L Hamilton, Rex Ying, and Jure Leskovec · 2017
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LightGBM: A highly efficient gradient boosting decision tree
Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, and Tie-Yan Liu · 2017
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Rényi differential privacy
Ilya Mironov · 2017
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Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio · 2017
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CatBoost: unbiased boosting with categorical features
Liudmila Prokhorenkova, Gleb Gusev, Aleksandr Vorobev, Anna Veronika Dorogush, and Andrey Gulin · 2018
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Predict then propagate: Graph neural networks meet personalized pagerank
Johannes Klicpera, Aleksandar Bojchevski, and Stephan Günnemann · 2018
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Cluster-gcn: An efficient algorithm for training deep and large graph convolutional networks
Wei-Lin Chiang, Xuanqing Liu, Si Si, Yang Li, Samy Bengio, and Cho-Jui Hsieh · 2019
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Combining label propagation and simple models out-performs graph neural networks
Qian Huang, Horace He, Abhay Singh, Ser-Nam Lim, and Austin R Benson · 2020
Convergent boosted smoothing for modeling graph data with tabular node features
Jiuhai Chen, Jonas Mueller, Vassilis N Ioannidis, Soji Adeshina, Yangkun Wang, Tom Goldstein, and David Wipf · 2021
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Scalable and adaptive graph neural networks with self-label-enhanced training
Chuxiong Sun and Guoshi Wu · 2021
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Graph attention multi-layer perceptron
Wentao Zhang, Ziqi Yin, Zeang Sheng, Wen Ouyang, Xiaosen Li, Yangyu Tao, Zhi Yang, and Bin Cui · 2021
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Bag of tricks for node classification with graph neural networks
Yangkun Wang, Jiarui Jin, Weinan Zhang, Yong Yu, Zheng Zhang, and David Wipf · 2021
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Boost then convolve: Gradient boosting meets graph neural networks
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Autogluon-tabular: Robust and accurate automl for structured data
Nick Erickson, Jonas Mueller, Alexander Shirkov, Hang Zhang, Pedro Larroy, Mu Li, and Alexander Smola · 2020
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L2-gcn: Layer-wise and learned efficient training of graph convolutional networks
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Matthias Blohm, Marc Hanussek, and Maximilien Kintz · 2020
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Fast, accurate, and simple models for tabular data via augmented distillation
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ELECTRA: Pre-training text encoders as discriminators rather than generators
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A unified view on graph neural networks as graph signal denoising
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Sergei Ivanov and Liudmila Prokhorenkova · 2021
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Node feature extraction by self-supervised multi-scale neighborhood prediction
Eli Chien, Wei-Cheng Chang, Cho-Jui Hsieh, Hsiang-Fu Yu, Jiong Zhang, Olgica Milenkovic, and Inderjit S Dhillon · 2021
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Bertgcn: Transductive text classification by combining gcn and bert, 2021
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Benchmarking multimodal automl for tabular data with text fields
Xingjian Shi, Jonas Mueller, Nick Erickson, Mu Li, and Alexander J Smola · 2021
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A neophyte with automl: Evaluating the promises of automatic machine learning tools
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Which machine learning classifiers are best for small datasets? An empirical study
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A unified framework for convolution-based graph neural networks, 2021
Xuran Pan, Shiji Song, and Gao Huang · 2021
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Graph neural networks inspired by classical iterative algorithms
Yongyi Yang, Tang Liu, Yangkun Wang, Jinjing Zhou, Quan Gan, Zhewei Wei, Zheng Zhang, Zengfeng Huang, and David Wipf · 2021
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Interpreting and unifying graph neural networks with an optimization framework
Meiqi Zhu, Xiao Wang, Chuan Shi, Houye Ji, and Peng Cui · 2021
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Junteng Jia and Austin R Benson · 2021
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