RelBench: A Benchmark for Deep Learning on Relational Databases

A method of comparing the areas under receiver operating characteristic curves derived from the same cases

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Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009

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Bpr: Bayesian personalized ranking from implicit feedback

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Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme · 2012

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Glove: Global vectors for word representation

Jeffrey Pennington, Richard Socher, and Christopher D Manning · 2014

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Xgboost: A scalable tree boosting system

Tianqi Chen and Carlos Guestrin · 2016

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Jeff Heaton · 2016

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Inductive representation learning on large graphs

Will Hamilton, Zhitao 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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Imagenet classification with deep convolutional neural networks

Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2017

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Scott M Lundberg and Su-In Lee · 2017

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Bert: Pre-training of deep bidirectional transformers for language understanding

Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018

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Modeling relational data with graph convolutional networks

Michael Schlichtkrull, Thomas N. Kipf, Peter Bloem, Rianne van den Berg, Ivan Titov, and Max Welling · 2018

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Feature engineering for machine learning: principles and techniques for data scientists

Alice Zheng and Amanda Casari · 2018

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Supervised learning on relational databases with graph neural networks

Tudataset: A collection of benchmark datasets for learning with graphs

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Christopher Morris, Nils M Kriege, Franka Bause, Kristian Kersting, Petra Mutzel, and Marion Neumann · 2020

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Revisiting deep learning models for tabular data

Yury Gorishniy, Ivan Rubachev, Valentin Khrulkov, and Artem Babenko · 2021

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Deep Learning with Relational Logic Representations

Gustav Šír · 2021

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Identity-aware graph neural networks

Jiaxuan You, Jonathan M Gomes-Selman, Rex Ying, and Jure Leskovec · 2021

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Kaggle Data Science & Machine Learning Survey, 2022

Kaggle · 2022

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Leakage and the reproducibility crisis in machine-learning-based science

Sayash Kapoor and Arvind Narayanan · 2023