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Tabular data represent one of the most prevalent data formats in applied machine learning, largely because they accommodate a broad spectrum of real-world problems.
Machine learning in manufacturing towards industry 4.0: From ‘for now’ to ‘four-know’
Tingting Chen, Vignesh Sampath, Marvin Carl May, Shuo Shan, Oliver Jonas Jorg, Juan José Aguilar Martín, Florian Stamer, Gualtiero Fantoni, Guido Tosello, and Matteo Calaon · 1903
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Greedy function approximation: A gradient boosting machine
Jerome H. Friedman · 2001
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Auto-sklearn 2.0: Hands-free automl via meta-learning, 2022
Matthias Feurer, Katharina Eggensperger, Stefan Falkner, Marius Lindauer, and Frank Hutter · 2007
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Anomaly detection: A survey
Varun Chandola, Arindam Banerjee, and Vipin Kumar · 2009
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Algorithms for hyper-parameter optimization
James Bergstra, Rémi Bardenet, Yoshua Bengio, and Balázs Kégl · 2011
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Scikit-learn: Machine learning in Python
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay · 2011
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An efficient approach for assessing hyperparameter importance
F. Hutter, H. Hoos, and K. Leyton-Brown · 2014
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Efficient and robust automated machine learning
Matthias Feurer, Aaron Klein, Katharina Eggensperger, Jost Springenberg, Manuel Blum, and Frank Hutter · 2015
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Xgboost: A scalable tree boosting system
Tianqi Chen and Carlos Guestrin · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Mimic-iii, a freely accessible critical care database
Alistair E. W. Johnson, Tom J. Pollard, Lu Shen, Li wei H. Lehman, Mengling Feng, Mohammad Mahdi Ghassemi, Benjamin Moody, Peter Szolovits, Leo Anthony Celi, and Roger G. Mark · 2016
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Hyperparameter optimization machines
Martin Wistuba, Nicolas Schilling, and Lars Schmidt-Thieme · 2016
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Deepfm: a factorization-machine based neural network for ctr prediction
Huifeng Guo, Ruiming Tang, Yunming Ye, Zhenguo Li, and Xiuqiang He · 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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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Ł ukasz Kaiser, and Illia Polosukhin · 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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Optuna: A next-generation hyperparameter optimization framework
Takuya Akiba, Shotaro Sano, Toshihiko Yanase, Takeru Ohta, and Masanori Koyama · 2019
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Mfe: Towards reproducible meta-feature extraction
Edesio Alcobaça, Felipe Siqueira, Adriano Rivolli, Luís P. F. Garcia, Jefferson T. Oliva, and André C. P. L. F. de Carvalho · 2020
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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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Autorank: A python package for automated ranking of classifiers
Steffen Herbold · 2020
Cited alongside, same era.
H2O AutoML: Scalable automatic machine learning
Erin LeDell and Sebastien Poirier · 2020
Cited alongside, same era.
Trust issues: Uncertainty estimation does not enable reliable ood detection on medical tabular data
Dennis Ulmer, Lotta Meijerink, and Giovanni Cinà · 2020
Cited alongside, same era.
TabPFN: A transformer that solves small tabular classification problems in a second
Noah Hollmann, Samuel Müller, Katharina Eggensperger, and Frank Hutter · 2023
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When do neural nets outperform boosted trees on tabular data?
Duncan McElfresh, Sujay Khandagale, Jonathan Valverde, Ganesh Ramakrishnan, Vishak Prasad, Micah Goldblum, and Colin White · 2023
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XTab: Cross-table pretraining for tabular transformers
Bingzhao Zhu, Xingjian Shi, Nick Erickson, Mu Li, George Karypis, and Mahsa Shoaran · 2023
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Excelformer: A neural network surpassing gbdts on tabular data, 2024
Jintai Chen, Jiahuan Yan, Qiyuan Chen, Danny Ziyi Chen, Jian Wu, and Jimeng Sun · 2024
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Amlb: an automl benchmark
Pieter Gijsbers, Marcos L. P. Bueno, Stefan Coors, Erin LeDell, Sébastien Poirier, Janek Thomas, Bernd Bischl, and Joaquin Vanschoren · 2024
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alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Sercan Ö Arik and Tomas Pfister · 2021
Cited alongside, same era.
OpenML benchmarking suites
Bernd Bischl, Giuseppe Casalicchio, Matthias Feurer, Pieter Gijsbers, Frank Hutter, Michel Lang, Rafael Gomes Mantovani, Jan N. van Rijn, and Joaquin Vanschoren · 2021
Cited alongside, same era.
Revisiting deep learning models for tabular data
Yury Gorishniy, Ivan Rubachev, Valentin Khrulkov, and Artem Babenko · 2021
Cited alongside, same era.
Well-tuned simple nets excel on tabular datasets
Arlind Kadra, Marius Lindauer, Frank Hutter, and Josif Grabocka · 2021
Cited alongside, same era.
Tabular data: Deep learning is not all you need
Ravid Shwartz-Ziv and Amitai Armon · 2021
Cited alongside, same era.
Saint: Improved neural networks for tabular data via row attention and contrastive pre-training
Gowthami Somepalli, Micah Goldblum, Avi Schwarzschild, C Bayan Bruss, and Tom Goldstein · 2021
Cited alongside, same era.
Deep learning: A primer for psychologists
Christopher J Urban and Kathleen M Gates · 2021
Cited alongside, same era.
Better by default: Strong pre-tuned MLPs and boosted trees on tabular data
David Holzmüller, Leo Grinsztajn, and Ingo Steinwart · 2024
Closest in time.
Interpretable mesomorphic networks for tabular data
Arlind Kadra, Sebastian Pineda Arango, and Josif Grabocka · 2024
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Carte: pretraining and transfer for tabular learning
Myung Jun Kim, Léo Grinsztajn, and Gaël Varoquaux · 2024
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Gamformer: In-context learning for generalized additive models
Andreas Mueller, Julien Siems, Harsha Nori, David Salinas, Arber Zela, Rich Caruana, and Frank Hutter · 2024
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Tabred: Analyzing pitfalls and filling the gaps in tabular deep learning benchmarks, 2024
Ivan Rubachev, Nikolay Kartashev, Yury Gorishniy, and Artem Babenko · 2024
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Position: Why tabular foundation models should be a research priority
Boris Van Breugel and Mihaela Van Der Schaar · 2024
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Making pre-trained language models great on tabular prediction
Jiahuan Yan, Bo Zheng, Hongxia Xu, Yiheng Zhu, Danny Chen, Jimeng Sun, Jian Wu, and Jintai Chen · 2024
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Tabm: Advancing tabular deep learning with parameter-efficient ensembling
Yury Gorishniy, Akim Kotelnikov, and Artem Babenko · 2025
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Accurate predictions on small data with a tabular foundation model
Noah Hollmann, Samuel Müller, Lennart Purucker, Arjun Krishnakumar, Max Körfer, Shi Bin Hoo, Robin Tibor Schirrmeister, and Frank Hutter · 2025
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Tabicl: A tabular foundation model for in-context learning on large data
Jingang Qu, David Holzmüller, Gaël Varoquaux, and Marine Le Morvan · 2025
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A closer look at deep learning methods on tabular datasets, 2025
Han-Jia Ye, Si-Yang Liu, Hao-Run Cai, Qi-Le Zhou, and De-Chuan Zhan · 2025
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