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Tabular datasets are inherently heterogeneous, presenting significant challenges for developing pre-trained foundation models.
Solving multiclass learning problems via error-correcting output codes
Thomas G. Dietterich and Ghulum Bakiri · 1995
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Random forests
Leo Breiman · 2001
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Data mining in finance: advances in relational and hybrid methods
Boris Kovalerchuk and Evgenii Vityaev · 2005
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Statistical comparisons of classifiers over multiple data sets
Janez Demsar · 2006
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Applications of support vector machines in chemistry
Ovidiu Ivanciuc et al · 2007
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Data mining methods for recommender systems
Xavier Amatriain, Alejandro Jaimes, Nuria Oliver, and Josep M Pujol · 2010
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Educational data mining: a review of the state of the art
Cristóbal Romero and Sebastián Ventura · 2010
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Do we need hundreds of classifiers to solve real world classification problems?
Manuel Fernández Delgado, Eva Cernadas, Senén Barro, and Dinani Gomes Amorim · 2014
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Efficient and robust automated machine learning
Matthias Feurer, Aaron Klein, Katharina Eggensperger, Jost Tobias 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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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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Self-normalizing neural networks
Günter Klambauer, Thomas Unterthiner, Andreas Mayr, and Sepp Hochreiter · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Catboost: unbiased boosting with categorical features
Liudmila Ostroumova 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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Analysis of the automl challenge series
Isabelle Guyon, Lisheng Sun-Hosoya, Marc Boullé, Hugo Jair Escalante, Sergio Escalera, Zhengying Liu, Damir Jajetic, Bisakha Ray, Mehreen Saeed, Michèle Sebag, et al · 2019
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Autoint: Automatic feature interaction learning via self-attentive neural networks
Weiping Song, Chence Shi, Zhiping Xiao, Zhijian Duan, Yewen Xu, Ming Zhang, and Jian Tang · 2019
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Gradient boosting neural networks: Grownet
Sarkhan Badirli, Xuanqing Liu, Zhengming Xing, Avradeep Bhowmik, and Sathiya S. Keerthi · 2020
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Tabtransformer: Tabular data modeling using contextual embeddings
Xin Huang, Ashish Khetan, Milan Cvitkovic, and Zohar S. Karnin · 2020
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Early prediction of circulatory failure in the intensive care unit using machine learning
Stephanie L Hyland, Martin Faltys, Matthias Hüser, Xinrui Lyu, Thomas Gumbsch, Cristóbal Esteban, Christian Bock, Max Horn, Michael Moor, Bastian Rieck, et al · 2020
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Meta-learning from tasks with heterogeneous attribute spaces
Tomoharu Iwata and Atsutoshi Kumagai · 2020
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Neural oblivious decision ensembles for deep learning on tabular data
Sergei Popov, Stanislav Morozov, and Artem Babenko · 2020
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Tabnet: Attentive interpretable tabular learning
Sercan Ö. Arik and Tomas Pfister · 2021
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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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Well-tuned simple nets excel on tabular datasets
Arlind Kadra, Marius Lindauer, Frank Hutter, and Josif Grabocka · 2021
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DCN V2: improved deep & cross network and practical lessons for web-scale learning to rank systems
Ruoxi Wang, Rakesh Shivanna, Derek Zhiyuan Cheng, Sagar Jain, Dong Lin, Lichan Hong, and Ed H. Chi · 2021
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Danets: Deep abstract networks for tabular data classification and regression
Jintai Chen, Kuanlun Liao, Yao Wan, Danny Z. Chen, and Jian Wu · 2022
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On embeddings for numerical features in tabular deep learning
Yury Gorishniy, Ivan Rubachev, and Artem Babenko · 2022
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Why do tree-based models still outperform deep learning on typical tabular data?
Léo Grinsztajn, Edouard Oyallon, and Gaël Varoquaux · 2022
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Distribution embedding networks for generalization from a diverse set of classification tasks
Lang Liu, Mahdi Milani Fard, and Sen Zhao · 2022
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DNNR: differential nearest neighbors regression
Youssef Nader, Leon Sixt, and Tim Landgraf · 2022
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Revisiting pretraining objectives for tabular deep learning
Ivan Rubachev, Artem Alekberov, Yury Gorishniy, and Artem Babenko · 2022
Cited alongside, same era.
SAINT: Improved neural networks for tabular data via row attention and contrastive pre-training
Gowthami Somepalli, Avi Schwarzschild, Micah Goldblum, C. Bayan Bruss, and Tom Goldstein · 2022
Cited alongside, same era.
CARTE: pretraining and transfer for tabular learning
Myung Jun Kim, Léo Grinsztajn, and Gaël Varoquaux · 2024
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Towards quantifying the effect of datasets for benchmarking: A look at tabular machine learning
Ravin Kohli, Matthias Feurer, Katharina Eggensperger, Bernd Bischl, and Frank Hutter · 2024
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TALENT: A tabular analytics and learning toolbox
Si-Yang Liu, Hao-Run Cai, Qi-Le Zhou, and Han-Jia Ye · 2024
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Tabpfgen - tabular data generation with tabpfn
Junwei Ma, Apoorv Dankar, George Stein, Guangwei Yu, and Anthony L. Caterini · 2024
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Tabdpt: Scaling tabular foundation models
Junwei Ma, Valentin Thomas, Rasa Hosseinzadeh, Hamidreza Kamkari, Alex Labach, Jesse C. Cresswell, Keyvan Golestan, Guangwei Yu, Maksims Volkovs, and Anthony L. Caterini · 2024
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Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed H. Chi, Quoc V. Le, and Denny Zhou · 2022
Cited alongside, same era.
Tabcaps: A capsule neural network for tabular data classification with bow routing
Jintai Chen, KuanLun Liao, Yanwen Fang, Danny Chen, and Jian Wu · 2023
Cited alongside, same era.
Scaling tabpfn: Sketching and feature selection for tabular prior-data fitted networks
Benjamin Feuer, Chinmay Hegde, and Niv Cohen · 2023
Cited alongside, same era.
Tabllm: few-shot classification of tabular data with large language models
Stefan Hegselmann, Alejandro Buendia, Hunter Lang, Monica Agrawal, Xiaoyi Jiang, and David Sontag · 2023
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
Cited alongside, same era.
Tangos: Regularizing tabular neural networks through gradient orthogonalization and specialization
Alan Jeffares, Tennison Liu, Jonathan Crabbé, Fergus Imrie, and Mihaela van der Schaar · 2023
Cited alongside, same era.
Segment anything
Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloé Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C. Berg, Wan-Yen Lo, Piotr Dollár, and Ross B. Girshick · 2023
Cited alongside, same era.
Later among the works it cites.
A tabpfn-based intrusion detection system for the industrial internet of things
Sergio Ruiz-Villafranca, José Roldán Gómez, Juan Manuel Castelo Gómez, Javier Carrillo Mondéjar, and José Luis Martínez · 2024
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Retrieval & fine-tuning for in-context tabular models
Valentin Thomas, Junwei Ma, Rasa Hosseinzadeh, Keyvan Golestan, Guangwei Yu, Maksims Volkovs, and Anthony L. Caterini · 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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From supervised to generative: A novel paradigm for tabular deep learning with large language models
Xumeng Wen, Han Zhang, Shun Zheng, Wei Xu, and Jiang Bian · 2024
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Switchtab: Switched autoencoders are effective tabular learners
Jing Wu, Suiyao Chen, Qi Zhao, Renat Sergazinov, Chen Li, Shengjie Liu, Chongchao Zhao, Tianpei Xie, Hanqing Guo, Cheng Ji, Daniel Cociorva, and Hakan Brunzell · 2024
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Mixture of in-context prompters for tabular pfns
Derek Xu, Olcay Cirit, Reza Asadi, Yizhou Sun, and Wei Wang · 2024
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Making pre-trained language models great on tabular prediction
Jiahuan Yan, Bo Zheng, Hongxia Xu, Yiheng Zhu, Danny Z. Chen, Jimeng Sun, Jian Wu, and Jintai Chen · 2024
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A closer look at deep learning on tabular data
Han-Jia Ye, Si-Yang Liu, Hao-Run Cai, Qi-Le Zhou, and De-Chuan Zhan · 2024
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Ptarl: Prototype-based tabular representation learning via space calibration
Hangting Ye, Wei Fan, Xiaozhuang Song, Shun Zheng, He Zhao, Dan dan Guo, and Yi Chang · 2024
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A comprehensive survey on pretrained foundation models: A history from bert to chatgpt
Ce Zhou, Qian Li, Chen Li, Jun Yu, Yixin Liu, Guangjing Wang, Kai Zhang, Cheng Ji, Qiben Yan, Lifang He, et al · 2024
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Equitabpfn: A target-permutation equivariant prior fitted networks
Michael Arbel, David Salinas, and Frank Hutter · 2025
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Fine-tuned in-context learning transformers are excellent tabular data classifiers
Felix den Breejen, Sangmin Bae, Stephen Cha, and Se-Young Yun · 2025
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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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Shi Bin Hoo, Samuel Müller, David Salinas, and Frank Hutter · 2025
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Tabpfn unleashed: A scalable and effective solution to tabular classification problems
Si-Yang Liu and Han-Jia Ye · 2025
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Mothernet: Fast training and inference via hyper-network transformers
Andreas C. Mueller, Carlo Curino, and Raghu Ramakrishnan · 2025
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Niklas Muennighoff, Zitong Yang, Weijia Shi, Xiang Lisa Li, Li Fei-Fei, Hannaneh Hajishirzi, Luke Zettlemoyer, Percy Liang, Emmanuel Candès, and Tatsunori Hashimoto · 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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Tabred: A benchmark of tabular machine learning in-the-wild
Ivan Rubachev, Nikolay Kartashev, Yury Gorishniy, and Artem Babenko · 2025
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Revisiting nearest neighbor for tabular data: A deep tabular baseline two decades later
Han-Jia Ye, Huai-Hong Yin, De-Chuan Zhan, and Wei-Lun Chao · 2025
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Rethinking pre-training in tabular data: A neighborhood embedding perspective
Han-Jia Ye, Qi-Le Zhou, Huai-Hong Yin, De-Chuan Zhan, and Wei-Lun Chao · 2025
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