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Tabular data is one of the most ubiquitous sources of information worldwide, spanning a wide variety of domains.
The proposed USCF rating system, its development, theory, and applications
Arpad E Elo · 1967
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Multidimensional binary search trees used for associative searching
Jon Louis Bentley · 1975
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Scikit-learn: Machine learning in python
Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, Jake Vanderplas, Alexandre Passos, David Cournapeau, Matthieu Brucher, Matthieu Perrot, and Édouard Duchesnay · 2011
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Example of the Glicko-2 system
Mark E Glickman · 2012
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OpenML: Networked science in machine learning
Joaquin Vanschoren, Jan N Van Rijn, Bernd Bischl, and Luis Torgo · 2014
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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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CatBoost: Unbiased boosting with categorical features
Liudmila Prokhorenkova, Gleb Gusev, Aleksandr Vorobev, Anna Veronika Dorogush, and Andrey Gulin · 2018
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BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
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Unsupervised learning via meta-learning
Kyle Hsu, Sergey Levine, and Chelsea Finn · 2019
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2019
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei · 2020
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TabTransformer: Tabular data modeling using contextual embeddings
Xin Huang, Ashish Khetan, Milan Cvitkovic, and Zohar Karnin · 2020
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Scaling laws for neural language models
Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B. Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei · 2020
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VIME: Extending the success of self-and semi-supervised learning to tabular domain
Jinsung Yoon, Yao Zhang, James Jordon, and Mihaela Van der Schaar · 2020
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Deep reinforcement learning at the edge of the statistical precipice
Rishabh Agarwal, Max Schwarzer, Pablo Samuel Castro, Aaron C Courville, and Marc Bellemare · 2021
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OpenML benchmarking suites
Bernd Bischl, Bernd Bischl, Giuseppe Casalicchio, Matthias Feurer, Pieter Gijsbers, Frank Hutter, Michel Lang, Rafael Gomes Mantovani, Jan van Rijn, and Joaquin Vanschoren · 2021
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby · 2021
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SCARF: Self-supervised contrastive learning using random feature corruption
Dara Bahri, Heinrich Jiang, Yi Tay, and Donald Metzler · 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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Masked autoencoders are scalable vision learners
Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick · 2022
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Training compute-optimal large language models
Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, Elena Buchatskaya, Trevor Cai, Eliza Rutherford, Diego de Las Casas, Lisa Anne Hendricks, Johannes Welbl, Aidan Clark, Tom Hennigan, Eric Noland, Katie Millican, George van den Driessche, Bogdan Damoc, Aurelia Guy, Simon Osindero, Karen Simonyan, Erich Elsen, Jack W Rae, Oriol Vinyals, and Laurent Sifre · 2022
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Fantastically ordered prompts and where to find them: Overcoming few-shot prompt order sensitivity
Yao Lu, Max Bartolo, Alastair Moore, Sebastian Riedel, and Pontus Stenetorp · 2022
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MET: Masked encoding for tabular data
Kushal Majmundar, Sachin Goyal, Praneeth Netrapalli, and Prateek Jain · 2022
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Transformers can do Bayesian inference
Samuel Müller, Noah Hollmann, Sebastian Pineda Arango, Josif Grabocka, and Frank Hutter · 2022
Cited alongside, same era.
Revisiting pretraining objectives for tabular deep learning
Ivan Rubachev, Artem Alekberov, Yury Gorishniy, and Artem Babenko · 2022
Cited alongside, same era.
Interpolation consistency training for semi-supervised learning
Vikas Verma, Kenji Kawaguchi, Alex Lamb, Juho Kannala, Arno Solin, Yoshua Bengio, and David Lopez-Paz · 2022
Cited alongside, same era.
Scaling vision transformers
Xiaohua Zhai, Alexander Kolesnikov, Neil Houlsby, and Lucas Beyer · 2022
The Llama 3 herd of models
Aaron Grattafiori et al · 2024
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Large language models can automatically engineer features for few-shot tabular learning
Sungwon Han, Jinsung Yoon, Sercan Ö Arik, and Tomas Pfister · 2024
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Better by default: Strong pre-tuned MLPs and boosted trees on tabular data
David Holzmüller, Léo Grinsztajn, and Ingo Steinwart · 2024
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The tabular foundation model TabPFN outperforms specialized time series forecasting models based on simple features
Shi Bin Hoo, Samuel Müller, David Salinas, and Frank Hutter · 2024
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CARTE: Pretraining and transfer for tabular learning
Myung Jun Kim, Leo Grinsztajn, and Gael Varoquaux · 2024
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Transfer learning of tabular data by finetuning large language models
Shourav B. Rabbani, Ibna Kowsar, and Manar D. Samad · 2024
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Cited alongside, same era.
Elo uncovered: Robustness and best practices in language model evaluation
Meriem Boubdir, Edward Kim, Beyza Ermis, Sara Hooker, and Marzieh Fadaee · 2023
Cited alongside, same era.
OpenML-CTR23 – A curated tabular regression benchmarking suite
Sebastian Felix Fischer, Matthias Feurer, and Bernd Bischl · 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.
TabPFGen – Tabular data generation with TabPFN
Junwei Ma, Apoorv Dankar, George Stein, Guangwei Yu, and Anthony Caterini · 2023
Cited alongside, same era.
When do neural nets outperform boosted trees on tabular data?
Duncan McElfresh, Sujay Khandagale, Jonathan Valverde, Vishak Prasad C, Ganesh Ramakrishnan, Micah Goldblum, and Colin White · 2023
Cited alongside, same era.
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Quantifying language models’ sensitivity to spurious features in prompt design or: How I learned to start worrying about prompt formatting
Melanie Sclar, Yejin Choi, Yulia Tsvetkov, and Alane Suhr · 2024
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Self-supervised representation learning from random data projectors
Yi Sui, Tongzi Wu, Jesse Cresswell, Ga Wu, George Stein, Xiaoshi Huang, Xiaochen Zhang, and Maksims Volkovs · 2024
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Scaling generative tabular learning for large language models
Yiming Sun, Xumeng Wen, Shun Zheng, Xiaowei Jia, and Jiang Bian · 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 Caterini · 2024
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Why tabular foundation models should be a research priority
Boris van Breugel and Mihaela van der Schaar · 2024
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LaTable: Towards large tabular models
Boris van Breugel, Jonathan Crabbé, Rob Davis, and Mihaela van der Schaar · 2024
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Mind your format: Towards consistent evaluation of in-context learning improvements
Anton Voronov, Lena Wolf, and Max Ryabinin · 2024
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Towards cross-table masked pretraining for web data mining
Chao Ye, Guoshan Lu, Haobo Wang, Liyao Li, Sai Wu, Gang Chen, and Junbo Zhao · 2024
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TabArena: A living benchmark for machine learning on tabular data
Nick Erickson, Lennart Purucker, Andrej Tschalzev, David Holzmüller, Prateek Mutalik Desai, David Salinas, and Frank Hutter · 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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TabPFN-2.5: Advancing the state of the art in tabular foundation models
Léo Grinsztajn, Klemens Flöge, Oscar Key, Felix Birkel, Philipp Jund, Brendan Roof, Benjamin Jäger, Dominik Safaric, Simone Alessi, Adrian Hayler, Mihir Manium, Rosen Yu, Felix Jablonski, Shi Bin Hoo, Anurag Garg, Jake Robertson, Magnus Bühler, Vladyslav Moroshan, Lennart Purucker, Clara Cornu, Lilly Charlotte Wehrhahn, Alessandro Bonetto, Bernhard Schölkopf, Sauraj Gambhir, Noah Hollmann, and Frank Hutter · 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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When and how unlabeled data provably improve in-context learning
Yingcong Li, Xiangyu Chang, Muti Kara, Xiaofeng Liu, Amit Roy-Chowdhury, and Samet Oymak · 2025
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CTSyn: A foundation model for cross tabular data generation
Xiaofeng Lin, Chenheng Xu, Matthew Yang, and Guang Cheng · 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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Scalable in-context learning on tabular data via retrieval-augmented large language models
Xumeng Wen, Shun Zheng, Zhen Xu, Yiming Sun, and Jiang Bian · 2025
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Mixture of in-context prompters for tabular PFNs
Derek Qiang Xu, F Olcay Cirit, Reza Asadi, Yizhou Sun, and Wei Wang · 2025
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