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Tabular data -- structured, heterogeneous, spreadsheet-style data with rows and columns -- is widely used in practice across many domains.
Random forests
Leo Breiman · 2001
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Xgboost: A scalable tree boosting system
Tianqi Chen and Carlos Guestrin · 2016
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Fasttext. zip: Compressing text classification models
Armand Joulin, Edouard Grave, Piotr Bojanowski, Matthijs Douze, Hérve Jégou, and Tomas Mikolov · 2016
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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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Deep contextualized word representations
Matthew E. Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer · 2018
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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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Bernd Bischl, Giuseppe Casalicchio, Matthias Feurer, Frank Hutter, Michel Lang, Rafael G. Mantovani, Jan N. van Rijn, and Joaquin Vanschoren · 2019
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Model cards for model reporting
Margaret Mitchell, Simone Wu, Andrew Zaldivar, Parker Barnes, Lucy Vasserman, Ben Hutchinson, Elena Spitzer, Inioluwa Deborah Raji, and Timnit Gebru · 2019
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Introduction to the special collection on the fragile families challenge
Matthew J Salganik, Ian Lundberg, Alexander T Kindel, and Sara McLanahan · 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, et al · 2020
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How Can We Know What Language Models Know?
Zhengbao Jiang, Frank F. Xu, Jun Araki, and Graham Neubig · 2020
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Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu · 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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Danny Hernandez, Jared Kaplan, Tom Henighan, and Sam McCandlish · 2021
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JUWELS Cluster and Booster: Exascale Pathfinder with Modular Supercomputing Architecture at Juelich Supercomputing Centre
Jülich Supercomputing Centre · 2021
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Mario Michael Krell, Matej Kosec, Sergio P Perez, and Andrew Fitzgibbon · 2021
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Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
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Self-diagnosis and self-debiasing: A proposal for reducing corpus-based bias in nlp
Timo Schick, Sahana Udupa, and Hinrich Schütze · 2021
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Multimodal automl on structured tables with text fields
Xingjian Shi, Jonas Mueller, Nick Erickson, Mu Li, and Alex Smola · 2021
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Tabular data: Deep learning is not all you need
Ravid Shwartz-Ziv and Amitai Armon · 2021
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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
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Flamingo: a visual language model for few-shot learning
Jean-Baptiste Alayrac, Jeff Donahue, Pauline Luc, Antoine Miech, Iain Barr, Yana Hasson, Karel Lenc, Arthur Mensch, Katherine Millican, Malcolm Reynolds, et al · 2022
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Deep neural networks and tabular data: A survey
Vadim Borisov, Tobias Leemann, Kathrin Seßler, Johannes Haug, Martin Pawelczyk, and Gjergji Kasneci · 2022
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Pali: A jointly-scaled multilingual language-image model
Xi Chen, Xiao Wang, Soravit Changpinyo, AJ Piergiovanni, Piotr Padlewski, Daniel Salz, Sebastian Goodman, Adam Grycner, Basil Mustafa, Lucas Beyer, et al · 2022
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Lift: Language-interfaced fine-tuning for non-language machine learning tasks
Tuan Dinh, Yuchen Zeng, Ruisu Zhang, Ziqian Lin, Michael Gira, Shashank Rajput, Jy-yong Sohn, Dimitris Papailiopoulos, and Kangwook Lee · 2022
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Subgroup robustness grows on trees: An empirical baseline investigation
Josh Gardner, Zoran Popovic, and Ludwig Schmidt · 2022
Cited alongside, same era.
What can transformers learn in-context? a case study of simple function classes
Shivam Garg, Dimitris Tsipras, Percy S Liang, and Gregory Valiant · 2022
Cited alongside, same era.
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 · 2023
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In-context pretraining: Language modeling beyond document boundaries
Weijia Shi, Sewon Min, Maria Lomeli, Chunting Zhou, Margaret Li, Xi Victoria Lin, Noah A Smith, Luke Zettlemoyer, Wen-tau Yih, and Mike Lewis · 2023
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Tabular representation, noisy operators, and impacts on table structure understanding tasks in llms
Ananya Singha, José Cambronero, Sumit Gulwani, Vu Le, and Chris Parnin · 2023
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Gemini: a family of highly capable multimodal models
Gemini Team, Rohan Anil, Sebastian Borgeaud, Yonghui Wu, Jean-Baptiste Alayrac, Jiahui Yu, Radu Soricut, Johan Schalkwyk, Andrew M Dai, Anja Hauth, et al · 2023
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Léo Grinsztajn, Edouard Oyallon, and Gaël Varoquaux · 2022
Cited alongside, same era.
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
Cited alongside, same era.
Rethinking the role of demonstrations: What makes in-context learning work?
Sewon Min, Xinxi Lyu, Ari Holtzman, Mikel Artetxe, Mike Lewis, Hannaneh Hajishirzi, and Luke Zettlemoyer · 2022
Cited alongside, same era.
Learning to retrieve prompts for in-context learning
Ohad Rubin, Jonathan Herzig, and Jonathan Berant · 2022
Cited alongside, same era.
Laion-5b: An open large-scale dataset for training next generation image-text models
Christoph Schuhmann, Romain Beaumont, Richard Vencu, Cade Gordon, Ross Wightman, Mehdi Cherti, Theo Coombes, Aarush Katta, Clayton Mullis, Mitchell Wortsman, et al · 2022
Cited alongside, same era.
Self-consistency improves chain of thought reasoning in language models
Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc V Le, Ed H Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou · 2022
Cited alongside, same era.
Two-stage llm fine-tuning with less specialization and more generalization
Yihan Wang, Si Si, Daliang Li, Michal Lukasik, Felix Yu, Cho-Jui Hsieh, Inderjit S Dhillon, and Sanjiv Kumar · 2022
Cited alongside, same era.
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al · 2023
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Llama 2: Open foundation and fine-tuned chat models
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al · 2023
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Unipredict: Large language models are universal tabular predictors
Ruiyu Wang, Zifeng Wang, and Jimeng Sun · 2023
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Towards foundation models for learning on tabular data
Han Zhang, Xumeng Wen, Shun Zheng, Wei Xu, and Jiang Bian · 2023
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Google usm: Scaling automatic speech recognition beyond 100 languages
Yu Zhang, Wei Han, James Qin, Yongqiang Wang, Ankur Bapna, Zhehuai Chen, Nanxin Chen, Bo Li, Vera Axelrod, Gary Wang, et al · 2023
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A decoder-only foundation model for time-series forecasting
Abhimanyu Das, Weihao Kong, Rajat Sen, and Yichen Zhou · 2024
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Benchmarking distribution shift in tabular data with tableshift
Josh Gardner, Zoran Popovic, and Ludwig Schmidt · 2024
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Large language models are zero-shot time series forecasters
Nate Gruver, Marc Finzi, Shikai Qiu, and Andrew G Wilson · 2024
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Deepseek-coder: When the large language model meets programming–the rise of code intelligence
Daya Guo, Qihao Zhu, Dejian Yang, Zhenda Xie, Kai Dong, Wentao Zhang, Guanting Chen, Xiao Bi, Y Wu, YK Li, et al · 2024
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Scaling laws for forgetting when fine-tuning large language models
Damjan Kalajdzievski · 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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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 · 2024
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Introducing meta llama 3: The most capable openly available llm to date
Meta Llama 3 Team · 2024
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Gpt-4 technical report, 2024
OpenAI GPT-4 Team · 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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Opengraph: Towards open graph foundation models
Lianghao Xia, Ben Kao, and Chao Huang · 2024
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When scaling meets llm finetuning: The effect of data, model and finetuning method
Biao Zhang, Zhongtao Liu, Colin Cherry, and Orhan Firat · 2024
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