Fetching the paper…
Reading the bibliography…
Tabular data is one of the most commonly used types of data in machine learning.
The use of ranks to avoid the assumption of normality implicit in the analysis of variance
Milton Friedman · 1937
Earlier work this paper cites.
The regression analysis of binary sequences
David R Cox · 1958
Earlier work this paper cites.
Nearest neighbor pattern classification
Thomas Cover and Peter Hart · 1967
Earlier work this paper cites.
A simple sequentially rejective multiple test procedure
Sture Holm · 1979
Earlier work this paper cites.
Induction of decision trees
J. Ross Quinlan · 1986
Earlier work this paper cites.
Support-vector networks
Corinna Cortes and Vladimir Vapnik · 1995
Earlier work this paper cites.
Practical nonparametric statistics
William Jay Conover · 1999
Earlier work this paper cites.
Ast: Support for algorithm selection with a cbr approach
Guido Lindner and Rudi Studer · 1999
Earlier work this paper cites.
Greedy function approximation: a gradient boosting machine
Jerome H Friedman · 2001
Earlier work this paper cites.
Classification and regression by randomforest
Andy Liaw, Matthew Wiener, et al · 2002
Earlier work this paper cites.
Predicting clicks: estimating the click-through rate for new ads
Matthew Richardson, Ewa Dominowska, and Robert Ragno · 2007
Earlier work this paper cites.
Anomaly detection: A survey
Varun Chandola, Arindam Banerjee, and Vipin Kumar · 2009
Earlier work this paper cites.
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, et al · 2011
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
Earlier work this paper cites.
Ad click prediction: a view from the trenches
H Brendan McMahan, Gary Holt, David Sculley, Michael Young, Dietmar Ebner, Julian Grady, Lan Nie, Todd Phillips, Eugene Davydov, Daniel Golovin, et al · 2013
Earlier work this paper cites.
Employment of neural network and rough set in meta-learning
Mostafa A Salama, Aboul Ella Hassanien, and Kenneth Revett · 2013
Earlier work this paper cites.
Pattern Recognition in Practice IV: Multiple Paradigms, Comparative Studies and Hybrid Systems
E.S. Gelsema and L.N. Kanal · 2014
Earlier work this paper cites.
Openml: networked science in machine learning
Joaquin Vanschoren, Jan N Van Rijn, Bernd Bischl, and Luis Torgo · 2014
Earlier work this paper cites.
A survey of data mining and machine learning methods for cyber security intrusion detection
Anna L Buczak and Erhan Guven · 2015
Earlier work this paper cites.
Learning hyperparameter optimization initializations
Martin Wistuba, Nicolas Schilling, and Lars Schmidt-Thieme · 2015
Earlier work this paper cites.
Loan approval prediction based on machine learning approach
Kumar Arun, Garg Ishan, and Kaur Sanmeet · 2016
Earlier work this paper cites.
Xgboost: A scalable tree boosting system
Tianqi Chen and Carlos Guestrin · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
Mimic-iii, a freely accessible critical care database
Alistair EW Johnson, Tom J Pollard, Lu Shen, Li-wei H Lehman, Mengling Feng, Mohammad Ghassemi, Benjamin Moody, Peter Szolovits, Leo Anthony Celi, and Roger G Mark · 2016
Earlier work this paper cites.
Bernd Bischl, Giuseppe Casalicchio, Matthias Feurer, Frank Hutter, Michel Lang, Rafael G Mantovani, Jan N van Rijn, and Joaquin Vanschoren · 2017
Cited alongside, same era.
Deepfm: a factorization-machine based neural network for ctr prediction
Huifeng Guo, Ruiming Tang, Yunming Ye, Zhenguo Li, and Xiuqiang He · 2017
Cited alongside, same era.
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
Cited alongside, same era.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
Cited alongside, same era.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
Cited alongside, same era.
Revisiting deep learning models for tabular data
Yury Gorishniy, Ivan Rubachev, Valentin Khrulkov, and Artem Babenko · 2021
Later among the works it cites.
Well-tuned simple nets excel on tabular datasets
Arlind Kadra, Marius Lindauer, Frank Hutter, and Josif Grabocka · 2021
Later among the works it cites.
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
Later among the works it cites.
Deep learning: A primer for psychologists
Christopher J Urban and Kathleen M Gates · 2021
Later among the works it cites.
Danets: Deep abstract networks for tabular data classification and regression
Jintai Chen, Kuanlun Liao, Yao Wan, Danny Z Chen, and Jian Wu · 2022
Later among the works it cites.
Auto-sklearn 2.0: Hands-free automl via meta-learning
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Catboost: unbiased boosting with categorical features
Liudmila Prokhorenkova, Gleb Gusev, Aleksandr Vorobev, Anna Veronika Dorogush, and Andrey Gulin · 2018
Cited alongside, same era.
Regularization learning networks: deep learning for tabular datasets
Ira Shavitt and Eran Segal · 2018
Cited alongside, same era.
Optuna: A next-generation hyperparameter optimization framework
Takuya Akiba, Shotaro Sano, Toshihiko Yanase, Takeru Ohta, and Masanori Koyama · 2019
Cited alongside, same era.
An open source automl benchmark
Pieter Gijsbers, Erin LeDell, Janek Thomas, Sébastien Poirier, Bernd Bischl, and Joaquin Vanschoren · 2019
Cited alongside, same era.
Troubling trends in machine learning scholarship: Some ml papers suffer from flaws that could mislead the public and stymie future research
Zachary C Lipton and Jacob Steinhardt · 2019
Cited alongside, same era.
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
Cited alongside, same era.
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
Cited alongside, same era.
Matthias Feurer, Katharina Eggensperger, Stefan Falkner, Marius Lindauer, and Frank Hutter · 2022
Later among the works it cites.
Why do tree-based models still outperform deep learning on typical tabular data?
Leo Grinsztajn, Edouard Oyallon, and Gael Varoquaux · 2022
Later among the works it cites.
Transformers can do bayesian inference
Samuel Müller, Noah Hollmann, Sebastian Pineda Arango, Josif Grabocka, and Frank Hutter · 2022
Later among the works it cites.
Revisiting pretraining objectives for tabular deep learning
Ivan Rubachev, Artem Alekberov, Yury Gorishniy, and Artem Babenko · 2022
Later among the works it cites.
Hopular: Modern hopfield networks for tabular data
Bernhard Schäfl, Lukas Gruber, Angela Bitto-Nemling, and Sepp Hochreiter · 2022
Later among the works it cites.
Tabular data: Deep learning is not all you need
Ravid Shwartz-Ziv and Amitai Armon · 2022
Later among the works it cites.
Trouble with hopular, 2022
Bojan Tunguz · 2022
Later among the works it cites.
Efficient bayesian learning curve extrapolation using prior-data fitted networks
Steven Adriaensen, Herilalaina Rakotoarison, Samuel Müller, and Frank Hutter · 2023
Closest in time.
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
Closest in time.
Tabpfn: A transformer that solves small tabular classification problems in a second
Noah Hollmann, Samuel Müller, Katharina Eggensperger, and Frank Hutter · 2023
Closest in time.
Gpt for semi-automated data science: Introducing caafe for context-aware automated feature engineering
Noah Hollmann, Samuel Müller, and Frank Hutter · 2023
Closest in time.
Tangos: Regularizing tabular neural networks through gradient orthogonalization and specialization
Alan Jeffares, Tennison Liu, Jonathan Crabbé, Fergus Imrie, and Mihaela van der Schaar · 2023
Closest in time.
Forecastpfn: Universal forecasting for healthcare
Gurnoor Singh Khurana, Samuel Dooley, Siddartha Venkat Naidu, and Colin White · 2023
Closest in time.
Transfer learning with deep tabular models
Roman Levin, Valeriia Cherepanova, Avi Schwarzschild, Arpit Bansal, C Bayan Bruss, Tom Goldstein, Andrew Gordon Wilson, and Micah Goldblum · 2023
Closest in time.
Transfer learning with deep tabular models
Roman Levin, Valeriia Cherepanova, Avi Schwarzschild, Arpit Bansal, C Bayan Bruss, Tom Goldstein, Andrew Gordon Wilson, and Micah Goldblum · 2023
Closest in time.
Pfns4bo: In-context learning for bayesian optimization
Samuel Müller, Matthias Feurer, Noah Hollmann, and Frank Hutter · 2023
Closest in time.
Statistical foundations of prior-data fitted networks
Thomas Nagler · 2023
Closest in time.
Xtab: Cross-table pretraining for tabular transformers
Bingzhao Zhu, Xingjian Shi, Nick Erickson, Mu Li, George Karypis, and Mahsa Shoaran · 2023
Closest in time.