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A key element in solving real-life data science problems is selecting the types of models to use.
The use of ranks to avoid the assumption of normality implicit in the analysis of variance
Milton Friedman · 1937
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Stacked generalization
David H Wolpert · 1992
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Support-vector networks
Corinna Cortes and Vladimir Vapnik · 1995
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Bagging predictors
Leo Breiman · 1996
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Experiments with a new boosting algorithm
Yoav Freund, Robert E Schapire, et al · 1996
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No free lunch theorems for optimization
David H Wolpert and William G Macready · 1997
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Greedy function approximation: a gradient boosting machine
Jerome H Friedman · 2001
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Random forests
Leo Breiman · 2001
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Ensemble selection from libraries of models
Rich Caruana, Alexandru Niculescu-Mizil, Geoff Crew, and Alex Ksikes · 2004
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Pascal large scale learning challenge, 2008
Pascal · 2008
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Introducing LETOR 4.0 datasets
Tao Qin and Tie-Yan Liu · 2013
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Openml: networked science in machine learning
Joaquin Vanschoren, Jan N Van Rijn, Bernd Bischl, and Luis Torgo · 2014
Earlier work this paper cites.
Deep neural decision forests
Peter Kontschieder, Madalina Fiterau, Antonio Criminisi, and Samuel Rota Bulò · 2015
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Hyperopt: a python library for model selection and hyperparameter optimization
James Bergstra, Brent Komer, Chris Eliasmith, Dan Yamins, and David D Cox · 2015
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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WaveNet: A Generative Model for Raw Audio
Aäron van den Oord, Sander Dieleman, Heiga Zen, Karen Simonyan, Oriol Vinyals, Alex Graves, Nal Kalchbrenner, Andrew Senior, and Koray Kavukcuoglu · 2016
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Xgboost: A scalable tree boosting system
Tianqi Chen and Carlos Guestrin · 2016
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Experimenting xgboost algorithm for prediction and classification of different datasets
S Ramraj, Nishant Uzir, R Sunil, and Shatadeep Banerjee · 2016
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Opening the black box of deep neural networks via information
Bert: Pre-training of deep bidirectional transformers for language understanding
J. Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
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Deep learning with xgboost for real estate appraisal
Yun Zhao, Girija Chetty, and Dat Tran · 2019
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Telco customer churn, 2019
IBM · 2019
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Theoretical issues in deep networks
Tomaso Poggio, Andrzej Banburski, and Qianli Liao · 2020
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The dual information bottleneck
Zoe Piran, Ravid Shwartz-Ziv, and Naftali Tishby · 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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Ravid Shwartz-Ziv and Naftali Tishby · 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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Self-normalizing neural networks
Günter Klambauer, Thomas Unterthiner, Andreas Mayr, and Sepp Hochreiter · 2017
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UCI machine learning repository, 2017
Dheeru Dua and Casey Graff · 2017
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Representation compression and generalization in deep neural networks
Ravid Shwartz-Ziv, Amichai Painsky, and Naftali Tishby · 2018
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GLUE: A multi-task benchmark and analysis platform for natural language understanding
Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel Bowman · 2018
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Regularization learning networks: deep learning for tabular datasets
Ira Shavitt and Eran Segal · 2018
Cited alongside, same era.
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Gradient boosting neural networks: Grownet
Sarkhan Badirli, Xuanqing Liu, Zhengming Xing, Avradeep Bhowmik, Khoa Doan, and Sathiya S Keerthi · 2020
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The tree ensemble layer: Differentiability meets conditional computation
Hussein Hazimeh, Natalia Ponomareva, Petros Mol, Zhenyu Tan, and Rahul Mazumder · 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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Tabnet: Attentive interpretable tabular learning
Sercan Arik and Tomas Pfister · 2021
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Net-dnf: Effective deep modeling of tabular data
Liran Katzir, Gal Elidan, and Ran El-Yaniv · 2021
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baosenguo/kaggle-moa-2nd-place-solution, 2021
Baosenguo · 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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Regularization is all you need: Simple neural nets can excel on tabular data
Arlind Kadra, Marius Lindauer, Frank Hutter, and Josif Grabocka · 2021
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