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Bagging predictors
Breiman, L · 1996
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Practical nonparametric statistics , volume 350
Conover, W. J · 1999
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A new family of power transformations to improve normality or symmetry
Yeo, I.-K. and Johnson, R. A · 2000
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A preprocessing scheme for high-cardinality categorical attributes in classification and prediction problems
Micci-Barreca, D · 2001
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Scikit-learn: Machine learning in python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al · 2011
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Principles of data integration
Doan, A., Halevy, A., and Ives, Z · 2012
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Yago3: A knowledge base from multilingual wikipedias
Mahdisoltani, F., Biega, J., and Suchanek, F. M · 2013
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Very deep convolutional networks for large-scale image recognition
Simonyan, K. and Zisserman, A · 2015
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Xgboost: A scalable tree boosting system
Chen, T. and Guestrin, C · 2016
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Advances in pre-training distributed word representations
Mikolov, T., Grave, E., Bojanowski, P., Puhrsch, C., and Joulin, A · 2017
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Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I · 2017
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Graph attention networks
Velickovic, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., Bengio, Y., et al · 2017
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CatBoost: Gradient boosting with categorical features support
Dorogush, A. V., Ershov, V., and Gulin, A · 2018
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Representation learning with contrastive predictive coding
Oord, A. v. d., Li, Y., and Vinyals, O · 2018
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Multi-relational poincaré graph embeddings
Balazevic, I., Allen, C., and Hospedales, T · 2019
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding, May 2019
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K · 2019
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Sherlock: A deep learning approach to semantic data type detection
Hulsebos, M., Hu, K., Bakker, M., Zgraggen, E., Satyanarayan, A., Kraska, T., Demiralp, Ç., and Hidalgo, C · 2019
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Neural Oblivious Decision Ensembles for Deep Learning on Tabular Data, September 2019
Popov, S., Morozov, S., and Babenko, A · 2019
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scikit-posthocs: Pairwise multiple comparison tests in python
Terpilowski, M · 2019
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DNF-Net: A Neural Architecture for Tabular Data, June 2020
Abutbul, A., Elidan, G., Katzir, L., and El-Yaniv, R · 2020
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TabNet: Attentive Interpretable Tabular Learning, December 2020
Arik, S. O. and Pfister, T · 2020
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Encoding high-cardinality string categorical variables
Cerda, P. and Varoquaux, G · 2020
ExcelFormer: A Neural Network Surpassing GBDTs on Tabular Data, January 2023
Chen, J., Yan, J., Chen, D. Z., and Wu, J · 2023
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A large-scaled corpus for assessing text readability
Crossley, S., Heintz, A., Choi, J. S., Batchelor, J., Karimi, M., and Malatinszky, A · 2023
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Relational data embeddings for feature enrichment with background information
Cvetkov-Iliev, A., Allauzen, A., and Varoquaux, G · 2023
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TabLib: A Dataset of 627M Tables with Context, October 2023
Eggert, G., Huo, K., Biven, M., and Waugh, J · 2023
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Relational deep learning: Graph representation learning on relational databases
Fey, M., Hu, W., Huang, K., Lenssen, J. E., Ranjan, R., Robinson, J., Ying, R., You, J., and Leskovec, J · 2023
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TURL: Table Understanding through Representation Learning, December 2020
Deng, X., Sun, H., Lees, A., Wu, Y., and Yu, C · 2020
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On the opportunities and risks of foundation models
Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., Bernstein, M. S., Bohg, J., Bosselut, A., Brunskill, E., et al · 2021
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Tabular Data: Deep Learning is Not All You Need, November 2021
Shwartz-Ziv, R. and Armon, A · 2021
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SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training, June 2021
Somepalli, G., Goldblum, M., Schwarzschild, A., Bruss, C. B., and Goldstein, T · 2021
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Subgroup Robustness Grows On Trees: An Empirical Baseline Investigation
Gardner, J., Popovic, Z., and Schmidt, L · 2022
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On embeddings for numerical features in tabular deep learning
Gorishniy, Y., Rubachev, I., and Babenko, A · 2022
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TabR: Tabular Deep Learning Meets Nearest Neighbors in 2023, October 2023a
Gorishniy, Y., Rubachev, I., Kartashev, N., Shlenskii, D., Kotelnikov, A., and Babenko, A · 2023
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TabLLM: Few-shot Classification of Tabular Data with Large Language Models, March 2023
Hegselmann, S., Buendia, A., Lang, H., Agrawal, M., Jiang, X., and Sontag, D · 2023
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TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second, September 2023
Hollmann, N., Müller, S., Eggensperger, K., and Hutter, F · 2023
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GitTables: A Large-Scale Corpus of Relational Tables
Hulsebos, M., Demiralp, Ç., and Groth, P · 2023
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Transfer Learning with Deep Tabular Models, August 2023
Levin, R., Cherepanova, V., Schwarzschild, A., Bansal, A., Bruss, C. B., Goldstein, T., Wilson, A. G., and Goldblum, M · 2023
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When Do Neural Nets Outperform Boosted Trees on Tabular Data?, October 2023
McElfresh, D., Khandagale, S., Valverde, J., C, V. P., Feuer, B., Hegde, C., Ramakrishnan, G., Goldblum, M., and White, C · 2023
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A survey on oversmoothing in graph neural networks
Rusch, T. K., Bronstein, M. M., and Mishra, S · 2023
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The Magellan Data Repository, 2023
Sanjib, D., AnHai, D., Suganthan, P., Chaitanya, G., Pradap, K., Yash, G., and Derek, P · 2023
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Llama: Open and efficient foundation language models
Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al · 2023
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Xtab: Cross-table pretraining for tabular transformers
Zhu, B., Shi, X., Erickson, N., Li, M., Karypis, G., and Shoaran, M · 2023
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Skrub, prepping tables for machine learning
Skrub · 2024
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