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Mach. Learn. , 45(1):5–32, 2001
Leo Breiman · 2001
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Benchmarking default prediction models: Pitfalls and remedies in model validation
Roger M Stein · 2002
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Estimating mutual information
Alexander Kraskov, Harald Stögbauer, and Peter Grassberger · 2004
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Scikit-learn: Machine learning in Python
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay · 2011
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Evaluating recommendation systems
Guy Shani and Asela Gunawardana · 2011
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Acquire valued shoppers challenge, 2014
Will Cukierski DMDave, Todd B · 2014
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kaggle-acquire-valued-shoppers-challenge, 2014
github MLWave · 2014
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Openml electricity dataset, 2014
Jan van Rijn · 2014
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Homesite quote conversion, 2015
Will Cukierski Darrel, Stephen D Stayton · 2015
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Xgboost: A scalable tree boosting system
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Deep coral: Correlation alignment for deep domain adaptation
Baochen Sun and Kate Saenko · 2016
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Sberbank russian housing market, 2017
DataCanary Alexey Matveev, Anastasia Sidorova 50806198 · 2017
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Alijs and Johnpateha · 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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Additional data - tverskoe issue, 2017
Anastasia Sidorova · 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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Optuna: A next-generation hyperparameter optimization framework
Takuya Akiba, Shotaro Sano, Toshihiko Yanase, Takeru Ohta, and Masanori Koyama · 2019
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Openml airlines dataset, 2019
Alexander Guillermo Segura Ballesteros · 2019
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On the spectral bias of neural networks
Nasim Rahaman, Aristide Baratin, Devansh Arpit, Felix Draxler, Min Lin, Fred Hamprecht, Yoshua Bengio, and Aaron Courville · 2019
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What neural networks memorize and why: Discovering the long tail via influence estimation
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Nikhil Simha · 2020
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Openml bike sharing demand dataset, 2020
Jan van Rijn · 2020
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Revisiting pretraining objectives for tabular deep learning
Ivan Rubachev, Artem Alekberov, Yury Gorishniy, and Artem Babenko · 2022
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Wild-time: A benchmark of in-the-wild distribution shift over time
Huaxiu Yao, Caroline Choi, Bochuan Cao, Yoonho Lee, Pang Wei W Koh, and Chelsea Finn · 2022
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Dedrift: Robust similarity search under content drift
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Benchmarking distribution shift in tabular data with tableshift
Joshua P Gardner, Zoran Popovi, and Ludwig Schmidt · 2023
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Tabpfn: A transformer that solves small tabular classification problems in a second
Noah Hollmann, Samuel Müller, Katharina Eggensperger, and Frank Hutter · 2023
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Real-time data infrastructure at uber
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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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Ml feature serving infrastructure at lyft, 2021
Vinay Kakade · 2021
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Self-attention between datapoints: Going beyond individual input-output pairs in deep learning
Jannik Kossen, Neil Band, Clare Lyle, Aidan N. Gomez, Tom Rainforth, and Yarin Gal · 2021
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Shifts: A dataset of real distributional shift across multiple large-scale tasks
Andrey Malinin, Neil Band, German Chesnokov, Yarin Gal, Mark John Francis Gales, Alexey Noskov, Andrey Ploskonosov, Liudmila Prokhorenkova, Ivan Provilkov, Vatsal Raina, Vyas Raina, Mariya Shmatova, Panos Tigas, and Boris Yangel · 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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Tangos: Regularizing tabular neural networks through gradient orthogonalization and specialization
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Large-scale study of temporal shift in health insurance claims
Christina X Ji, Ahmed M Alaa, and David Sontag · 2023
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Last layer re-training is sufficient for robustness to spurious correlations
Polina Kirichenko, Pavel Izmailov, and Andrew Gordon Wilson · 2023
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Wild-tab: A benchmark for out-of-distribution generalization in tabular regression, 2023
Sergey Kolesnikov · 2023
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When do neural nets outperform boosted trees on tabular data?
Duncan McElfresh, Sujay Khandagale, Jonathan Valverde, Ganesh Ramakrishnan, Micah Goldblum, Colin White, et al · 2023
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Modern neighborhood components analysis: A deep tabular baseline two decades later
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