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Natural analysts in adaptive data analysis
Original
T. Zrnic and M. Hardt · 1901
Earlier work this paper cites.
Random search and reproducibility for neural architecture search
Original
L. Li and A. Talwalkar · 1902
Earlier work this paper cites.
Do ImageNet classifiers generalize to ImageNet?
Original
B. Recht, R. Roelofs, L. Schmidt, and V. Shankar · 1902
Earlier work this paper cites.
The advantages of multiple classes for reducing overfitting from test set reuse
Original
V. Feldman, R. Frostig, and M. Hardt · 1905
Earlier work this paper cites.
Cold Case: The Lost MNIST Digits
Original
C. Yadav and L. Bottou · 1905
Earlier work this paper cites.
A probabilistic theory of pattern recognition
L. Devroye, L. Györfi, and G. Lugosi · 1996
Earlier work this paper cites.
Quantitatively Tight Sample Complexity Bounds
J. Langford · 2002
Earlier work this paper cites.

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Reading the bibliography…

2019

Mania, Horia, Miller, John, Schmidt, Ludwig et al.

Excessive reuse of test data has become commonplace in today's machine learning workflows.

Popular benchmarks, competitions, industrial scale tuning, among other applications, all involve test data reuse beyond guidance by statistical confidence bounds.
Nonetheless, recent replication studies give evidence that popular benchmarks continue to support progress despite years of extensive reuse.
We proffer a new explanation for the apparent longevity of test data: Many proposed models are similar in their predictions and we prove that this similarity mitigates overfitting.

Natural analysts in adaptive data analysis
Original
T. Zrnic and M. Hardt · 1901
Earlier work this paper cites.
Random search and reproducibility for neural architecture search
Original
L. Li and A. Talwalkar · 1902
Earlier work this paper cites.
Do ImageNet classifiers generalize to ImageNet?
Original
B. Recht, R. Roelofs, L. Schmidt, and V. Shankar · 1902
Earlier work this paper cites.
The advantages of multiple classes for reducing overfitting from test set reuse
Original
V. Feldman, R. Frostig, and M. Hardt · 1905
Earlier work this paper cites.
Cold Case: The Lost MNIST Digits
Original
C. Yadav and L. Bottou · 1905
Earlier work this paper cites.
A probabilistic theory of pattern recognition
L. Devroye, L. Györfi, and G. Lugosi · 1996
Earlier work this paper cites.
Quantitatively Tight Sample Complexity Bounds
J. Langford · 2002
Earlier work this paper cites.

ImageNet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
Cited alongside, same era.
Learning multiple layers of features from tiny images, 2009
A. Krizhevsky · 2009
Cited alongside, same era.
Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
Cited alongside, same era.
Very deep convolutional networks for large-scale image recognition
Original
K. Simonyan and A. Zisserman · 2014
Cited alongside, same era.
The Ladder: A reliable leaderboard for machine learning competitions
Original
A. Blum and M. Hardt · 2015
Cited alongside, same era.
Preserving statistical validity in adaptive data analysis

Going deeper with convolutions
Original
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich · 2015
Later among the works it cites.
Algorithmic stability for adaptive data analysis
Original
R. Bassily, K. Nissim, A. Smith, T. Steinke, U. Stemmer, and J. Ullman · 2016
Later among the works it cites.
Deep residual learning for image recognition
Original
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Later among the works it cites.
Densely connected convolutional networks
Original
G. Huang, Z. Liu, K. Q. Weinberger, and L. van der Maaten · 2017
Later among the works it cites.
Information, privacy and stability in adaptive data analysis, 2017

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