Fetching the paper…
Reading the bibliography…
Model selection on validation data is an essential step in machine learning.
Cross-validatory choice and assessment of statistical predictions
Stone, Mervyn · 1974
Earlier work this paper cites.
The predictive sample reuse method with applications
Geisser, Seymour · 1975
Earlier work this paper cites.
Distribution-free performance bounds for potential function rules
Devroye, Luc and Wagner, Terry · 1979
Earlier work this paper cites.
The jackknife, the bootstrap, and other resampling plans , volume 38
Efron, Bradley · 1982
Earlier work this paper cites.
A note on screening regression equations
Freedman, David A and Freedman, David A · 1983
Earlier work this paper cites.
An introduction to kernel and nearest-neighbor nonparametric regression
Altman, Naomi S · 1992
Earlier work this paper cites.
Selecting a classification method by cross-validation
Schaffer, Cullen · 1993
Earlier work this paper cites.
A study of cross-validation and bootstrap for accuracy estimation and model selection
Kohavi, Ron et al · 1995
Earlier work this paper cites.
Support vector machines
Hearst, Marti A., Dumais, Susan T, Osuna, Edgar, Platt, John, and Scholkopf, Bernhard · 1998
Earlier work this paper cites.
The mnist database of handwritten digits
LeCun, Yann · 1998
Cited alongside, same era.
A study about algorithmic stability and their relation to generalization performances
e Elissee, Andr · 2000
Cited alongside, same era.
Stability and generalization
Bousquet, Olivier and Elisseeff, André · 2002
Cited alongside, same era.
Leave-one-out error and stability of learning algorithms with applications
Elisseeff, André, Pontil, Massimiliano, et al · 2003
Cited alongside, same era.
Learning theory: stability is sufficient for generalization and necessary and sufficient for consistency of empirical risk minimization
Mukherjee, Sayan, Niyogi, Partha, Poggio, Tomaso, and Rifkin, Ryan · 2006
Cited alongside, same era.
Learning multiple layers of features from tiny images
Krizhevsky, Alex and Hinton, Geoffrey · 2009
On over-fitting in model selection and subsequent selection bias in performance evaluation
Cawley, Gavin C and Talbot, Nicola LC · 2010
Later among the works it cites.
Scientific method: statistical errors
Nuzzo, Regina · 2014
Later among the works it cites.
Understanding machine learning: From theory to algorithms
Shalev-Shwartz, Shai and Ben-David, Shai · 2014
Later among the works it cites.
The reusable holdout: Preserving validity in adaptive data analysis
Dwork, Cynthia, Feldman, Vitaly, Hardt, Moritz, Pitassi, Toniann, Reingold, Omer, and Roth, Aaron · 2015
Later among the works it cites.
The extent and consequences of p-hacking in science
Head, Megan L, Holman, Luke, Lanfear, Rob, Kahn, Andrew T, and Jennions, Michael D · 2015
Later among the works it cites.
Tensorflow: A system for large-scale machine learning
Abadi, Martín, Barham, Paul, Chen, Jianmin, Chen, Zhifeng, Davis, Andy, Dean, Jeffrey, Devin, Matthieu, Ghemawat, Sanjay, Irving, Geoffrey, Isard, Michael, et al · 2016
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Dataset shift in machine learning
Quionero-Candela, Joaquin, Sugiyama, Masashi, Schwaighofer, Anton, and Lawrence, Neil D · 2009
Cited alongside, same era.
Cross-validation
Refaeilzadeh, Payam, Tang, Lei, and Liu, Huan · 2009
Cited alongside, same era.
Learnability and stability in the general learning setting
Shalev-Shwartz, Shai, Shamir, Ohad, Sridharan, Karthik, and Srebro, Nathan · 2009
Cited alongside, same era.
Later among the works it cites.
Densely connected convolutional networks
Huang, Gao, Liu, Zhuang, Weinberger, Kilian Q, and van der Maaten, Laurens · 2016
Later among the works it cites.
Guilt-free data reuse
Dwork, Cynthia, Feldman, Vitaly, Hardt, Moritz, Pitassi, Toniann, Reingold, Omer, and Roth, Aaron · 2017
Later among the works it cites.