Fetching the paper…
Reading the bibliography…
Many modern machine learning models are trained to achieve zero or near-zero training error in order to obtain near-optimal (but non-zero) test error.
On estimating regression
Elizbar A Nadaraya · 1964
Earlier work this paper cites.
Smooth regression analysis
Geoffrey S Watson · 1964
Earlier work this paper cites.
Nearest neighbor pattern classification
Thomas Cover and Peter Hart · 1967
Earlier work this paper cites.
A two-dimensional interpolation function for irregularly-spaced data
Donald Shepard · 1968
Earlier work this paper cites.
On the convex hull of uniform random points in a simpled-polytope
Fernando Affentranger and John A Wieacker · 1991
Earlier work this paper cites.
Simplicial multivariable linear interpolation
John H Halton · 1991
Earlier work this paper cites.
Function learning from interpolation
Martin Anthony and Peter L Bartlett · 1995
Earlier work this paper cites.
Fat-shattering and the learnability of real-valued functions
Peter L Bartlett, Philip M Long, and Robert C Williamson · 1996
Earlier work this paper cites.
Multidimensional triangulation and interpolation for reinforcement learning
Scott Davies · 1997
Earlier work this paper cites.
The hilbert kernel regression estimate
Luc Devroye, Laszlo Györfi, and Adam Krzyżak · 1998
Earlier work this paper cites.
Boosting the margin: a new explanation for the effectiveness of voting methods
Robert E Schapire, Yoav Freund, Peter Bartlett, and Wee Sun Lee · 1998
Earlier work this paper cites.
Neural Network Learning: Theoretical Foundations
Martin Anthony and Peter L Bartlett · 1999
Earlier work this paper cites.
Smooth discrimination analysis
Enno Mammen and Alexandre B Tsybakov · 1999
Earlier work this paper cites.
Random forests
Leo Breiman · 2001
Earlier work this paper cites.
Pert-perfect random tree ensembles
Adele Cutler and Guohua Zhao · 2001
Earlier work this paper cites.
Learning with kernels: support vector machines, regularization, optimization, and beyond
Bernhard Scholkopf and Alexander J Smola · 2001
Earlier work this paper cites.
Stability and generalization
Olivier Bousquet and André Elisseeff · 2002
Earlier work this paper cites.
A Distribution-Free Theory of Nonparametric Regression
László Györfi, Michael Kohler, Adam Krzyzak, and Harro Walk · 2002
Cited alongside, same era.
Empirical margin distributions and bounding the generalization error of combined classifiers
Vladimir Koltchinskii and Dmitry Panchenko · 2002
Cited alongside, same era.
Semi-supervised learning using gaussian fields and harmonic functions
Xiaojin Zhu, Zoubin Ghahramani, and John D Lafferty · 2003
Cited alongside, same era.
Regularization and semi-supervised learning on large graphs
Mikhail Belkin, Irina Matveeva, and Partha Niyogi · 2004
Cited alongside, same era.
Polyhedral computation FAQ
Komei Fukuda · 2004
Cited alongside, same era.
Discrete aspects of stochastic geometry
Rolf Schneider · 2004
Cited alongside, same era.
Boosting: Foundations and algorithms
Robert E Schapire and Yoav Freund · 2012
Later among the works it cites.
Rates of convergence for nearest neighbor classification
Kamalika Chaudhuri and Sanjoy Dasgupta · 2014
Later among the works it cites.
Understanding machine learning: From theory to algorithms
Shai Shalev-Shwartz and Shai Ben-David · 2014
Later among the works it cites.
Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2014
Later among the works it cites.
Algorithmic stability for adaptive data analysis
Raef Bassily, Kobbi Nissim, Adam Smith, Thomas Steinke, Uri Stemmer, and Jonathan Ullman · 2016
Later among the works it cites.
Robustness of classifiers: from adversarial to random noise
Alhussein Fawzi, Seyed-Mohsen Moosavi-Dezfooli, and Pascal Frossard · 2016
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Optimal aggregation of classifiers in statistical learning
Alexander B Tsybakov · 2004
Cited alongside, same era.
All of statistics
Larry Wasserman · 2004
Cited alongside, same era.
Risk bounds for statistical learning
Pascal Massart and Élodie Nédélec · 2006
Cited alongside, same era.
All of nonparametric statistics
Larry Wasserman · 2006
Cited alongside, same era.
Complexity of delaunay triangulation for points on lower-dimensional polyhedra
Nina Amenta, Dominique Attali, and Olivier Devillers · 2007
Cited alongside, same era.
Fast learning rates for plug-in classifiers
Jean-Yves Audibert and Alexandre B Tsybakov · 2007
Cited alongside, same era.
Deep Learning
Ian Goodfellow, Yoshua Bengio, and Aaron Courville · 2016
Later among the works it cites.
Spectrally-normalized margin bounds for neural networks
Peter Bartlett, Dylan J Foster, and Matus Telgarsky · 2017
Later among the works it cites.
Fisher-rao metric, geometry, and complexity of neural networks
Tengyuan Liang, Tomaso Poggio, Alexander Rakhlin, and James Stokes · 2017
Later among the works it cites.
Deep learning tutorial at the Simons Institute, Berkeley, 2017
Ruslan Salakhutdinov · 2017
Later among the works it cites.
One pixel attack for fooling deep neural networks
Jiawei Su, Danilo Vasconcellos Vargas, and Sakurai Kouichi · 2017
Later among the works it cites.
Explaining the success of adaboost and random forests as interpolating classifiers
Abraham J Wyner, Matthew Olson, Justin Bleich, and David Mease · 2017
Later among the works it cites.
Understanding deep learning requires rethinking generalization
Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, and Oriol Vinyals · 2017
Later among the works it cites.
Size-independent sample complexity of neural networks
Noah Golowich, Alexander Rakhlin, and Ohad Shamir · 2018
Closest in time.
Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2018
Closest in time.
A PAC-bayesian approach to spectrally-normalized margin bounds for neural networks
Behnam Neyshabur, Srinadh Bhojanapalli, and Nathan Srebro · 2018
Closest in time.
Analyzing the robustness of nearest neighbors to adversarial examples
Yizhen Wang, Somesh Jha, and Kamalika Chaudhuri · 2018
Closest in time.