Fetching the paper…
Reading the bibliography…
We study the problem of reducing adversarially robust learning to standard PAC learning, i.e.
On the uniform convergence of relative frequencies of events to their probabilities
V. Vapnik and A. Chervonenkis · 1971
Earlier work this paper cites.
Theory of Pattern Recognition
V. Vapnik and A. Chervonenkis · 1974
Earlier work this paper cites.
Densité et dimension
P. Assouad · 1983
Earlier work this paper cites.
Learnability and the Vapnik-Chervonenkis dimension
A. Blumer, A. Ehrenfeucht, D. Haussler, and M. Warmuth · 1989
Earlier work this paper cites.
A general lower bound on the number of examples needed for learning
A. Ehrenfeucht, D. Haussler, M. Kearns, and L. Valiant · 1989
Earlier work this paper cites.
A decision-theoretic generalization of on-line learning and an application to boosting
Yoav Freund and Robert E. Schapire · 1997
Earlier work this paper cites.
Potential-based agnostic boosting
Adam Kalai and Varun Kanade · 2009
Earlier work this paper cites.
Lecture notes - machine learning theory, January 2010
Maria-Florina Balcan · 2010
Earlier work this paper cites.
Boosting
R. E. Schapire and Y. Freund · 2012
Cited alongside, same era.
Evasion attacks against machine learning at test time
Battista Biggio, Igino Corona, Davide Maiorca, Blaine Nelson, Nedim Šrndić, Pavel Laskov, Giorgio Giacinto, and Fabio Roli · 2013
Cited alongside, same era.
Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2013
Cited alongside, same era.
Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2014
Cited alongside, same era.
Learning and inference in the presence of corrupted inputs
Uriel Feige, Yishay Mansour, and Robert E. Schapire · 2015
Cited alongside, same era.
Robust inference for multiclass classification
Uriel Feige, Yishay Mansour, and Robert E. Schapire · 2018
Later among the works it cites.
Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2018
Later among the works it cites.
Improved generalization bounds for robust learning
Idan Attias, Aryeh Kontorovich, and Yishay Mansour · 2019
Later among the works it cites.
Adversarial examples from computational constraints
Sebastien Bubeck, Yin Tat Lee, Eric Price, and Ilya Razenshteyn · 2019
Later among the works it cites.
Vc classes are adversarially robustly learnable, but only improperly
Omar Montasser, Steve Hanneke, and Nathan Srebro · 2019
Later among the works it cites.
Efficiently learning adversarially robust halfspaces with noise
Omar Montasser, Surbhi Goel, Ilias Diakonikolas, and Nati Srebro · 2020
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Ian J. Goodfellow, Jonathon Shlens, and Christian Szegedy · 2015
Cited alongside, same era.
Robust probabilistic inference
Yishay Mansour, Aviad Rubinstein, and Moshe Tennenholtz · 2015
Cited alongside, same era.
Sample compression schemes for VC classes
S. Moran and A. Yehudayoff · 2016
Cited alongside, same era.
How I wasted too long finding a concentration inequality for sums of geometric variables
Daniel G Brown
Cited in the paper.
Closest in time.
Black-box smoothing: A provable defense for pretrained classifiers
Hadi Salman, Mingjie Sun, Greg Yang, Ashish Kapoor, and J Zico Kolter · 2020
Closest in time.