Fetching the paper…
Reading the bibliography…
Neural networks are vulnerable to adversarial examples and researchers have proposed many heuristic attack and defense mechanisms.
Optimization by Vector Space Methods
D. Luenberger · 1969
Earlier work this paper cites.
Weak Convergence and Empirical Processes: With Applications to Statistics
A. W. van der Vaart and J. A. Wellner · 1996
Earlier work this paper cites.
Variational Analysis
R. T. Rockafellar and R. J. B. Wets · 1998
Earlier work this paper cites.
Convergence of Probability Measures
P. Billingsley · 1999
Earlier work this paper cites.
A unified analysis of value-function-based reinforcement-learning algorithms
C. Szepesvári and M. L. Littman · 1999
Earlier work this paper cites.
Rademacher and Gaussian complexities: Risk bounds and structural results
P. L. Bartlett and S. Mendelson · 2002
Earlier work this paper cites.
Theory of classification: a survey of some recent advances
S. Boucheron, O. Bousquet, and G. Lugosi · 2005
Earlier work this paper cites.
Maximum margin planning
N. Ratliff, J. A. Bagnell, and M. Zinkevich · 2006
Earlier work this paper cites.
A robust optimization perspective on stochastic programming
X. Chen, M. Sim, and P. Sun · 2007
Earlier work this paper cites.
Robust Optimization
A. Ben-Tal, L. E. Ghaoui, and A. Nemirovski · 2009
Earlier work this paper cites.
Optimal Transport: Old and New
C. Villani · 2009
Earlier work this paper cites.
Robustness and regularization of support vector machines
H. Xu, C. Caramanis, and S. Mannor · 2009
Earlier work this paper cites.
A theory of learning from different domains
S. Ben-David, J. Blitzer, K. Crammer, A. Kulesza, F. Pereira, and J. Vaughan · 2010
Earlier work this paper cites.
Distributionally robust optimization under moment uncertainty with application to data-driven problems
E. Delage and Y. Ye · 2010
Earlier work this paper cites.
Distributionally robust optimization and its tractable approximations
J. Goh and M. Sim · 2010
Earlier work this paper cites.
Solving variational inequalities with the stochastic mirror-prox algorithm
A. Juditsky, A. Nemirovski, and C. Tauvel · 2011
Earlier work this paper cites.
Novel dataset for fine-grained image categorization: Stanford dogs
A. Khosla, N. Jayadevaprakash, B. Yao, and F.-F. Li · 2011
Earlier work this paper cites.
A distributional interpretation of robust optimization
H. Xu, C. Caramanis, and S. Mannor · 2012
Earlier work this paper cites.
Robust solutions of optimization problems affected by uncertain probabilities
A. Ben-Tal, D. den Hertog, A. D. Waegenaere, B. Melenberg, and G. Rennen · 2013
Earlier work this paper cites.
Data-driven robust optimization
D. Bertsimas, V. Gupta, and N. Kallus · 2013
Earlier work this paper cites.
Perturbation analysis of optimization problems
J. F. Bonnans and A. Shapiro · 2013
Earlier work this paper cites.
Concentration Inequalities: a Nonasymptotic Theory of Independence
S. Boucheron, G. Lugosi, and P. Massart · 2013
Earlier work this paper cites.
Stochastic first- and zeroth-order methods for nonconvex stochastic programming
S. Ghadimi and G. Lan · 2013
Cited alongside, same era.
Proximal algorithms
N. Parikh and S. Boyd · 2013
Cited alongside, same era.
Intriguing properties of neural networks
C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus · 2013
Cited alongside, same era.
Accelerated schemes for a class of variational inequalities
Y. Chen, G. Lan, and Y. Ouyang · 2014
Cited alongside, same era.
Fast and accurate deep network learning by exponential linear units (elus)
D.-A. Clevert, T. Unterthiner, and S. Hochreiter · 2015
Cited alongside, same era.
Towards robust deep neural networks with bang
A. Rozsa, M. Gunther, and T. E. Boult · 2016
Later among the works it cites.
Spectrally-normalized margin bounds for neural networks
P. L. Bartlett, D. J. Foster, and M. J. Telgarsky · 2017
Closest in time.
Data-driven optimal transport cost selection for distributionally robust optimizatio
J. Blanchet, Y. Kang, F. Zhang, and K. Murthy · 2017
Closest in time.
Adversarial patch
T. Brown, D. Mane, A. Roy, M. Abadi, and J. Gilmer · 2017
Closest in time.
Towards evaluating the robustness of neural networks
N. Carlini and D. Wagner · 2017
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
P. M. Esfahani and D. Kuhn · 2015
Cited alongside, same era.
Explaining and harnessing adversarial examples
I. J. Goodfellow, J. Shlens, and C. Szegedy · 2015
Cited alongside, same era.
Quantifying input uncertainty in stochastic optimization
H. Lam and E. Zhou · 2015
Cited alongside, same era.
Distributional smoothing with virtual adversarial training
T. Miyato, S.-i. Maeda, M. Koyama, K. Nakae, and S. Ishii · 2015
Cited alongside, same era.
Human-level control through deep reinforcement learning
V. Mnih, K. Kavukcuoglu, D. Silver, A. A. Rusu, J. Veness, M. G. Bellemare, A. Graves, M. Riedmiller, A. K. Fidjeland, G. Ostrovski, et al · 2015
Cited alongside, same era.
Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
A. Nguyen, J. Yosinski, and J. Clune · 2015
Cited alongside, same era.
Distributionally robust logistic regression
S. Shafieezadeh-Abadeh, P. M. Esfahani, and D. Kuhn · 2015
Cited alongside, same era.
G. K. Dziugaite and D. M. Roy · 2017
Closest in time.
Wasserstein distributional robustness and regularization in statistical learning
R. Gao, X. Chen, and A. J. Kleywegt · 2017
Closest in time.
Adversarial example defenses: Ensembles of weak defenses are not strong
W. He, J. Wei, X. Chen, N. Carlini, and D. Song · 2017
Closest in time.
Safety verification of deep neural networks
X. Huang, M. Kwiatkowska, S. Wang, and M. Wu · 2017
Closest in time.
Provable defenses against adversarial examples via the convex outer adversarial polytope
J. Z. Kolter and E. Wong · 2017
Closest in time.
Minimax statistical learning and domain adaptation with Wasserstein distances
J. Lee and M. Raginsky · 2017
Closest in time.
Towards deep learning models resistant to adversarial attacks
A. Madry, A. Makelov, L. Schmidt, D. Tsipras, and A. Vladu · 2017
Closest in time.
Variance regularization with convex objectives
H. Namkoong and J. C. Duchi · 2017
Closest in time.
Exploring generalization in deep learning
B. Neyshabur, S. Bhojanapalli, D. McAllester, and N. Srebro · 2017
Closest in time.
Ensemble adversarial training: Attacks and defenses
F. Tramèr, A. Kurakin, N. Papernot, D. Boneh, and P. McDaniel · 2017
Closest in time.
J. Blanchet, K. Murthy, and F. Zhang · 2018
Closest in time.
H. Lam · 2018
Closest in time.
Certified defenses against adversarial examples
A. Raghunathan, J. Steinhardt, and P. Liang · 2018
Closest in time.
Certifying some distributional robustness with principled adversarial training
A. Sinha, H. Namkoong, and J. Duchi · 2018
Closest in time.
Generalizing to unseen domains via adversarial data augmentation
R. Volpi, H. Namkoong, J. Duchi, V. Murino, and S. Savarese · 2018
Closest in time.
Wasserstein distributionally robust optimization: Theory and applications in machine learning
D. Kuhn, P. M. Esfahani, V. A. Nguyen, and S. Shafieezadeh-Abadeh · 2019
Closest in time.