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Distributionally robust optimization (DRO) has attracted attention in machine learning due to its connections to regularization, generalization, and robustness.
A min-max solution of an inventory problem
Herbert Scarf · 1958
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Optimal Transport: Old and New (Grundlehren der mathematischen Wissenschaften)
Cédric Villani · 2008
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Empirical Bernstein bounds and sample variance penalization
Andreas Maurer and Massimiliano Pontil · 2009
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Robustness and regularization of support vector machines
Huan Xu, Constantine Caramanis, and Shie Mannor · 2009
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Distributionally robust optimization under moment uncertainty with application to data-driven problems
Erick Delage and Yinyu Ye · 2010
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Distributionally robust optimization and its tractable approximations
Joel Goh and Melvyn Sim · 2010
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A kernel two-sample test
Arthur Gretton, Karsten M. Borgwardt, Malte J. Rasch, Bernhard Schölkopf, and Alexander Smola · 2012
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Robust solutions of optimization problems affected by uncertain probabilities
Aharon Ben-Tal, Dick den Hertog, Anja De Waegenaere, Bertrand Melenberg, and Gijs Rennen · 2013
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Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2014
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Training generative neural networks via maximum mean discrepancy optimization
Gintare Karolina Dziugaite, Daniel M. Roy, and Zoubin Ghahramani · 2015
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On the rate of convergence in Wasserstein distance of the empirical measure
Nicolas Fournier and Arnaud Guillin · 2015
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Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2015
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Robust Empirical Optimization is Almost the Same As Mean-Variance Optimization
Jun-ya Gotoh, Michael Kim, and Andrew Lim · 2015
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Generative moment matching networks
Yujia Li, Kevin Swersky, and Rich Zemel · 2015
Cited alongside, same era.
Distributionally robust logistic regression
Soroosh Shafieezadeh Abadeh, Peyman Mohajerin Mohajerin Esfahani, and Daniel Kuhn · 2015
Cited alongside, same era.
Robust wasserstein profile inference and applications to machine learning
Jose Blanchet, Yang Kang, and Karthyek Murthy · 2016
Cited alongside, same era.
A kernel test of goodness of fit
Kacper Chwialkowski, Heiko Strathmann, and Arthur Gretton · 2016
Cited alongside, same era.
Statistics of robust optimization: A generalized empirical likelihood approach
John Duchi, Peter Glynn, and Hongseok Namkoong · 2016
Cited alongside, same era.
Distributionally robust stochastic optimization with Wasserstein distance
Distributionally robust deep learning as a generalization of adversarial training
Matthew Staib and Stefanie Jegelka · 2017
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Generative models and model criticism via optimized maximum mean discrepancy
Dougal J Sutherland, Hsiao-Yu Tung, Heiko Strathmann, Soumyajit De, Aaditya Ramdas, Alex Smola, and Arthur Gretton · 2017
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Data-driven robust optimization
Dimitris Bertsimas, Vishal Gupta, and Nathan Kallus · 2018
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Demystifying MMD GANs
Mikołaj Bińkowski, Dougal J. Sutherland, Michael Arbel, and Arthur Gretton · 2018
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Jose Blanchet, Karthyek Murthy, and Fan Zhang · 2018
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Fairness without demographics in repeated loss minimization
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Rui Gao and Anton J Kleywegt · 2016
Cited alongside, same era.
Robust Sensitivity Analysis for Stochastic Systems
Henry Lam · 2016
Cited alongside, same era.
A kernelized Stein discrepancy for goodness-of-fit tests
Qiang Liu, Jason Lee, and Michael Jordan · 2016
Cited alongside, same era.
Data-driven optimal transport cost selection for distributionally robust optimization
Jose Blanchet, Yang Kang, Fan Zhang, and Karthyek Murthy · 2017
Cited alongside, same era.
Wasserstein distributional robustness and regularization in statistical learning
Rui Gao, Xi Chen, and Anton J Kleywegt · 2017
Cited alongside, same era.
A linear-time kernel goodness-of-fit test
Wittawat Jitkrittum, Wenkai Xu, Zoltan Szabo, Kenji Fukumizu, and Arthur Gretton · 2017
Cited alongside, same era.
Kernel mean embedding of distributions: A review and beyond
Krikamol Muandet, Kenji Fukumizu, Bharath Sriperumbudur, and Bernhard Schölkopf · 2017
Cited alongside, same era.
Tatsunori Hashimoto, Megha Srivastava, Hongseok Namkoong, and Percy Liang · 2018
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Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2018
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Data-driven distributionally robust optimization using the Wasserstein metric: performance guarantees and tractable reformulations
Peyman Mohajerin Esfahani and Daniel Kuhn · 2018
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Foundations of machine learning
Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar · 2018
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Certifying some distributional robustness with principled adversarial training
Aman Sinha, Hongseok Namkoong, and John Duchi · 2018
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Group invariance, stability to deformations, and complexity of deep convolutional representations
Alberto Bietti and Julien Mairal · 2019
Closest in time.
A kernel perspective for regularizing deep neural networks
Alberto Bietti, Grégoire Mialon, Dexiong Chen, and Julien Mairal · 2019
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Distributionally robust submodular maximization
Matthew Staib, Bryan Wilder, and Stefanie Jegelka · 2019
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