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We propose kernel distributionally robust optimization (Kernel DRO) using insights from the robust optimization theory and functional analysis.
Robust Solutions of Optimization Problems Affected by Uncertain Probabilities
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Ioana Popescu · 2005
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The scenario approach to robust control design
G.C. Calafiore and M.C. Campi · 2006
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Randomized Algorithms for Semi-Infinite Programming Problems
Vladislav B. Tadić, Sean P. Meyn, and Roberto Tempo · 2006
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Consistency and robustness of kernel-based regression in convex risk minimization
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Generalized semi-infinite programming: A tutorial
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A hilbert space embedding for distributions
Alex Smola, Arthur Gretton, Le Song, and Bernhard Schölkopf · 2007
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Generalized Chebyshev Bounds via Semidefinite Programming
Lieven. Vandenberghe, Stephen. Boyd, and Katherine. Comanor · 2007
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Random Features for Large-Scale Kernel Machines
Ali Rahimi and Benjamin Recht · 2008
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Likelihood robust optimization for data-driven problems
Zizhuo Wang, Peter W. Glynn, and Yinyu Ye · 2016
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Data-Driven Robust Optimization
Dimitris Bertsimas, Nathan Kallus, and Vishal Gupta · 2017
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Learning with SGD and Random Features
Luigi Carratino, Alessandro Rudi, and Lorenzo Rosasco · 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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Lipschitz regularity of deep neural networks: Analysis and efficient estimation
Aladin Virmaux and Kevin Scaman · 2018
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Provable defenses against adversarial examples via the convex outer adversarial polytope
Eric Wong and Zico Kolter · 2018
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Support Vector Machines
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Robust Optimization , volume 28
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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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A randomized Mirror-Prox method for solving structured large-scale matrix saddle-point problems
Michel Baes, Michael Bürgisser, and Arkadi Nemirovski · 2011
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Universality, Characteristic Kernels and RKHS Embedding of Measures
Bharath K. Sriperumbudur, Kenji Fukumizu, and Gert R. G. Lanckriet · 2011
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Data-driven risk-averse stochastic optimization with Wasserstein metric
Chaoyue Zhao and Yongpei Guan · 2018
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A Kernel Perspective for Regularizing Deep Neural Networks
Alberto Bietti, Grégoire Mialon, Dexiong Chen, and Julien Mairal · 2019
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Robust Wasserstein Profile Inference and Applications to Machine Learning
Jose Blanchet, Yang Kang, and Karthyek Murthy · 2019
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A course in functional analysis , volume 96
John B Conway · 2019
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A First-Order Algorithmic Framework for Wasserstein Distributionally Robust Logistic Regression
Jiajin Li, Sen Huang, and Anthony Man-Cho So · 2019
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Towards Deep Learning Models Resistant to Adversarial Attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2019
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Distributionally robust optimization and generalization in kernel methods
Matthew Staib and Stefanie Jegelka · 2019
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Distributionally Robust Losses for Latent Covariate Mixtures
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