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We study a robust alternative to empirical risk minimization called distributionally robust learning (DRL), in which one learns to perform against an adversary who can choose the data distribution from a specified set of distributions.
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Charles J Clopper and Egon S Pearson · 1934
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On distributionally robust chance-constrained linear programs
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Ambiguous chance constrained problems and robust optimization
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A robust optimization perspective on stochastic programming
Xin Chen, Melvyn Sim, and Peng Sun · 2007
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Discriminative batch mode active learning
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Multiple-instance active learning
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Distributionally robust optimization and its tractable approximations
Joel Goh and Melvyn Sim · 2010
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Robustness in automatic speech recognition: fundamentals and applications , volume 341
Jean-Claude Junqua and Jean-Paul Haton · 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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A solution to the ecological inference problem: Reconstructing individual behavior from aggregate data
Gary King · 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 · 2013
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Selecting influential examples: Active learning with expected model output changes
Alexander Freytag, Erik Rodner, and Joachim Denzler · 2014
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Deep speech: Scaling up end-to-end speech recognition
Awni Hannun, Carl Case, Jared Casper, Bryan Catanzaro, Greg Diamos, Erich Elsen, Ryan Prenger, Sanjeev Satheesh, Shubho Sengupta, Adam Coates, et al · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Distributionally robust convex optimization
Wolfram Wiesemann, Daniel Kuhn, and Melvyn Sim · 2014
Wasserstein distributional robustness and regularization in statistical learning
Rui Gao, Xi Chen, and Anton J Kleywegt · 2017
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Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2017
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Data-driven robust optimization
Dimitris Bertsimas, Vishal Gupta, and Nathan Kallus · 2018
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Audio adversarial examples: Targeted attacks on speech-to-text
Nicholas Carlini and David Wagner · 2018
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A robust learning approach for regression models based on distributionally robust optimization
Ruidi Chen and Ioannis Ch Paschalidis · 2018
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Distributionally robust logistic regression
Soroosh Shafieezadeh Abadeh, Peyman Mohajerin Mohajerin Esfahani, and Daniel Kuhn · 2015
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An empirical evaluation of deep learning on highway driving
Brody Huval, Tao Wang, Sameep Tandon, Jeff Kiske, Will Song, Joel Pazhayampallil, Mykhaylo Andriluka, Pranav Rajpurkar, Toki Migimatsu, Royce Cheng-Yue, et al · 2015
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Distributional smoothing with virtual adversarial training
Takeru Miyato, Shin-ichi Maeda, Masanori Koyama, Ken Nakae, and Shin Ishii · 2015
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Robust Wasserstein profile inference and applications to machine learning
Jose Blanchet, Yang Kang, and Karthyek Murthy · 2016
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End to end learning for self-driving cars
Mariusz Bojarski, Davide Del Testa, Daniel Dworakowski, Bernhard Firner, Beat Flepp, Prasoon Goyal, Lawrence D Jackel, Mathew Monfort, Urs Muller, Jiakai Zhang, et al · 2016
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Statistics of robust optimization: A generalized empirical likelihood approach
John Duchi, Peter Glynn, and Hongseok Namkoong · 2016
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Distributionally robust stochastic optimization with Wasserstein distance
Rui Gao and Anton J Kleywegt · 2016
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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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Semidefinite relaxations for certifying robustness to adversarial examples
Aditi Raghunathan, Jacob Steinhardt, and Percy S Liang · 2018
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Fast and effective robustness certification
Gagandeep Singh, Timon Gehr, Matthew Mirman, Markus Püschel, and Martin Vechev · 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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A benchmark and comparison of active learning for logistic regression
Yazhou Yang and Marco Loog · 2018
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Quantifying distributional model risk via optimal transport
Jose Blanchet and Karthyek Murthy · 2019
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Certified adversarial robustness via randomized smoothing
Jeremy M Cohen, Elan Rosenfeld, and J Zico Kolter · 2019
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Dheeru Dua and Casey Graff · 2019
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Fast and flexible inference of joint distributions from their marginals
Charlie Frogner and Tomaso Poggio · 2019
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Wasserstein distributionally robust optimization: Theory and applications in machine learning
Daniel Kuhn, Peyman Mohajerin Esfahani, Viet Anh Nguyen, and Soroosh Shafieezadeh-Abadeh · 2019
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Adversarial examples: Attacks and defenses for deep learning
Xiaoyong Yuan, Pan He, Qile Zhu, and Xiaolin Li · 2019
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