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Min-max optimization is emerging as a key framework for analyzing problems of robustness to strategically and adversarially generated data.
Principles Of Mathematical Analysis
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Robust solutions of optimization problems affected by uncertain probabilities
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Introduction to Smooth Manifolds
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Virginie Gabrel, Cécile Murat, and Aurélie Thiele · 2014
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Generative adversarial nets
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Introductory Lectures on Convex Optimization: A Basic Course
Yurii Nesterov · 2014
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A practical guide to robust optimization
Bram L. Gorissen, Ihsan Yanikoglu, and Dick den Hertog · 2015
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Distributionally robust logistic regression
Soroosh Shafieezadeh-Abadeh, Peyman Mohajerin Esfahani, and D. Kuhn · 2015
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Strategic classification
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Stochastic gradient methods for distributionally robust optimization with f-divergences
Hongseok Namkoong and John C Duchi · 2016
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Without-Replacement Sampling for Stochastic Gradient Methods
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Derivative-free and blackbox optimization
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ZOO: Zeroth-Order Optimization-based Black-box Attacks to Deep Neural Networks Without Training Substitute Models
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Yurii Nesterov and Vladimir Spokoiny · 2017
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Certified defenses for data poisoning attacks
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Uber, Lyft drivers coordinate to manipulate surge pricing at Virginia airport over pay concerns: Report
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Gradient-free methods with inexact oracle for convex-concave stochastic saddle-point problem
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Black-box adversarial attacks with limited queries and information
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Uber drivers are reportedly colluding to trigger surge prices because they say the company is not paying them enough
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Closing the convergence gap of SGD without replacement
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Zeroth-order algorithms for nonconvex minimax problems with improved complexities
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Global convergence and variance reduction for a class of nonconvex-nonconcave minimax problems
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