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[1] Tijana Zrnic,et al. Outside the Echo Chamber: Optimizing the Performative Risk , 2021, ICML.
[2] Aryan Mokhtari,et al. A Unified Analysis of Extra-gradient and Optimistic Gradient Methods for Saddle Point Problems: Proximal Point Approach , 2019, AISTATS.
[3] Cécile Murat,et al. Recent advances in robust optimization: An overview , 2014, Eur. J. Oper. Res..
[4] Ohad Shamir,et al. Without-Replacement Sampling for Stochastic Gradient Methods , 2016, NIPS.
[5] Christopher Ré,et al. Parallel stochastic gradient algorithms for large-scale matrix completion , 2013, Math. Program. Comput..
[6] Dick den Hertog,et al. A practical guide to robust optimization , 2015, 1501.02634.
[7] Celestine Mendler-Dünner,et al. Stochastic Optimization for Performative Prediction , 2020, NeurIPS.
[8] Percy Liang,et al. Certified Defenses for Data Poisoning Attacks , 2017, NIPS.
[9] John C. Duchi,et al. Stochastic Gradient Methods for Distributionally Robust Optimization with f-divergences , 2016, NIPS.
[10] Constantine Caramanis,et al. Theory and Applications of Robust Optimization , 2010, SIAM Rev..
[11] Christos H. Papadimitriou,et al. Strategic Classification , 2015, ITCS.
[12] Jinfeng Yi,et al. ZOO: Zeroth Order Optimization Based Black-box Attacks to Deep Neural Networks without Training Substitute Models , 2017, AISec@CCS.
[13] Daniel Kuhn,et al. Distributionally Robust Logistic Regression , 2015, NIPS.
[14] Jason D. Lee,et al. Solving a Class of Non-Convex Min-Max Games Using Iterative First Order Methods , 2019, NeurIPS.
[15] Adam Tauman Kalai,et al. Online convex optimization in the bandit setting: gradient descent without a gradient , 2004, SODA '05.
[16] David S. Leslie,et al. Bandit learning in concave $N$-person games , 2018, 1810.01925.
[17] W. Rudin. Principles of mathematical analysis , 1964 .
[18] Yoshua Bengio,et al. Generative Adversarial Nets , 2014, NIPS.
[19] Shiqian Ma,et al. Zeroth-Order Algorithms for Nonconvex Minimax Problems with Improved Complexities , 2020, ArXiv.
[20] Sijia Liu,et al. Min-Max Optimization without Gradients: Convergence and Applications to Adversarial ML , 2019, ArXiv.
[21] John M. Lee. Introduction to Smooth Manifolds , 2002 .
[22] Pramod K. Varshney,et al. A Primer on Zeroth-Order Optimization in Signal Processing and Machine Learning: Principals, Recent Advances, and Applications , 2020, IEEE Signal Processing Magazine.
[23] Celestine Mendler-Dünner,et al. Performative Prediction , 2020, ICML.
[24] Suvrit Sra,et al. Random Shuffling Beats SGD after Finite Epochs , 2018, ICML.
[25] Bernhard Schölkopf,et al. Optimal Decision Making Under Strategic Behavior , 2019, ArXiv.
[26] Michael I. Jordan,et al. What is Local Optimality in Nonconvex-Nonconcave Minimax Optimization? , 2019, ICML.
[27] D. Drusvyatskiy,et al. Stochastic Optimization with Decision-Dependent Distributions , 2020, Math. Oper. Res..
[28] Jinfeng Yi,et al. AutoZOOM: Autoencoder-based Zeroth Order Optimization Method for Attacking Black-box Neural Networks , 2018, AAAI.
[29] Neil D. Lawrence,et al. Dataset Shift in Machine Learning , 2009 .
[30] Anja De Waegenaere,et al. Robust Solutions of Optimization Problems Affected by Uncertain Probabilities , 2011, Manag. Sci..
[31] Konstantin Mishchenko,et al. Random Reshuffling: Simple Analysis with Vast Improvements , 2020, NeurIPS.
[32] Yurii Nesterov,et al. Introductory Lectures on Convex Optimization - A Basic Course , 2014, Applied Optimization.
[33] James C. Spall,et al. A one-measurement form of simultaneous perturbation stochastic approximation , 1997, Autom..
[34] Aaron Roth,et al. Strategic Classification from Revealed Preferences , 2017, EC.
[35] Dimitris Papailiopoulos,et al. Closing the convergence gap of SGD without replacement , 2020, ICML.
[36] Sanjay Mehrotra,et al. Distributionally Robust Optimization: A Review , 2019, ArXiv.
[37] Xiang Gao,et al. On the Information-Adaptive Variants of the ADMM: An Iteration Complexity Perspective , 2017, Journal of Scientific Computing.
[38] Tong Zhang,et al. NATTACK: Learning the Distributions of Adversarial Examples for an Improved Black-Box Attack on Deep Neural Networks , 2019, ICML.
[39] Yurii Nesterov,et al. Random Gradient-Free Minimization of Convex Functions , 2015, Foundations of Computational Mathematics.
[40] Michael I. Jordan,et al. Fast Distributionally Robust Learning with Variance Reduced Min-Max Optimization , 2021, AISTATS.
[41] Paul I. Barton,et al. Decision-dependent probabilities in stochastic programs with recourse , 2018, Comput. Manag. Sci..
[42] J. Andrew Bagnell,et al. Robust Supervised Learning , 2005, AAAI.
[43] Asuman E. Ozdaglar,et al. Why random reshuffling beats stochastic gradient descent , 2015, Mathematical Programming.
[44] Maryam Kamgarpour,et al. Learning to Play Sequential Games versus Unknown Opponents , 2020, NeurIPS.
[45] Charles Audet,et al. Derivative-Free and Blackbox Optimization , 2017 .
[46] L. Bottou. Curiously Fast Convergence of some Stochastic Gradient Descent Algorithms , 2009 .
[47] Ohad Shamir,et al. How Good is SGD with Random Shuffling? , 2019, COLT 2019.
[48] Niao He,et al. Global Convergence and Variance Reduction for a Class of Nonconvex-Nonconcave Minimax Problems , 2020, NeurIPS.
[49] Tony Jebara,et al. Frank-Wolfe Algorithms for Saddle Point Problems , 2016, AISTATS.
[50] Aleksander Madry,et al. Towards Deep Learning Models Resistant to Adversarial Attacks , 2017, ICLR.