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Statistical machine learning models trained with stochastic gradient algorithms are increasingly being deployed in critical scientific applications.
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Nathan L Kleinman, James C Spall, and Daniel Q Naiman · 1999
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Jürgen Dippon · 2003
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Optimization by direct search: New perspectives on some classical and modern methods
Tamara G Kolda, Robert Michael Lewis, and Virginia Torczon · 2003
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Introduction to stochastic search and optimization: Estimation, simulation, and control
James C Spall · 2005
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A companion for the Kiefer–Wolfowitz–Blum stochastic approximation algorithm
Abdelkader Mokkadem and Mariane Pelletier · 2007
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Introduction to derivative-free optimization
Andrew R Conn, Katya Scheinberg, and Luis N Vicente · 2009
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Optimal algorithms for online convex optimization with multi-point bandit feedback
Alekh Agarwal, Ofer Dekel, and Lin Xiao · 2010
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Trajectory averaging for stochastic approximation MCMC algorithms
Faming Liang · 2010
The implicit bias of gradient descent on separable data
Daniel Soudry, Elad Hoffer, Mor Shpigel Nacson, Suriya Gunasekar, and Nathan Srebro · 2018
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Weijie Su and Yuancheng Zhu · 2018
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Stochastic zeroth-order optimization in high dimensions
Yining Wang, Simon Du, Sivaraman Balakrishnan, and Aarti Singh · 2018
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A generic approach for accelerating stochastic zeroth-order convex optimization
Xiaotian Yu, Irwin King, Michael R Lyu, and Tianbao Yang · 2018
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Normal approximation for stochastic gradient descent via non-asymptotic rates of martingale CLT
Andreas Anastasiou, Krishnakumar Balasubramanian, and Murat A Erdogdu · 2019
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The Yahoo! music dataset and KDD-cup’11
Gideon Dror, Noam Koenigstein, Yehuda Koren, and Markus Weimer · 2012
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Query complexity of derivative-free optimization
Kevin G Jamieson, Robert Nowak, and Ben Recht · 2012
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Practical bayesian optimization of machine learning algorithms
Jasper Snoek, Hugo Larochelle, and Ryan P Adams · 2012
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Algorithms for minimization without derivatives
Richard P Brent · 2013
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Stochastic first-and zeroth-order methods for nonconvex stochastic programming
Saeed Ghadimi and Guanghui Lan · 2013
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On the complexity of bandit and derivative-free stochastic convex optimization
Ohad Shamir · 2013
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Stochastic (approximate) proximal point methods: Convergence, optimality, and adaptivity
Hilal Asi and John C Duchi · 2019
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ZO-AdaMM: Zeroth-order adaptive momentum method for black-box optimization
Xiangyi Chen, Sijia Liu, Kaidi Xu, Xingguo Li, Xue Lin, Mingyi Hong, and David Cox · 2019
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Sign-OPT: A Query-Efficient Hard-label Adversarial Attack
Minhao Cheng, Simranjit Singh, Patrick H Chen, Pin-Yu Chen, Sijia Liu, and Cho-Jui Hsieh · 2019
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Efficient decision-based black-box adversarial attacks on face recognition
Yinpeng Dong, Hang Su, Baoyuan Wu, Zhifeng Li, Wei Liu, Tong Zhang, and Jun Zhu · 2019
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Gradientless descent: High-dimensional zeroth-order optimization
Daniel Golovin, John Karro, Greg Kochanski, Chansoo Lee, Xingyou Song, and Qiuyi Zhang · 2019
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Faster gradient-free proximal stochastic methods for nonconvex nonsmooth optimization
Feihu Huang, Bin Gu, Zhouyuan Huo, Songcan Chen, and Heng Huang · 2019
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The implicit bias of gradient descent on nonseparable data
Ziwei Ji and Matus Telgarsky · 2019
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Improved zeroth-order variance reduced algorithms and analysis for nonconvex optimization
Kaiyi Ji, Zhe Wang, Yi Zhou, and Yingbin Liang · 2019
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NATTACK: Learning the distributions of adversarial examples for an improved black-box attack on deep neural networks
Yandong Li, Lijun Li, Liqiang Wang, Tong Zhang, and Boqing Gong · 2019
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Derivative-free optimization methods
Jeffrey Larson, Matt Menickelly, and Stefan M Wild · 2019
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Towards gradient free and projection free stochastic optimization
Anit Kumar Sahu, Manzil Zaheer, and Soummya Kar · 2019
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P. Zhao, S. Liu, P-Y. Chen, N. Hoang, K. Xu, B. Kailkhura, and X. Lin · 2019
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Statistical inference for model parameters in stochastic gradient descent
Xi Chen, Jason D Lee, Xin T Tong, and Yichen Zhang · 2020
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Zeroth-order regularized optimization (ZORO): Approximately sparse gradients and adaptive sampling
HanQin Cai, Daniel Mckenzie, Wotao Yin, and Zhenliang Zhang · 2020
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Provably robust blackbox optimization for reinforcement learning
Krzysztof Choromanski, Aldo Pacchiano, Jack Parker-Holder, Yunhao Tang, Deepali Jain, Yuxiang Yang, Atil Iscen, Jasmine Hsu, and Vikas Sindhwani · 2020
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A Frank-Wolfe framework for efficient and effective adversarial attacks
Jinghui Chen, Dongruo Zhou, Jinfeng Yi, and Quanquan Gu · 2020
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Bridging the gap between constant step size stochastic gradient descent and Markov chains
Aymeric Dieuleveut, Alain Durmus, and Francis Bach · 2020
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Asymptotic optimality in stochastic optimization
John Duchi and Feng Ruan · 2020
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Can stochastic zeroth-order Frank-Wolfe method converge faster for non-convex problems?
Hongchang Gao and Heng Huang · 2020
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Accelerated stochastic gradient-free and projection-free methods
Feihu Huang, Lue Tao, and Songcan Chen · 2020
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Stochastic zeroth-order riemannian derivative estimation and optimization
Jiaxiang Li, Krishnakumar Balasubramanian, and Shiqian Ma · 2020
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A primer on zeroth-order optimization in signal processing and machine learning
Sijia Liu, Pin-Yu Chen, Bhavya Kailkhura, Gaoyuan Zhang, Alfred O Hero III, and Pramod K Varshney · 2020
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An ADMM based framework for AutoML pipeline configuration
Sijia Liu, Parikshit Ram, Deepak Vijaykeerthy, Djallel Bouneffouf, Gregory Bramble, Horst Samulowitz, Dakuo Wang, Andrew Conn, and Alexander Gray · 2020
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An analysis of constant step size sgd in the non-convex regime: Asymptotic normality and bias
Lu Yu, Krishnakumar Balasubramanian, Stanislav Volgushev, and Murat A Erdogdu · 2020
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A fully online approach for covariance matrices estimation of stochastic gradient descent solutions
Wanrong Zhu, Xi Chen, and Wei Biao Wu · 2020
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Zeroth-order nonconvex stochastic optimization: Handling constraints, high-dimensionality and saddle-points
Krishnakumar Balasubramanian and Saeed Ghadimi · 2021
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