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We study differentially private (DP) algorithms for stochastic non-convex optimization.
Gradient methods for minimizing functionals
Boris Teodorovich Polyak · 1963
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Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude
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Stochastic first-and zeroth-order methods for nonconvex stochastic programming
Saeed Ghadimi and Guanghui Lan · 2013
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Stochastic gradient descent with differentially private updates
Shuang Song, Kamalika Chaudhuri, and Anand D. Sarwate · 2013
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Differentially private empirical risk minimization: Efficient algorithms and tight error bounds
Raef Bassily, Adam Smith, and Abhradeep Thakurta · 2014
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The algorithmic foundations of differential privacy
Cynthia Dwork and Aaron Roth · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Cynthia Dwork, Vitaly Feldman, Moritz Hardt, Toni Pitassi, Omer Reingold, and Aaron Roth · 2015
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Cynthia Dwork, Vitaly Feldman, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Aaron Roth · 2015
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Preserving statistical validity in adaptive data analysis
Cynthia Dwork, Vitaly Feldman, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Aaron Leon Roth · 2015
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Deep learning with differential privacy
Martin Abadi, Andy Chu, Ian Goodfellow, H Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang · 2016
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Algorithmic stability for adaptive data analysis
On the convergence of adam and beyond
Sashank J. Reddi, Satyen Kale, and Sanjiv Kumar · 2018
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Adaptive methods for nonconvex optimization
Manzil Zaheer, Sashank Reddi, Devendra Sachan, Satyen Kale, and Sanjiv Kumar · 2018
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Stable gradient descent
Yingxue Zhou, Sheng Chen, and Arindam Banerjee · 2018
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Private stochastic convex optimization with optimal rates
Raef Bassily, Vitaly Feldman, Kunal Talwar, and Abhradeep Thakurta · 2019
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Deep learning with gaussian differential privacy
Zhiqi Bu, Jinshuo Dong, Qi Long, and Weijie J Su · 2019
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On the convergence of a class of adam-type algorithms for non-convex optimization
Xiangyi Chen, Sijia Liu, Ruoyu Sun, and Mingyi Hong · 2019
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Raef Bassily, Kobbi Nissim, Adam Smith, Thomas Steinke, Uri Stemmer, and Jonathan Ullman · 2016
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Deep residual learning for image recognition
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Song Mei, Yu Bai, and Andrea Montanari · 2016
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Di Wang, Minwei Ye, and Jinhui Xu · 2017
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The marginal value of adaptive gradient methods in machine learning
Ashia C Wilson, Rebecca Roelofs, Mitchell Stern, Nati Srebro, and Benjamin Recht · 2017
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Differentially private empirical risk minimization with smooth non-convex loss functions: A non-stationary view
Di Wang and Jinhui Xu · 2019
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Efficient privacy-preserving nonconvex optimization
Lingxiao Wang, Bargav Jayaraman, David Evans, and Quanquan Gu · 2019
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Subsampled renyi differential privacy and analytical moments accountant
Yu Xiang Wang, Borja Balle, and Shiva Prasad Kasiviswanathan · 2019
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Adagrad stepsizes: sharp convergence over nonconvex landscapes
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Private stochastic convex optimization: optimal rates in linear time
Vitaly Feldman, Tomer Koren, and Kunal Talwar · 2020
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A new analysis of differential privacy’s generalization guarantees
Christopher Jung, Katrina Ligett, Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi, and Moshe Shenfeld · 2020
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