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We analytically investigate how over-parameterization of models in randomized machine learning algorithms impacts the information leakage about their training data.
Sur la théorie du mouvement brownien [on the theory of brownian motion]
DS Lemons, A Gythiel, and Paul Langevin’s · 1908
Earlier work this paper cites.
Sull’integrazione per parti
Leonida Tonelli · 1909
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Efficient backprop
Yann LeCun, Léon Bottou, Genevieve B Orr, and Klaus-Robert Müller · 2002
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John Wiley & Sons, Ltd, 2005
Entropy, Relative Entropy, and Mutual Information , chapter 2, pages 13–55 · 2005
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Calibrating noise to sensitivity in private data analysis
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Understanding the difficulty of training deep feedforward neural networks
Xavier Glorot and Yoshua Bengio · 2010
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Stochastic gradient descent for non-smooth optimization: Convergence results and optimal averaging schemes
Ohad Shamir and Tong Zhang · 2013
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Rina Foygel Barber and John C Duchi · 2014
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Private empirical risk minimization: Efficient algorithms and tight error bounds
Raef Bassily, Adam Smith, and Abhradeep Thakurta · 2014
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Cynthia Dwork, Aaron Roth, et al · 2014
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Rényi divergence and kullback-leibler divergence
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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
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On-average kl-privacy and its equivalence to generalization for max-entropy mechanisms
Yu-Xiang Wang, Jing Lei, and Stephen E Fienberg · 2016
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Membership inference attacks against machine learning models
Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov · 2017
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Privacy amplification by subsampling: Tight analyses via couplings and divergences
Borja Balle, Gilles Barthe, and Marco Gaboardi · 2018
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Gradient descent provably optimizes over-parameterized neural networks
Simon S. Du, Xiyu Zhai, Barnabas Poczos, and Aarti Singh · 2018
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Arthur Jacot, Franck Gabriel, and Clément Hongler · 2018
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Fine-grained analysis of optimization and generalization for overparameterized two-layer neural networks
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Private stochastic convex optimization with optimal rates
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Yuan Cao and Quanquan Gu · 2019
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On lazy training in differentiable programming
Phase diagram for two-layer relu neural networks at infinite-width limit
Tao Luo, Zhi-Qin John Xu, Zheng Ma, and Yaoyu Zhang · 2021
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Tight bounds on the smallest eigenvalue of the neural tangent kernel for deep relu networks
Quynh Nguyen, Marco Mondelli, and Guido F Montufar · 2021
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What can linearized neural networks actually say about generalization?
Guillermo Ortiz-Jiménez, Seyed-Mohsen Moosavi-Dezfooli, and Pascal Frossard · 2021
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Evading the curse of dimensionality in unconstrained private glms
Shuang Song, Thomas Steinke, Om Thakkar, and Abhradeep Thakurta · 2021
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Covariate shift in high-dimensional random feature regression
Nilesh Tripuraneni, Ben Adlam, and Jeffrey Pennington · 2021
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Gradient descent finds global minima of deep neural networks
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Wide neural networks of any depth evolve as linear models under gradient descent
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Rapid convergence of the unadjusted langevin algorithm: Isoperimetry suffices
Santosh Vempala and Andre Wibisono · 2019
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Faster differentially private samplers via rényi divergence analysis of discretized langevin mcmc
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Neural kernels without tangents
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Gradient descent optimizes over-parameterized deep relu networks
Difan Zou, Yuan Cao, Dongruo Zhou, and Quanquan Gu · 2020
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Reconstructing training data with informed adversaries
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Raef Bassily, Mehryar Mohri, and Ananda Theertha Suresh · 2022
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Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions
Sitan Chen, Sinho Chewi, Jerry Li, Yuanzhi Li, Adil Salim, and Anru R Zhang · 2022
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When is the convergence time of langevin algorithms dimension independent? a composite optimization viewpoint
Yoav Freund, Yi-An Ma, and Tong Zhang · 2022
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Langevin diffusion: An almost universal algorithm for private euclidean (convex) optimization
Arun Ganesh, Abhradeep Thakurta, and Jalaj Upadhyay · 2022
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Reconstructing training data from trained neural networks
Niv Haim, Gal Vardi, Gilad Yehudai, Ohad Shamir, and Michal Irani · 2022
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When does differentially private learning not suffer in high dimensions?
Xuechen Li, Daogao Liu, Tatsunori B Hashimoto, Huseyin A Inan, Janardhan Kulkarni, Yin-Tat Lee, and Abhradeep Guha Thakurta · 2022
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Optimal membership inference bounds for adaptive composition of sampled gaussian mechanisms
Saeed Mahloujifar, Alexandre Sablayrolles, Graham Cormode, and Somesh Jha · 2022
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Parameters or privacy: A provable tradeoff between overparameterization and membership inference
Jasper Tan, Blake Mason, Hamid Javadi, and Richard Baraniuk · 2022
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Robustness in deep learning: The good (width), the bad (depth), and the ugly (initialization)
Zhenyu Zhu, Fanghui Liu, Grigorios G Chrysos, and Volkan Cevher · 2022
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A universal law of robustness via isoperimetry
Sébastien Bubeck and Mark Sellke · 2023
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A blessing of dimensionality in membership inference through regularization
Jasper Tan, Daniel LeJeune, Blake Mason, Hamid Javadi, and Richard G Baraniuk · 2023
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