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We study dynamic algorithms robust to adaptive input generated from sources with bounded capabilities, such as sparsity or limited interaction.
One-dimensional stable distributions
Vladimir M Zolotarev · 1986
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Michael W Mahoney · 2011
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Ilya Mironov, Moni Naor, and Gil Segev · 2011
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Anna C. Gilbert, Brett Hemenway, Martin J. Strauss, David P. Woodruff, and Mary Wootters · 2012
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Evasion attacks against machine learning at test time
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Low rank approximation and regression in input sparsity time
Kenneth L. Clarkson and David P. Woodruff · 2013
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Matrix computations
Gene H Golub and Charles F Van Loan · 2013
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Intriguing properties of neural networks
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David P Woodruff · 2014
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Alexandr Andoni, Piotr Indyk, Thijs Laarhoven, Ilya Razenshteyn, and Ludwig Schmidt · 2015
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Adversarial robustness of streaming algorithms through importance sampling
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Omri Ben-Eliezer, Rajesh Jayaram, David P. Woodruff, and Eylon Yogev · 2021
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Algorithmic stability for adaptive data analysis
Raef Bassily, Kobbi Nissim, Adam D. Smith, Thomas Steinke, Uri Stemmer, and Jonathan R. Ullman · 2021
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Separating adaptive streaming from oblivious streaming using the bounded storage model
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Handbook of discrete and computational geometry
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Tight bounds for adversarially robust streams and sliding windows via difference estimators
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The white-box adversarial data stream model
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Sub-quadratic algorithms for kernel matrices via kernel density estimation
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Dynamic algorithms against an adaptive adversary: generic constructions and lower bounds
Amos Beimel, Haim Kaplan, Yishay Mansour, Kobbi Nissim, Thatchaphol Saranurak, and Uri Stemmer · 2022
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Fully-dynamic graph sparsifiers against an adaptive adversary
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Amit Chakrabarti, Prantar Ghosh, and Manuel Stoeckl · 2022
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Uniform approximations for randomized hadamard transforms with applications
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A framework for adversarial streaming via differential privacy and difference estimators
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On differential privacy and adaptive data analysis with bounded space
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