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Model extraction attacks have renewed interest in the classic problem of learning neural networks from queries.
Distributional and L q L^{q} norm inequalities for polynomials over convex bodies in R n R^{n}
A. Carbery and J. Wright · 2001
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Kernel methods for deep learning
Youngmin Cho and Lawrence K. Saul · 2009
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Theoretical foundations of active learning
Steve Hanneke · 2009
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Beating the perils of non-convexity: Guaranteed training of neural networks using tensor methods
Majid Janzamin, Hanie Sedghi, and Anima Anandkumar · 2015
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Stealing machine learning models via prediction apis
Florian Tramèr, Fan Zhang 0022, Ari Juels, Michael K. Reiter, and Thomas Ristenpart · 2016
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Practical black-box attacks against machine learning
Nicolas Papernot, Patrick D. McDaniel, Ian J. Goodfellow, Somesh Jha, Z. Berkay Celik, and Ananthram Swami · 2017
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Recovery guarantees for one-hidden-layer neural networks
Kai Zhong, Zhao Song, Prateek Jain, Peter L Bartlett, and Inderjit S Dhillon · 2017
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Learning two-layer neural networks with symmetric inputs
Rong Ge, Rohith Kuditipudi, Zhize Li, and Xiang Wang · 2018
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Learning one convolutional layer with overlapping patches
Surbhi Goel, Adam R. Klivans, and Raghu Meka · 2018
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Learning one-hidden-layer neural networks with landscape design
Rong Ge, Jason D Lee, and Tengyu Ma · 2018
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High-dimensional probability: An introduction with applications in data science
Roman Vershynin · 2018
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Learning two layer rectified neural networks in polynomial time
Ainesh Bakshi, Rajesh Jayaram, and David P Woodruff · 2019
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Model reconstruction from model explanations
Smitha Milli, Ludwig Schmidt, Anca D. Dragan, and Moritz Hardt · 2019
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Learning one-hidden-layer relu networks via gradient descent
Xiao Zhang, Yaodong Yu, Lingxiao Wang, and Quanquan Gu · 2019
Cited alongside, same era.
Cryptanalytic extraction of neural network models
Nicholas Carlini, Matthew Jagielski, and Ilya Mironov · 2020
Cited alongside, same era.
Hardness of learning neural networks with natural weights
Amit Daniely and Gal Vardi · 2020
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Superpolynomial lower bounds for learning one-layer neural networks using gradient descent
Surbhi Goel, Aravind Gollakota, Zhihan Jin, Sushrut Karmalkar, and Adam Klivans · 2020
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High accuracy and high fidelity extraction of neural networks
Matthew Jagielski, Nicholas Carlini, David Berthelot, Alex Kurakin, and Nicolas Papernot · 2020
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Span recovery for deep neural networks with applications to input obfuscation, 2020
Rajesh Jayaram, David P. Woodruff, and Qiuyi Zhang · 2020
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Learning over-parametrized two-layer relu neural networks beyond ntk
Yuanzhi Li, Tengyu Ma, and Hongyang R. Zhang · 2020
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Learning deep relu networks is fixed parameter tractable
Sitan Chen, Adam Klivans, and Raghu Meka · 2020
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Learning polynomials of few relevant dimensions
Sitan Chen and Raghu Meka · 2020
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Algorithms and sq lower bounds for pac learning one-hidden-layer relu networks
Ilias Diakonikolas, Daniel M Kane, Vasilis Kontonis, and Nikos Zarifis · 2020
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Reverse-engineering deep relu networks
David Rolnick and Konrad P. Kording · 2020
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Efficient algorithms for learning depth-2 neural networks with general relu activations
Pranjal Awasthi, Alex Tang, and Aravindan Vijayaraghavan · 2021
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An exact poly-time membership-queries algorithm for extraction a three-layer relu network
Amit Daniely and Elad Granot · 2021
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From local pseudorandom generators to hardness of learning
Amit Daniely and Gal Vardi · 2021
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