Fetching the paper…
Reading the bibliography…
Given the computational cost and technical expertise required to train machine learning models, users may delegate the task of learning to a service provider.
An optimum character recognition system using decision functions
Chi-Keung Chow · 1957
Earlier work this paper cites.
Perceptrons
Marvin Minsky and Seymour Papert · 1969
Earlier work this paper cites.
Coin flipping by telephone
Manuel Blum · 1981
Earlier work this paper cites.
A theory of the learnable
Leslie G. Valiant · 1984
Earlier work this paper cites.
The knowledge complexity of interactive proof-systems (extended abstract)
Shafi Goldwasser, Silvio Micali, and Charles Rackoff · 1985
Earlier work this paper cites.
Learning complicated concepts reliably and usefully
Ronald L Rivest and Robert H Sloan · 1988
Earlier work this paper cites.
Universal one-way hash functions and their cryptographic applications
Moni Naor and Moti Yung · 1989
Earlier work this paper cites.
Reliable and useful learning with uniform probability distributions
Jyrki Kivinen · 1990
Earlier work this paper cites.
One-way functions are necessary and sufficient for secure signatures
John Rompel · 1990
Earlier work this paper cites.
Non-malleable cryptography (extended abstract)
Danny Dolev, Cynthia Dwork, and Moni Naor · 1991
Earlier work this paper cites.
Concentration of measure and isoperimetric inequalities in product spaces
Michel Talagrand · 1995
Earlier work this paper cites.
Kleptography: Using cryptography against cryptography
Adam L. Young and Moti Yung · 1997
Earlier work this paper cites.
On the (im) possibility of obfuscating programs
Boaz Barak, Oded Goldreich, Rusell Impagliazzo, Steven Rudich, Amit Sahai, Salil Vadhan, and Ke Yang · 2001
Earlier work this paper cites.
On lattices, learning with errors, random linear codes, and cryptography
Oded Regev · 2005
Earlier work this paper cites.
Cryptographic hardness for learning intersections of halfspaces
Adam R. Klivans and Alexander A. Sherstov · 2006
Earlier work this paper cites.
On best-possible obfuscation
Shafi Goldwasser and Guy N Rothblum · 2007
Earlier work this paper cites.
Random features for large-scale kernel machines
Ali Rahimi and Benjamin Recht · 2007
Earlier work this paper cites.
On the possibility of a back door in the nist sp800-90 dual ec prng, 2007
Dan Shumow and Niels Ferguson · 2007
Earlier work this paper cites.
Trapdoors for hard lattices and new cryptographic constructions
Craig Gentry, Chris Peikert, and Vinod Vaikuntanathan · 2008
Earlier work this paper cites.
Uniform approximation of functions with random bases
Ali Rahimi and Benjamin Recht · 2008
Earlier work this paper cites.
Weighted sums of random kitchen sinks: replacing minimization with randomization in learning
Ali Rahimi and Benjamin Recht · 2008
Cited alongside, same era.
Bonsai trees, or how to delegate a lattice basis
David Cash, Dennis Hofheinz, Eike Kiltz, and Chris Peikert · 2010
Cited alongside, same era.
Reliable agnostic learning
Adam Tauman Kalai, Varun Kanade, and Yishay Mansour · 2012
Cited alongside, same era.
Complexity theoretic lower bounds for sparse principal component detection
Quentin Berthet and Philippe Rigollet · 2013
Cited alongside, same era.
Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2013
Cited alongside, same era.
Delegating computation: interactive proofs for muggles
Shafi Goldwasser, Yael Tauman Kalai, and Guy N Rothblum · 2015
Cited alongside, same era.
Adversarial examples are not bugs, they are features
Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Logan Engstrom, Brandon Tran, and Aleksander Madry · 2019
Later among the works it cites.
Constant-round interactive proofs for delegating computation
Omer Reingold, Guy N Rothblum, and Ron D Rothblum · 2019
Later among the works it cites.
Provably robust deep learning via adversarially trained smoothed classifiers
Hadi Salman, Jerry Li, Ilya Razenshteyn, Pengchuan Zhang, Huan Zhang, Sebastien Bubeck, and Greg Yang · 2019
Later among the works it cites.
Adversarial training for free!
Ali Shafahi, Mahyar Najibi, Amin Ghiasi, Zheng Xu, John Dickerson, Christoph Studer, Larry S Davis, Gavin Taylor, and Tom Goldstein · 2019
Later among the works it cites.
A simple explanation for the existence of adversarial examples with small hamming distance
Adi Shamir, Itay Safran, Eyal Ronen, and Orr Dunkelman · 2019
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
A decade of lattice cryptography
Chris Peikert · 2016
Cited alongside, same era.
Targeted backdoor attacks on deep learning systems using data poisoning
Xinyun Chen, Chang Liu, Bo Li, Kimberly Lu, and Dawn Song · 2017
Cited alongside, same era.
Statistical query lower bounds for robust estimation of high-dimensional gaussians and gaussian mixtures
Ilias Diakonikolas, Daniel M Kane, and Alistair Stewart · 2017
Cited alongside, same era.
Turning your weakness into a strength: Watermarking deep neural networks by backdooring
Yossi Adi, Carsten Baum, Moustapha Cissé, Benny Pinkas, and Joseph Keshet · 2018
Cited alongside, same era.
Adversarial risk and robustness: General definitions and implications for the uniform distribution
Dimitrios I. Diochnos, Saeed Mahloujifar, and Mohammad Mahmoody · 2018
Cited alongside, same era.
Certified defenses against adversarial examples
Aditi Raghunathan, Jacob Steinhardt, and Percy Liang · 2018
Cited alongside, same era.
Later among the works it cites.
On the robustness of the backdoor-based watermarking in deep neural networks
Masoumeh Shafieinejad, Jiaqi Wang, Nils Lukas, and Florian Kerschbaum · 2019
Later among the works it cites.
Neural cleanse: Identifying and mitigating backdoor attacks in neural networks
Bolun Wang, Yuanshun Yao, Shawn Shan, Huiying Li, Bimal Viswanath, Haitao Zheng, and Ben Y. Zhao · 2019
Later among the works it cites.
Detection as regression: Certified object detection with median smoothing
Ping-Yeh Chiang, Michael J. Curry, Ahmed Abdelkader, Aounon Kumar, John Dickerson, and Tom Goldstein · 2020
Later among the works it cites.
Adversarially robust learning could leverage computational hardness
Sanjam Garg, Somesh Jha, Saeed Mahloujifar, and Mahmoody Mohammad · 2020
Later among the works it cites.
Beyond perturbations: Learning guarantees with arbitrary adversarial test examples
Shafi Goldwasser, Adam Tauman Kalai, Yael Tauman Kalai, and Omar Montasser · 2020
Later among the works it cites.
Continuous LWE
Joan Bruna, Oded Regev, Min Jae Song, and Yi Tang · 2021
Later among the works it cites.
Adversarial robustness guarantees for random deep neural networks
Giacomo De Palma, Bobak Kiani, and Seth Lloyd · 2021
Later among the works it cites.
Interactive proofs for verifying machine learning
Shafi Goldwasser, Guy N. Rothblum, Jonathan Shafer, and Amir Yehudayoff · 2021
Later among the works it cites.
Handcrafted backdoors in deep neural networks
Sanghyun Hong, Nicholas Carlini, and Alexey Kurakin · 2021
Later among the works it cites.
Spectre: defending against backdoor attacks using robust statistics
Jonathan Hayase, Weihao Kong, Raghav Somani, and Sewoong Oh · 2021
Later among the works it cites.
Indistinguishability obfuscation from well-founded assumptions
Aayush Jain, Huijia Lin, and Amit Sahai · 2021
Later among the works it cites.
Efficient learning with arbitrary covariate shift
Adam Tauman Kalai and Varun Kanade · 2021
Later among the works it cites.
Spoofing generalization: When can’t you trust proprietary models?
Ankur Moitra, Elchanan Mossel, and Colin Sandon · 2021
Later among the works it cites.
The dimpled manifold model of adversarial examples in machine learning
Adi Shamir, Odelia Melamed, and Oriel BenShmuel · 2021
Later among the works it cites.
Personal communication, 2024
Miranda Christ and Sam Gunn · 2024
Closest in time.