Fetching the paper…
Reading the bibliography…
We study binary classification algorithms for which the prediction on any point is not too sensitive to individual examples in the dataset.
“High probability generalization bounds for uniformly stable algorithms with nearly optimal rate”
Vitaly Feldman and Jan Vondr“’ak · 1902
Earlier work this paper cites.
“Does Learning Require Memorization? A Short Tale about a Long Tail”
Vitaly Feldman · 1906
Earlier work this paper cites.
“A Finite Sample Distribution-Free Performance Bound for Local Discrimination Rules”
W.. Rogers and T.. Wagner · 1978
Earlier work this paper cites.
“Distribution-free inequalities for the deleted and holdout error estimates”
Luc Devroye and Terry. Wagner · 1979
Earlier work this paper cites.
“A theory of the learnable”
L.. Valiant · 1984
Earlier work this paper cites.
“Decision theoretic generalizations of the PAC model for neural net and other learning applications”
D. Haussler · 1992
Earlier work this paper cites.
“Toward Efficient Agnostic Learning.”
M. Kearns, R. Schapire and L. Sellie · 1994
Earlier work this paper cites.
“Bagging predictors”
Leo Breiman · 1996
Earlier work this paper cites.
“Stability and generalization”
Olivier Bousquet and Andr“’e Elisseeff · 2002
Earlier work this paper cites.
“Stability of Randomized Learning Algorithms”
Andr“’e Elisseeff, Theodoros Evgeniou and Massimiliano Pontil · 2005
Earlier work this paper cites.
“Calibrating noise to sensitivity in private data analysis”
C. Dwork, F. McSherry, K. Nissim and A. Smith · 2006
Earlier work this paper cites.
“Mechanism Design via Differential Privacy”
Frank McSherry and Kunal Talwar · 2007
Earlier work this paper cites.
“Learnability, stability and uniform convergence”
Shai Shalev-Shwartz, Ohad Shamir, Nathan Srebro and Karthik Sridharan · 2010
Cited alongside, same era.
“Sample Complexity Bounds for Differentially Private Learning”
Kamalika Chaudhuri and Daniel Hsu · 2011
Cited alongside, same era.
“What Can We Learn Privately?”
Shiva Kasiviswanathan, Homin. Lee, Kobbi Nissim, Sofya Raskhodnikova and Adam Smith · 2011
Cited alongside, same era.
“Poisoning Attacks Against Support Vector Machines”
Battista Biggio, Blaine Nelson and Pavel Laskov · 2012
Cited alongside, same era.
“Characterizing the sample complexity of private learners”
Amos Beimel, Kobbi Nissim and Uri Stemmer · 2013
Cited alongside, same era.
“Private Learning and Sanitization: Pure vs. Approximate Differential Privacy”
Amos Beimel, Kobbi Nissim and Uri Stemmer · 2013
Cited alongside, same era.
“Membership Inference Attacks Against Machine Learning Models”
Reza Shokri, Marco Stronati, Congzheng Song and Vitaly Shmatikov · 2017
Later among the works it cites.
“Subgaussian Tail Bounds via Stability Arguments”
Thomas Steinke and Jonathan Ullman · 2017
Later among the works it cites.
“Model-Agnostic Private Learning via Stability”
Raef Bassily, Om Thakkar and Abhradeep Thakurta · 2018
Later among the works it cites.
“Privacy-preserving Prediction” Extended abstract in COLT 2018
Cynthia Dwork and Vitaly Feldman · 2018
Later among the works it cites.
“Personal communication”, 2018
Kobbi Nissim and Uri Stemmer · 2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
“Understanding machine learning: From theory to algorithms”
Shai Shalev-Shwartz and Shai Ben-David · 2014
Cited alongside, same era.
“Learning privately with labeled and unlabeled examples”
Amos Beimel, Kobbi Nissim and Uri Stemmer · 2015
Cited alongside, same era.
“Sample Complexity Bounds on Differentially Private Learning via Communication Complexity”
Vitaly Feldman and David Xiao · 2015
Cited alongside, same era.
“Learning privately from multiparty data”
Jihun Hamm, Yingjun Cao and Mikhail Belkin · 2016
Cited alongside, same era.
“Towards Measuring Membership Privacy”
Yunhui Long, Vincent Bindschaedler and Carl. Gunter · 2017
Cited alongside, same era.
“Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data”
Nicolas Papernot, Mart“’n Abadi, “’Ulfar Erlingsson, Ian. Goodfellow and Kunal Talwar · 2017
Cited alongside, same era.
“Scalable Private Learning with PATE”
Nicolas Papernot, Shuang Song, Ilya Mironov, Ananth Raghunathan, Kunal Talwar and Ulfar Erlingsson · 2018
Later among the works it cites.
“Towards demystifying membership inference attacks”
Stacey Truex, Ling Liu, Mehmet Gursoy, Lei Yu and Wenqi Wei · 2018
Later among the works it cites.
“Private PAC learning implies finite Littlestone dimension”
Noga Alon, Roi Livni, Maryanthe Malliaris and Shay Moran · 2019
Closest in time.
“Limits of Private Learning with Access to Public Data”
Raef Bassily, Shay Moran and Noga Alon · 2019
Closest in time.
“The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networks”
Nicholas Carlini, Chang Liu, Jernej Kos, “’Ulfar Erlingsson and Dawn Song · 2019
Closest in time.
“Private Query Release Assisted by Public Data”
Raef Bassily, Albert Cheu, Shay Moran, Aleksandar Nikolov, Jonathan Ullman and Zhiwei Wu · 2020
Closest in time.
“Privately Answering Classification Queries in the Agnostic PAC Model”
Anupama Nandi and Raef Bassily · 2020
Closest in time.