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We consider learning under the constraint of local differential privacy (LDP).
“The Role of Interactivity in Local Differential Privacy”
Matthew Joseph, Jieming Mao, Seth Neel and Aaron Roth · 1904
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“Exponential Separations in Local Differential Privacy”, 2019
Matthew Joseph, Jieming Mao and Aaron Roth · 1907
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“On convergence proofs on perceptrons”
A. Novikoff · 1962
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“Theoretical foundations of the potential function method in pattern recognition learning.”
M.. Aizerman, E.. Braverman and L. Rozonoer · 1964
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“Randomized Response: A Survey Technique for Eliminating Evasive Answer Bias”
Stanley. Warner · 1965
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“A theory of the learnable”
L.. Valiant · 1984
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“Learning decision lists”
R. Rivest · 1987
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“A Training Algorithm for Optimal Margin Classifiers”
Bernhard. Boser, Isabelle Guyon and Vladimir Vapnik · 1992
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“Majority gates vs. general weighted threshold gates”
M. Goldmann, J. Hstad and A. Razborov · 1992
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“An introduction to computational learning theory”
M. Kearns and U. Vazirani · 1994
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“Learning with Restricted Focus of Attention”
Shai Ben-David and Eli Dichterman · 1998
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“Efficient noise-tolerant Learning from statistical queries”
M. Kearns · 1998
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“An Algorithmic Theory of Learning: Robust Concepts and Random Projection”
R. Arriaga and S. Vempala · 1999
Earlier work this paper cites.
“Estimating the Optimal Margins of Embeddings in Euclidean Half Spaces”
J“”urgen Forster, Niels Schmitt and Hans Simon · 2001
Earlier work this paper cites.
“Limitations of Learning Via Embeddings in Euclidean Half Spaces”
Shai Ben-David, Nadav Eiron and Hans Simon · 2002
Earlier work this paper cites.
“On using extended statistical queries to avoid membership queries”
N. Bshouty and V. Feldman · 2002
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“Limiting privacy breaches in privacy preserving data mining”
Alexandre. Evfimievski, Johannes Gehrke and Ramakrishnan Srikant · 2003
Earlier work this paper cites.
“A simple polynomial-time rescaling algorithm for solving linear programs”
J. Dunagan and S. Vempala · 2004
Earlier work this paper cites.
“Universal decentralized estimation in a bandwidth constrained sensor network”
Zhi-Quan Luo · 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.
“Bandwidth-constrained distributed estimation for wireless sensor networks-part I: Gaussian case”
Alejandro Ribeiro and Georgios Giannakis · 2006
Cited alongside, same era.
“Universal quantile estimation with feedback in the communication-constrained setting”
Ram Rajagopal, Martin Wainwright and Pravin Varaiya · 2006
Cited alongside, same era.
“On Computation and Communication with Small Bias”
H. Buhrman, N. Vereshchagin and R. de Wolf · 2007
Cited alongside, same era.
“Evolvability from Learning Algorithms”
V. Feldman · 2008
Cited alongside, same era.
“Halfspace Matrices”
Alexander. Sherstov · 2008
Cited alongside, same era.
“Learning Complexity vs Communication Complexity”
Nati Linial and Adi Shraibman · 2009
Cited alongside, same era.
Vitaly Feldman, Cristobal Guzman and Santosh Vempala · 2015
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“Minimax rates for memory-bounded sparse linear regression”
Jacob Steinhardt and John. Duchi · 2015
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“Interactive Fingerprinting Codes and the Hardness of Preventing False Discovery”
Thomas Steinke and Jonathan Ullman · 2015
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“Algorithmic stability for adaptive data analysis”
Raef Bassily, Kobbi Nissim, Adam. Smith, Thomas Steinke, Uri Stemmer and Jonathan Ullman · 2016
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“Memory, Communication, and Statistical Queries”
J. Steinhardt, G. Valiant and S. Wager · 2016
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“Evolvability” Earlier version in ECCC, 2006
L.. Valiant · 2009
Cited alongside, same era.
“Distribution-Independent Evolvability of Linear Threshold Functions”
V. Feldman · 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.
“A Close Look to Margin Complexity and Related Parameters”
M. Kallweit and H. Simon · 2011
Cited alongside, same era.
Vitaly Feldman, Elena Grigorescu, Lev Reyzin, Santosh Vempala and Ying Xiao · 2012
Cited alongside, same era.
“Local Privacy and Statistical Minimax Rates”
John. Duchi, Michael. Jordan and Martin. Wainwright · 2013
Cited alongside, same era.
Ananda Suresh, Felix Yu, H McMahan and Sanjiv Kumar · 2016
Later among the works it cites.
“The limitations of optimization from samples”
Eric Balkanski, Aviad Rubinstein and Yaron Singer · 2017
Later among the works it cites.
“Collecting Telemetry Data Privately”
Bolin Ding, Janardhan Kulkarni and Sergey Yekhanin · 2017
Later among the works it cites.
“Is Interaction Necessary for Distributed Private Learning?”
Adam. Smith, Abhradeep Thakurta and Jalaj Upadhyay · 2017
Later among the works it cites.
“Learning with Privacy at Scale”
Apple’s Differential Privacy Team · 2017
Later among the works it cites.
“Inference under Information Constraints I: Lower Bounds from Chi-Square Contraction”
Jayadev Acharya, Cl“’ement Canonne and Himanshu Tyagi · 2018
Closest in time.
Eric Balkanski and Yaron Singer · 2018
Closest in time.
“The adaptive complexity of maximizing a submodular function”
Eric Balkanski and Yaron Singer · 2018
Closest in time.
“Lower Bounds for Parallel and Randomized Convex Optimization”
Jelena Diakonikolas and Crist“’obal Guzm“’an · 2018
Closest in time.
“Minimax Bounds on Stochastic Batched Convex Optimization”
John. Duchi, Feng Ruan and Chulhee Yun · 2018
Closest in time.
“Graph Oracle Models, Lower Bounds, and Gaps for Parallel Stochastic Optimization”
Blake. Woodworth, Jialei Wang, Adam. Smith, Brendan McMahan and Nati Srebro · 2018
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“An Exponential Speedup in Parallel Running Time for Submodular Maximization without Loss in Approximation”
Eric Balkanski, Aviad Rubinstein and Yaron Singer · 2019
Closest in time.
“Open Problem: Is Margin Sufficient for Non-Interactive Private Distributed Learning?”
Amit Daniely and Vitaly Feldman · 2019
Closest in time.
“Lower Bounds for Locally Private Estimation via Communication Complexity”
John Duchi and Ryan Rogers · 2019
Closest in time.