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We study learning algorithms that are restricted to using a small amount of information from their input sample.
Bounds for the exponent of the probability of error for a semicontinuous memoryless channel
E. A. Arutyunyan · 1968
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Relating data compression and learnability
N. Littlestone and M. Warmuth · 1986
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Occam’s razor
A. Blumer, A. Ehrenfeucht, D. Haussler, and M. Warmuth · 1987
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Sphere packing numbers for subsets of the boolean n n -cube with bounded Vapnik-Chervonenkis dimension
David Haussler · 1995
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PAC-bayesian model averaging
D. McAllester · 2003
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T. Cover and J. A. Thomas. Elements of information theory
2006
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Calibrating noise to sensitivity in private data analysis
C. Dwork, F. McSherry, K. Nissim, and A. Smith · 2006
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What can we learn privately?
S. Kasiviswanathan, H. K. Lee, K. Nissim, S. Raskhodnikova, and A. Smith · 2008
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Bounds on the sample complexity for private learning and private data release
A. Beimel, S. P. Kasiviswanathan , and K. Nissim · 2010
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The Communication Complexity of Correlation
P. Harsha, R. Jain, D. McAllester, and J. Radhakrishnan · 2010
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The limits of two-party differential privacy
A. McGregor, I. Mironov, T. Pitassi, O. Reingold, K. Talwar, and S. Vadhan · 2010
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Lower bounds in differential privacy
A. De · 2012
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Characterizing the sample complexity of private learners
A. Beimel, K. Nissim, and U. Stemmer · 2013
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Private empirical risk minimization: Efficient algorithms and tight error bounds
R. Bassily, A. Smith, and A. Thakurta · 2014
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Public vs private coin in bounded-round information
M. Braverman and A. Garg · 2014
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Differentially private release and learning of threshold functions
M. Bun, K. Nissim, U. Stemmer, and S. P. Vadhan · 2015
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Generalization in adaptive data analysis and holdout reuse
C. Dwork, V. Feldman, M. Hardt, T. Pitassi, O. Reingold, and A. Roth · 2015
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Preserving statistical validity in adaptive data analysis
C. Dwork, V. Feldman, M. Hardt, T. Pitassi, O. Reingold, and Aaron Roth · 2015
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Algorithmic stability for adaptive data analysis
R. Bassily, K. Nissim, A. Smith, U. Stemmer, and J. Ullman · 2016
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Sample compression schemes for VC classes
S. Moran and A. Yehudayoff · 2016
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Max-information, differential privacy, and post-selection hypothesis testing
R. Rogers, A. Roth, A. Smith, and O. Thakkar · 2016
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The algorithmic foundations of differential privacy
C. Dwork and A. Roth · 2014
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Sample complexity bounds on differentially private learning via communication complexity
V. Feldman and D. Xiao · 2014
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Understanding machine learning: From theory to algorithms
S. Shalev-Shwartz and S. Ben-David · 2014
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Controlling bias in adaptive data analysis using information theory
D. Russo and J. Zhou · 2016
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Information-theoretic analysis of generalization capability of learning algorithms
M. Raginsky and A. Xu · 2017
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The complexity of differential privacy
S. Vadhan · 2017
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