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This paper studies the relationship between generalization and privacy preservation in iterative learning algorithms by two sequential steps.
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Rademacher and gaussian complexities: Risk bounds and structural results
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Stability and generalization
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Differential privacy
Cynthia Dwork · 2006
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Cynthia Dwork, Guy N Rothblum, and Salil Vadhan · 2010
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Sparse algorithms are not stable: A no-free-lunch theorem
Huan Xu, Constantine Caramanis, and Shie Mannor · 2011
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A framework for mining signatures from event sequences and its applications in healthcare data
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Concentration inequalities: A nonasymptotic theory of independence
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The algorithmic foundations of differential privacy
Cynthia Dwork and Aaron Roth · 2014
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Peter Kairouz, Sewoong Oh, and Pramod Viswanath · 2015
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A complete recipe for stochastic gradient mcmc
Yi-An Ma, Tianqi Chen, and Emily Fox · 2015
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On the generalization properties of differential privacy
Kobbi Nissim and Uri Stemmer · 2015
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Differential privacy and generalization: Sharper bounds with applications
Luca Oneto, Sandro Ridella, and Davide Anguita · 2017
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Non-convex learning via stochastic gradient langevin dynamics: a nonasymptotic analysis
Maxim Raginsky, Alexander Rakhlin, and Matus Telgarsky · 2017
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Deep learning with long short-term memory networks for financial market predictions
Thomas Fischer and Christopher Krauss · 2018
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Size-independent sample complexity of neural networks
Noah Golowich, Alexander Rakhlin, and Ohad Shamir · 2018
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Foundations of machine learning
Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar · 2018
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Generalization bounds of sgld for non-convex learning: Two theoretical viewpoints
Wenlong Mou, Liwei Wang, Xiyu Zhai, and Kai Zheng · 2018
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Privacy for free: Posterior sampling and stochastic gradient monte carlo
Yu-Xiang Wang, Stephen Fienberg, and Alex Smola · 2015
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Deep learning with differential privacy
Martin Abadi, Andy Chu, Ian Goodfellow, H Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang · 2016
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Concentrated differential privacy: Simplifications, extensions, and lower bounds
Mark Bun and Thomas Steinke · 2016
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Differential privacy as a mutual information constraint
Paul Cuff and Lanqing Yu · 2016
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Concentrated differential privacy
Cynthia Dwork and Guy N Rothblum · 2016
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Train faster, generalize better: Stability of stochastic gradient descent
Moritz Hardt, Ben Recht, and Yoram Singer · 2016
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A pac-bayesian approach to spectrally-normalized margin bounds for neural networks
Behnam Neyshabur, Srinadh Bhojanapalli, David McAllester, and Nathan Srebro · 2018
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Ankit Pensia, Varun Jog, and Po-Ling Loh · 2018
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Bayesian neural networks with weight sharing using dirichlet processes
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Advances in variational inference
Cheng Zhang, Judith Bütepage, Hedvig Kjellström, and Stephan Mandt · 2018
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Small nonlinearities in activation functions create bad local minima in neural networks
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Piecewise linear activations substantially shape the loss surfaces of neural networks
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