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Differential privacy is a mathematical framework for privacy-preserving data analysis.
On Bayesian methods for seeking the extremum
J Močkus · 1975
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Scaling up the accuracy of Naïve-Bayes classifiers: a decision-tree hybrid
Ron Kohavi · 1996
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Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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Warped Gaussian processes
Edward Snelson, Zoubin Ghahramani, and Carl E Rasmussen · 2004
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Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Carl Edward Rasmussen and Christopher K. I. Williams · 2005
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Differential privacy
Cynthia Dwork · 2006
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Calibrating noise to sensitivity in private data analysis
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith · 2006
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On the complexity of differentially private data release: efficient algorithms and hardness results
Cynthia Dwork, Moni Naor, Omer Reingold, Guy N Rothblum, and Salil Vadhan · 2009
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Large-scale machine learning with stochastic gradient descent
Léon Bottou · 2010
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Convergence rates of efficient global optimization algorithms
Adam D Bull · 2011
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Kernels for vector-valued functions: A review
Mauricio A. Álvarez, Lorenzo Rosasco, and Neil D. Lawrence · 2012
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Practical Bayesian optimization of machine learning algorithms
Jasper Snoek, Hugo Larochelle, and Ryan P Adams · 2012
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The large margin mechanism for differentially private maximization
Kamalika Chaudhuri, Daniel J Hsu, and Shuang Song · 2014
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Fast calculation of multiobjective probability of improvement and expected improvement criteria for Pareto optimization
Ivo Couckuyt, Dirk Deschrijver, and Tom Dhaene · 2014
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The algorithmic foundations of differential privacy
Cynthia Dwork, Aaron Roth, et al · 2014
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MXNet: A flexible and efficient machine learning library for heterogeneous distributed systems
Tianqi Chen, Mu Li, Yutian Li, Min Lin, Naiyan Wang, Minjie Wang, Tianjun Xiao, Bing Xu, Chiyuan Zhang, and Zheng Zhang · 2015
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2015
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Differentially private Bayesian optimization
Matt Kusner, Jacob Gardner, Roman Garnett, and Kilian Weinberger · 2015
Cited alongside, same era.
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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ε \varepsilon -pal: an active learning approach to the multi-objective optimization problem
Marcela Zuluaga, Andreas Krause, and Markus Püschel · 2016
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Rényi differential privacy mechanisms for posterior sampling
Joseph Geumlek, Shuang Song, and Kamalika Chaudhuri · 2017
Cited alongside, same era.
Google vizier: A service for black-box optimization
Daniel Golovin, Benjamin Solnik, Subhodeep Moitra, Greg Kochanski, John Karro, and D Sculley · 2017
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Bayesian optimization with tree-structured dependencies
Rodolphe Jenatton, Cedric Archambeau, Javier González, and Matthias Seeger · 2017
Improving the Gaussian mechanism for differential privacy: Analytical calibration and optimal denoising
Borja Balle and Yu-Xiang Wang · 2018
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The secret sharer: Measuring unintended neural network memorization & extracting secrets
Nicholas Carlini, Chang Liu, Jernej Kos, Úlfar Erlingsson, and Dawn Song · 2018
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Privacy amplification by iteration
Vitaly Feldman, Ilya Mironov, Kunal Talwar, and Abhradeep Thakurta · 2018
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Issues encountered deploying differential privacy
Simson L Garfinkel, John M Abowd, and Sarah Powazek · 2018
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A batched scalable multi-objective bayesian optimization algorithm
Xi Lin, Hui-Ling Zhen, Zhenhua Li, Qingfu Zhang, and Sam Kwong · 2018
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Cited alongside, same era.
Fast Bayesian Optimization of Machine Learning Hyperparameters on Large Datasets
Aaron Klein, Stefan Falkner, Simon Bartels, Philipp Hennig, and Frank Hutter · 2017
Cited alongside, same era.
GPflowOpt: A Bayesian Optimization Library using TensorFlow
Nicolas Knudde, Joachim van der Herten, Tom Dhaene, and Ivo Couckuyt · 2017
Cited alongside, same era.
Pythia: Data dependent differentially private algorithm selection
Ios Kotsogiannis, Ashwin Machanavajjhala, Michael Hay, and Gerome Miklau · 2017
Cited alongside, same era.
Hyperband: A novel bandit-based approach to hyperparameter optimization
Lisha Li, Kevin Jamieson, Giulia DeSalvo, Afshin Rostamizadeh, and Ameet Talwalkar · 2017
Cited alongside, same era.
Accuracy first: Selecting a differential privacy level for accuracy constrained ERM
Katrina Ligett, Seth Neel, Aaron Roth, Bo Waggoner, and Steven Z Wu · 2017
Cited alongside, same era.
Understanding the sparse vector technique for differential privacy
Min Lyu, Dong Su, and Ninghui Li · 2017
Cited alongside, same era.
A general approach to adding differential privacy to iterative training procedures
H. Brendan McMahan and Galen Andrew · 2018
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Learning differentially private recurrent language models
H. Brendan McMahan, Daniel Ramage, Kunal Talwar, and Li Zhang · 2018
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Differential privacy: A primer for a non-technical audience (preliminary version)
Kobbi Nissim, Thomas Steinke, Alexandra Wood, Micah Altman, Aaron Bembenek, Mark Bun, Marco Gaboardi, David O’Brien, and Salil Vadhan · 2018
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Differentially private regression with Gaussian processes
Michael Smith, Mauricio Álvarez, Max Zwiessele, and Neil Lawrence · 2018
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A practical approach to differential private learning
Koen Lennart van der Veen · 2018
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An economic analysis of privacy protection and statistical accuracy as social choices
John M Abowd and Ian M Schmutte · 2019
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Apex: Accuracy-aware differentially private data exploration
Chang Ge, Xi He, Ihab F Ilyas, and Ashwin Machanavajjhala · 2019
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Private selection from private candidates
Jingcheng Liu and Kunal Talwar · 2019
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Balancing exploration and exploitation in multiobjective batch bayesian optimization
Hongyan Wang, Hua Xu, Yuan Yuan, Xiaomin Sun, and Junhui Deng · 2019
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Subsampled Rényi differential privacy and analytical moments accountant
Yu-Xiang Wang, Borja Balle, and Shiva Kasiviswanathan · 2019
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Targeting solutions in bayesian multi-objective optimization: sequential and batch versions
David Gaudrie, Rodolphe Le Riche, Victor Picheny, Benoit Enaux, and Vincent Herbert · 2020
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