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Bayesian optimization is a powerful tool for fine-tuning the hyper-parameters of a wide variety of machine learning models.
The application of bayesian methods for seeking the extremum
Mockus, Jonas, Tiesis, Vytautas, and Zilinskas, Antanas · 1978
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Support-vector networks
Cortes, Corinna and Vapnik, Vladimir · 1995
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Weaving technology and policy together to maintain confidentiality
Sweeney, Latanya · 1997
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Learning with kernels: support vector machines, regularization, optimization, and beyond
Schölkopf, Bernhard and Smola, Alexander J · 2001
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Finite-time analysis of the multiarmed bandit problem
Auer, Peter, Cesa-Bianchi, Nicolo, and Fischer, Paul · 2002
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Revealing information while preserving privacy
Dinur, Irit and Nissim, Kobbi · 2003
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Traffic accident analysis using machine learning paradigms
Chong, Miao M, Abraham, Ajith, and Paprzycki, Marcin · 2005
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Our data, ourselves: Privacy via distributed noise generation
Dwork, Cynthia, Kenthapadi, Krishnaram, McSherry, Frank, Mironov, Ilya, and Naor, Moni · 2006
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Gaussian processes for machine learning
Rasmussen, Carl Edward and Williams, Christopher K. I · 2006
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Mechanism design via differential privacy
McSherry, Frank and Talwar, Kunal · 2007
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Online learning: Theory, algorithms, and applications
Shalev-Shwartz, Shai · 2007
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Multi-task gaussian process prediction
Bonilla, Edwin, Chai, Kian Ming, and Williams, Christopher · 2008
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Composition attacks and auxiliary information in data privacy
Ganta, Srivatsava Ranjit, Kasiviswanathan, Shiva Prasad, and Smith, Adam · 2008
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Near-optimal sensor placements in gaussian processes: Theory, efficient algorithms and empirical studies
Krause, Andreas, Singh, Ajit, and Guestrin, Carlos · 2008
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Robust de-anonymization of large sparse datasets
Narayanan, Arvind and Shmatikov, Vitaly · 2008
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Feature hashing for large scale multitask learning
Weinberger, Kilian, Dasgupta, Anirban, Langford, John, Smola, Alex, and Attenberg, Josh · 2009
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Gaussian process optimization in the bandit setting: No regret and experimental design
Srinivas, Niranjan, Krause, Andreas, Kakade, Sham M, and Seeger, Matthias · 2010
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Convergence properties of the expected improvement algorithm with fixed mean and covariance functions
Vazquez, Emmanuel and Bect, Julien · 2010
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Convergence rates of efficient global optimization algorithms
Bull, Adam D · 2011
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Differentially private empirical risk minimization
Chaudhuri, Kamalika, Monteleoni, Claire, and Sarwate, Anand D · 2011
A stability-based validation procedure for differentially private machine learning
Chaudhuri, Kamalika and Vinterbo, Staal A · 2013
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Local privacy and statistical minimax rates
Duchi, John C, Jordan, Michael I, and Wainwright, Martin J · 2013
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The algorithmic foundations of differential privacy
Dwork, Cynthia and Roth, Aaron · 2013
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Differentially private learning with kernels
Jain, Prateek and Thakurta, Abhradeep · 2013
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Stochastic gradient descent with differentially private updates
Song, Shuang, Chaudhuri, Kamalika, and Sarwate, Anand D · 2013
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Multi-task bayesian optimization
Swersky, Kevin, Snoek, Jasper, and Adams, Ryan P · 2013
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Sequential model-based optimization for general algorithm configuration
Hutter, Frank, Hoos, H. Holger, and Leyton-Brown, Kevin · 2011
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Hybrid batch bayesian optimization
Azimi, Javad, Jalali, Ali, and Fern, Xiaoli Z · 2012
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Random search for hyper-parameter optimization
Bergstra, James and Bengio, Yoshua · 2012
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Exponential regret bounds for gaussian process bandits with deterministic observations
de Freitas, Nando, Smola, Alex, and Zoghi, Masrour · 2012
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Differentially private online learning
Jain, Prateek, Kothari, Pravesh, and Thakurta, Abhradeep · 2012
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Private convex empirical risk minimization and high-dimensional regression
Kifer, Daniel, Smith, Adam, and Thakurta, Abhradeep · 2012
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Predicting readmission risk with institution specific prediction models
Yu, Shipeng, Esbroeck, Alexander van, Farooq, Faisal, Fung, Glenn, Anand, Vikram, and Krishnapuram, Balaji · 2013
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Private empirical risk minimization, revisited
Bassily, Raef, Smith, Adam, and Thakurta, Abhradeep · 2014
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Bayesian optimization with inequality constraints
Gardner, Jacob, Kusner, Matt, Xu, Zhixiang, Weinberger, Kilian, and Cunningham, John · 2014
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On correlation and budget constraints in model-based bandit optimization with application to automatic machine learning
Hoffman, Matthew, Shahriari, Bobak, and de Freitas, Nando · 2014
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Ramp loss linear programming support vector machine
Huang, Xiaolin, Shi, Lei, and Suykens, Johan AK · 2014
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(near) dimension independent risk bounds for differentially private learning
Jain, Prateek and Thakurta, Abhradeep Guha · 2014
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Private stochastic multi-arm bandits: From theory to practice
Mishra, Nikita and Thakurta, Abhradeep · 2014
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