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Bayesian quadrature optimization (BQO) maximizes the expectation of an expensive black-box integrand taken over a known probability distribution.
The application of bayesian methods for seeking the extremum
Jonas Mockus, Vytautas Tiesis, and Antanas Zilinskas · 1978
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Bayes–hermite quadrature
Anthony O’Hagan · 1991
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Gradient-based learning applied to document recognition
LeCun Yann, Bottou Leon, Bengio Yoshua, and Haffner Patrick · 1998
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Sequential Design of Computer Experiments to Minimize Integrated Response Functions
Brian Jonathan Williams · 2000
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Bayesian monte carlo
Carl Edward Rasmussen and Zoubin Ghahramani · 2002
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Gaussian processes for machine learning
Carl Edward Rasmussen and Christopher K. I. Williams · 2006
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Gaussian process optimization in the bandit setting: No regret and experimental design
Niranjan Srinivas, Andreas Krause, Sham M. Kakade, and Matthias W. Seeger · 2010
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Distributionally robust markov decision processes
Huan Xu and Shie Mannor · 2010
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Learning from distributions via support measure machines
Krikamol Muandet, Kenji Fukumizu, Francesco Dinuzzo, and Bernhard Schölkopf · 2012
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Scikit-learn: Machine learning in python, 2012
Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Andreas Müller, Joel Nothman, Gilles Louppe, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, Jake Vanderplas, Alexandre Passos, David Cournapeau, Matthieu Brucher, Matthieu Perrot, and Édouard Duchesnay · 2012
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Robust solutions of optimization problems affected by uncertain probabilities
Aharon Ben-Tal, Dick den Hertog, Anja De Waegenaere, Bertrand Melenberg, and Gijs Rennen · 2013
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Multi-task bayesian optimization
Kevin Swersky, Jasper Snoek, and Ryan Prescott Adams · 2013
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Predictive entropy search for efficient global optimization of black-box functions
José Miguel Hernández-Lobato, Matthew W. Hoffman, and Zoubin Ghahramani · 2014
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Learning to optimize via posterior sampling
Daniel Russo and Benjamin Van Roy · 2014
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
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Stochastic gradient methods for distributionally robust optimization with f-divergences
Hongseok Namkoong and John C. Duchi · 2016
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Probabilistic models for integration error in the assessment of functional cardiac models, 2016
Chris. J. Oates, Steven Niederer, Angela Lee, François-Xavier Briol, and Mark Girolami · 2016
Distributional robust kelly gambling, 2018
Qingyun Sun and Stephen Boyd · 2018
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Bayesian optimization with expensive integrands
Saul Toscano-Palmerin and Peter I. Frazier · 2018
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A distributionally robust boosting algorithm
Jose H. Blanchet, Yang Kang, Fan Zhang, and Zhangyi Hu · 2019
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Wasserstein distributionally robust optimization: Theory and applications in machine learning
Daniel Kuhn, Peyman Mohajerin Esfahani, Viet Anh Nguyen, and Soroosh Shafieezadeh-Abadeh · 2019
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A modern retrospective on probabilistic numerics
Chris J. Oates and Timothy John Sullivan · 2019
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Variance-based regularization with convex objectives
Hongseok Namkoong and John C. Duchi · 2017
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Bayesian simulation optimization with input uncertainty
Michael Pearce and Jürgen Branke · 2017
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Distributionally robust graphical models
Rizal Fathony, Ashkan Rezaei, Mohammad Ali Bashiri, Xinhua Zhang, and Brian D. Ziebart · 2018
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A tutorial on bayesian optimization
Peter I. Frazier · 2018
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Fairness without demographics in repeated loss minimization
Tatsunori B. Hashimoto, Megha Srivastava, Hongseok Namkoong, and Percy Liang · 2018
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Distributionally robust optimization: A review
Hamed Rahimian and Sanjay Mehrotra · 2019
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Distributionally robust reinforcement learning
Elena Smirnova, Elvis Dohmatob, and Jérémie Mary · 2019
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Distributionally robust optimization and generalization in kernel methods
Matthew Staib and Stefanie Jegelka · 2019
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Distributionally robust submodular maximization
Matthew Staib, Bryan Wilder, and Stefanie Jegelka · 2019
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A robust zero-sum game framework for pool-based active learning
Dixian Zhu, Zhe Li, Xiaoyu Wang, Boqing Gong, and Tianbao Yang · 2019
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