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Bayesian optimisation presents a sample-efficient methodology for global optimisation.
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A Sherman–Morrison–Woodbury identity for rank augmenting matrices with application to centering
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A direct adaptive method for faster backpropagation learning: The Rprop algorithm
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A limited memory algorithm for bound constrained optimization
Richard H Byrd, Peihuang Lu, Jorge Nocedal, and Ciyou Zhu · 1995
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Adapting arbitrary normal mutation distributions in evolution strategies: The covariance matrix adaptation
Nikolaus Hansen and Andreas Ostermeier · 1996
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Differential evolution: a fast and simple numerical optimizer
Kenneth V Price · 1996
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Neural learning in structured parameter spaces-natural Riemannian gradient
Shun-ichi Amari · 1997
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Algorithm 778: L-BFGS-B: Fortran subroutines for large-scale bound-constrained optimization
Ciyou Zhu, Richard H Byrd, Peihuang Lu, and Jorge Nocedal · 1997
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Natural gradient works efficiently in learning
Shun-ichi Amari · 1998
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Efficient global optimization of expensive black-box functions
Donald R Jones, Matthias Schonlau, and William J Welch · 1998
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Evolutionary design by computers
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Iterative Methods for Optimization
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From naive mean field theory to the tap equations
Manfred Opper, Ole Winther, et al · 2001
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A fast and elitist multiobjective genetic algorithm: NSGA-II
Kalyanmoy Deb, Amrit Pratap, Sameer Agarwal, and TAMT Meyarivan · 2002
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Quasi-Monte Carlo sampling
Art B Owen · 2003
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Convex Optimization
Stephen Boyd and Lieven Vandenberghe · 2004
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Numerical computation of rectangular bivariate and trivariate normal and t probabilities
Alan Genz · 2004
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A computational efficient covariance matrix update and a (1+ 1)-CMA for evolution strategies
Christian Igel, Thorsten Suttorp, and Nikolaus Hansen · 2006
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Improving evolution strategies through active covariance matrix adaptation
Grahame A Jastrebski and Dirk V Arnold · 2006
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Gaussian Processes for Machine Learning , volume 2
Carl Edward Rasmussen and Christopher KI Williams · 2006
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Methods of Information Geometry
Shun-ichi Amari and Hiroshi Nagaoka · 2007
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The tradeoffs of large scale learning
Léon Bottou and Olivier Bousquet · 2007
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Online learning: Theory, algorithms, and applications
Shai Shalev-Shwartz and Yoram Singer · 2007
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A Multi-points Criterion for Deterministic Parallel Global Optimization based on Gaussian Processes
David Ginsbourger, Rodolphe Le Riche, and Laurent Carraro · 2008
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Gaussian processes for global optimization
Michael A Osborne, Roman Garnett, and Stephen J Roberts · 2009
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Variational learning of inducing variables in sparse Gaussian processes
Michalis Titsias · 2009
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Regret-based optimal recommendation sets in conversational recommender systems
Paolo Viappiani and Craig Boutilier · 2009
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Gaussian process optimization in the bandit setting: no regret and experimental design
Niranjan Srinivas, Andreas Krause, Sham Kakade, and Matthias Seeger · 2010
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Gaussian probabilities and expectation propagation
John P Cunningham, Philipp Hennig, and Simon Lacoste-Julien · 2011
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Adaptive subgradient methods for online learning and stochastic optimization
John Duchi, Elad Hazan, and Yoram Singer · 2011
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Sequential model-based optimization for general algorithm configuration
Frank Hutter, Holger H Hoos, and Kevin Leyton-Brown · 2011
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Differential-Geometrical Methods in Statistics
Shun-ichi Amari · 2012
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Random search for hyper-parameter optimization
James Bergstra and Yoshua Bengio · 2012
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Entropy search for information-efficient global optimization
Philipp Hennig and Christian J Schuler · 2012
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Neural networks for machine learning lecture 6a overview of mini-batch gradient descent
Geoffrey Hinton, Nitish Srivastava, and Kevin Swersky · 2012
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SAS-Pro: Simultaneous residue assignment and structure superposition for protein structure alignment
Shweta B Shah and Nikolaos V Sahinidis · 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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Interpolation of spatial data: some theory for kriging
Michael L Stein · 2012
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Lecture 6.5—RMSprop: Divide the gradient by a running average of its recent magnitude
T. Tieleman and G. Hinton · 2012
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Adadelta: An adaptive learning rate method
Matthew D Zeiler · 2012
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Combinatorial multi-armed bandit: General framework and applications
Wei Chen, Yajun Wang, and Yang Yuan · 2013
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Fast computation of the multi-points expected improvement with applications in batch selection
A Bayesian optimization approach to compute Nash equilibrium of potential games using bandit feedback
Anup Aprem and Stephen Roberts · 2018
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Optimization methods for large-scale machine learning
Léon Bottou, Frank E Curtis, and Jorge Nocedal · 2018
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BOHB: Robust and efficient hyperparameter optimization at scale
Stefan Falkner, Aaron Klein, and Frank Hutter · 2018
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A tutorial on Bayesian optimization
Peter I Frazier · 2018
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Gpytorch: Blackbox matrix-matrix Gaussian process inference with GPU acceleration
Jacob R Gardner, Geoff Pleiss, David Bindel, Kilian Q Weinberger, and Andrew Gordon Wilson · 2018
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Clément Chevalier and David Ginsbourger · 2013
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Parallel Gaussian process optimization with upper confidence bound and pure exploration
Emile Contal, David Buffoni, Alexandre Robicquet, and Nicolas Vayatis · 2013
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Gaussian processes for Big data
James Hensman, Nicolò Fusi, and Neil D Lawrence · 2013
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A literature survey of benchmark functions for global optimisation problems
Momin Jamil and Xin-She Yang · 2013
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Stochastic simultaneous optimistic optimization
Michal Valko, Alexandra Carpentier, and Rémi Munos · 2013
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Auto-encoding variational Bayes
Diederik P. Kingma and Max Welling · 2014
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RES: Regularized stochastic BFGS algorithm
Aryan Mokhtari and Alejandro Ribeiro · 2014
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Rafael Gómez-Bombarelli, Jennifer N Wei, David Duvenaud, José Miguel Hernández-Lobato, Benjamín Sánchez-Lengeling, Dennis Sheberla, Jorge Aguilera-Iparraguirre, Timothy D Hirzel, Ryan P Adams, and Alán Aspuru-Guzik · 2018
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Parallelised Bayesian optimisation via Thompson sampling
Kirthevasan Kandasamy, Akshay Krishnamurthy, Jeff Schneider, and Barnabás Póczos · 2018
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Attentive neural processes
Hyunjik Kim, Andriy Mnih, Jonathan Schwarz, Marta Garnelo, Ali Eslami, Dan Rosenbaum, Oriol Vinyals, and Yee Whye Teh · 2018
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Optimization of circuitry arrangements for heat exchangers using derivative-free optimization
Nikolaos Ploskas, Christopher Laughman, Arvind U Raghunathan, and Nikolaos V Sahinidis · 2018
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Semi-implicit variational inference
Mingzhang Yin and Mingyuan Zhou · 2018
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Wasserstein robust reinforcement learning
Mohammed Amin Abdullah, Hang Ren, Haitham Bou Ammar, Vladimir Milenkovic, Rui Luo, Mingtian Zhang, and Jun Wang · 2019
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Bayesian optimization of composite functions
Raul Astudillo and Peter Frazier · 2019
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Achieving robustness to aleatoric uncertainty with heteroscedastic Bayesian optimisation
Ryan-Rhys Griffiths, Miguel Garcia-Ortegon, Alexander A Aldrick, and Alpha A Lee · 2019
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2019
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 2019
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Bayesian optimisation over multiple continuous and categorical inputs
Binxin Ru, Ahsan S Alvi, Vu Nguyen, Michael A Osborne, and Stephen J Roberts · 2019
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A general framework for multi-fidelity Bayesian optimization with Gaussian processes
Jialin Song, Yuxin Chen, and Yisong Yue · 2019
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A survey of optimization methods from a machine learning perspective
Shiliang Sun, Zehui Cao, Han Zhu, and Jing Zhao · 2019
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Distributed Newton method for large-scale consensus optimization
Rasul Tutunov, Haitham Bou-Ammar, and Ali Jadbabaie · 2019
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BANANAS: Bayesian optimization with neural architectures for neural architecture search
Colin White, Willie Neiswanger, and Yash Savani · 2019
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Regret in online recommendation systems
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