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Science and Engineering applications are typically associated with expensive optimization problems to identify optimal design solutions and states of the system of interest.
Sequential design of experiments
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A new method of locating the maximum point of an arbitrary multipeak curve in the presence of noise
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A new method of locating the maximum point of an arbitrary multipeak curve in the presence of noise
Harold J Kushner · 1964
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Single-step bayesian search method for an extremum of functions of a single variable
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Adaptive importance sampling
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Global r/sup d/optimization when probes are expensive: the grope algorithm
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Query by committee
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Latin hypercube sampling as a tool in uncertainty analysis of computer models
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Neural network exploration using optimal experiment design
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Heterogeneous uncertainty sampling for supervised learning
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Goal-driven learning
Ashwin Ram and David B Leake · 1995
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Weighted average importance sampling and defensive mixture distributions
Tim Hesterberg · 1995
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Minimisation of data collection by active learning
Tirthankar RayChaudhuri and Leonard GC Hamey · 1995
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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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Query learning strategies using boosting and bagging
Naoki Abe · 1998
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Efficient progressive sampling
Foster Provost, David Jensen, and Tim Oates · 1999
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Monte Carlo statistical methods
Christian P Robert, George Casella, and George Casella · 1999
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A comparison of three methods for selecting values of input variables in the analysis of output from a computer code
Michael D McKay, Richard J Beckman, and William J Conover · 2000
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Estimation of local modeling error and goal-oriented adaptive modeling of heterogeneous materials: I. error estimates and adaptive algorithms
J Tinsley Oden and Kumar S Vemaganti · 2000
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Predicting the output from a complex computer code when fast approximations are available
Marc C Kennedy and Anthony O’Hagan · 2000
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A taxonomy of global optimization methods based on response surfaces
Donald R Jones · 2001
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On sequential sampling for global metamodeling in engineering design
Ruichen Jin, Wei Chen, and Agus Sudjianto · 2002
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Needs and opportunities for uncertainty-based multidisciplinary design methods for aerospace vehicles
Thomas A Zang · 2002
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Monte carlo sampling methods
Alexander Shapiro · 2003
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Quasi-monte carlo sampling
Art B Owen · 2003
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Dimension–adaptive tensor–product quadrature
Thomas Gerstner and Michael Griebel · 2003
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A bayesian optimization algorithm for the nurse scheduling problem
Jingpeng Li and Uwe Aickelin · 2003
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Gaussian processes in machine learning
Carl Edward Rasmussen · 2003
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Multi-criteria-based active learning for named entity recognition
Dan Shen, Jie Zhang, Jian Su, Guodong Zhou, and Chew Lim Tan · 2004
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On adaptive markov chain monte carlo algorithms
Yves F Atchadé and Jeffrey S Rosenthal · 2005
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A bayesian approach for shadow extraction from a single image
Tai-Pang Wu and Chi-Keung Tang · 2005
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Sequential kriging optimization using multiple-fidelity evaluations
Deng Huang, Theodore T Allen, William I Notz, and R Allen Miller · 2006
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Confidence-based active learning
Mingkun Li and Ishwar K Sethi · 2006
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Research on query-by-committee method of active learning and application
Yue Zhao, Ciwen Xu, and Yongcun Cao · 2006
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Goal-oriented, model-constrained optimization for reduction of large-scale systems
Tan Bui-Thanh, Karen Willcox, Omar Ghattas, and Bart van Bloemen Waanders · 2007
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Multi-fidelity optimization via surrogate modelling
Alexander IJ Forrester, András Sóbester, and Andy J Keane · 2007
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Active learning for regression based on query by committee
Robert Burbidge, Jem J Rowland, and Ross D King · 2007
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Margin based active learning
Maria-Florina Balcan, Andrei Broder, and Tong Zhang · 2007
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An analysis of active learning strategies for sequence labeling tasks
Burr Settles and Mark Craven · 2008
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Entropy-based active learning for object recognition
Alex Holub, Pietro Perona, and Michael C Burl · 2008
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Hierarchical sampling for active learning
Sanjoy Dasgupta and Daniel Hsu · 2008
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Engineering design via surrogate modelling: a practical guide
András Sobester, Alexander Forrester, and Andy Keane · 2008
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Fractional factorial design
Richard F Gunst and Robert L Mason · 2009
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Pool-based active learning in approximate linear regression
Masashi Sugiyama and Shinichi Nakajima · 2009
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A stochastic collocation approach to bayesian inference in inverse problems
Youssef Marzouk and Dongbin Xiu · 2009
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An efficient bayesian inference approach to inverse problems based on an adaptive sparse grid collocation method
Xiang Ma and Nicholas Zabaras · 2009
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Active learning literature survey
Burr Settles · 2009
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Gaussian processes for global optimization
Michael A Osborne, Roman Garnett, and Stephen J Roberts · 2009
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Efficiently learning the accuracy of labeling sources for selective sampling
Pinar Donmez, Jaime G Carbonell, and Jeff Schneider · 2009
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Adaptive markov chain monte carlo: theory and methods
Yves Atchade, Gersende Fort, Eric Moulines, and Pierre Priouret · 2011
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Abrupt motion tracking via intensively adaptive markov-chain monte carlo sampling
Xiuzhuang Zhou, Yao Lu, Jiwen Lu, and Jie Zhou · 2011
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The correlated knowledge gradient for simulation optimization of continuous parameters using gaussian process regression
Warren Scott, Peter Frazier, and Warren Powell · 2011
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Incremental relabeling for active learning with noisy crowdsourced annotations
Liyue Zhao, Gita Sukthankar, and Rahul Sukthankar · 2011
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Active learning from crowds
Yan Yan, Romer Rosales, Glenn Fung, and Jennifer G Dy · 2011
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Local and dimension adaptive stochastic collocation for uncertainty quantification
John D Jakeman and Stephen G Roberts · 2012
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Bayesian approach to global optimization: theory and applications
Jonas Mockus · 2012
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Entropy search for information-efficient global optimization
Philipp Hennig and Christian J Schuler · 2012
Multi-fidelity optimization of super-cavitating hydrofoils
L Bonfiglio, P Perdikaris, S Brizzolara, and GE Karniadakis · 2018
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Sequential optimization using multi-level cokriging and extended expected improvement criterion
Yixin Liu, Shishi Chen, Fenggang Wang, and Fenfen Xiong · 2018
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Adaptive sampling approaches for surrogate-based optimization
Lisia Dias, Atharv Bhosekar, and Mariathi Ierapetritou · 2019
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Efficient adaptive multiple importance sampling
Yousef El-Laham, Luca Martino, Víctor Elvira, and Mónica F Bugallo · 2019
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Financial applications of gaussian processes and bayesian optimization
Joan Gonzalvez, Edmond Lezmi, Thierry Roncalli, and Jiali Xu · 2019
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Active learning from multiple knowledge sources
Yan Yan, Rómer Rosales, Glenn Fung, Faisal Farooq, Bharat Rao, and Jennifer Dy · 2012
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Conjoint measurement: Methods and applications
Anders Gustafsson, Andreas Herrmann, and Frank Huber · 2013
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Goal-oriented inference: Approach, linear theory, and application to advection diffusion
Chad Lieberman and Karen Willcox · 2013
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Maximizing expected model change for active learning in regression
Wenbin Cai, Ya Zhang, and Jun Zhou · 2013
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Batch mode active sampling based on marginal probability distribution matching
Rita Chattopadhyay, Zheng Wang, Wei Fan, Ian Davidson, Sethuraman Panchanathan, and Jieping Ye · 2013
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Active learning without knowing individual instance labels: a pairwise label homogeneity query approach
Yifan Fu, Bin Li, Xingquan Zhu, and Chengqi Zhang · 2013
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John J Dudley, Jason T Jacques, and Per Ola Kristensson · 2019
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A framework for bayesian optimization in embedded subspaces
Amin Nayebi, Alexander Munteanu, and Matthias Poloczek · 2019
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Scalable global optimization via local bayesian optimization
David Eriksson, Michael Pearce, Jacob Gardner, Ryan D Turner, and Matthias Poloczek · 2019
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Practical two-step lookahead bayesian optimization
Jian Wu and Peter Frazier · 2019
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Issues in deciding whether to use multifidelity surrogates
M Giselle Fernández-Godino, Chanyoung Park, Nam H Kim, and Raphael T Haftka · 2019
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Multi-fidelity efficient global optimization: Methodology and application to airfoil shape design
Mostafa Meliani, Nathalie Bartoli, Thierry Lefebvre, Mohamed-Amine Bouhlel, Joaquim RRA Martins, and Joseph Morlier · 2019
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Multifidelity and multiscale bayesian framework for high-dimensional engineering design and calibration
Soumalya Sarkar, Sudeepta Mondal, Michael Joly, Matthew E Lynch, Shaunak D Bopardikar, Ranadip Acharya, and Paris Perdikaris · 2019
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A novel active learning framework for classification: Using weighted rank aggregation to achieve multiple query criteria
Yu Zhao, Zhenhui Shi, Jingyang Zhang, Dong Chen, and Lixu Gu · 2019
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Self-paced active learning: Query the right thing at the right time
Ying-Peng Tang and Sheng-Jun Huang · 2019
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A new reliability method for small failure probability problems by combining the adaptive importance sampling and surrogate models
Ning-Cong Xiao, Hongyou Zhan, and Kai Yuan · 2020
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An adaptive scheme for reliability-based global design optimization: A markov chain monte carlo approach
HA Jensen, DJ Jerez, and M Valdebenito · 2020
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Adaptive multi-index collocation for uncertainty quantification and sensitivity analysis
John D Jakeman, Michael S Eldred, Gianluca Geraci, and Alex Gorodetsky · 2020
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A fully adaptive multilevel stochastic collocation strategy for solving elliptic pdes with random data
Jens Lang, Robert Scheichl, and David Silvester · 2020
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Energy management strategy for electric vehicles based on deep q-learning using bayesian optimization
Huifang Kong, Jiapeng Yan, Hai Wang, and Lei Fan · 2020
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An efficient application of bayesian optimization to an industrial mdo framework for aircraft design
Remy Priem, Hugo Gagnon, Ian Chittick, Stephane Dufresne, Youssef Diouane, and Nathalie Bartoli · 2020
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Efficient exploration of reward functions in inverse reinforcement learning via bayesian optimization
Sreejith Balakrishnan, Quoc Phong Nguyen, Bryan Kian Hsiang Low, and Harold Soh · 2020
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Distributed bayesian optimization of deep reinforcement learning algorithms
M Todd Young, Jacob D Hinkle, Ramakrishnan Kannan, and Arvind Ramanathan · 2020
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Sequential gallery for interactive visual design optimization
Yuki Koyama, Issei Sato, and Masataka Goto · 2020
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Parallel bayesian global optimization of expensive functions
Jialei Wang, Scott C Clark, Eric Liu, and Peter I Frazier · 2020
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Comparison of multi-fidelity approaches for military vehicle design
Philip S Beran, Dean Bryson, Andrew S Thelen, Matteo Diez, and Andrea Serani · 2020
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Practical multi-fidelity bayesian optimization for hyperparameter tuning
Jian Wu, Saul Toscano-Palmerin, Peter I Frazier, and Andrew Gordon Wilson · 2020
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Variable-fidelity probability of improvement method for efficient global optimization of expensive black-box problems
Xiongfeng Ruan, Ping Jiang, Qi Zhou, Jiexiang Hu, and Leshi Shu · 2020
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Multi-fidelity bayesian optimization with max-value entropy search and its parallelization
Shion Takeno, Hitoshi Fukuoka, Yuhki Tsukada, Toshiyuki Koyama, Motoki Shiga, Ichiro Takeuchi, and Masayuki Karasuyama · 2020
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Cmal: Cost-effective multi-label active learning by querying subexamples
Guoxian Yu, Xia Chen, Carlotta Domeniconi, Jun Wang, Zhao Li, Zili Zhang, and Xiangliang Zhang · 2020
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Cost-accuracy aware adaptive labeling for active learning
Ruijiang Gao and Maytal Saar-Tsechansky · 2020
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Engineering design optimization
Joaquim RRA Martins and Andrew Ning · 2021
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Deepxde: A deep learning library for solving differential equations
Lu Lu, Xuhui Meng, Zhiping Mao, and George Em Karniadakis · 2021
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Efficient training of physics-informed neural networks via importance sampling
Mohammad Amin Nabian, Rini Jasmine Gladstone, and Hadi Meidani · 2021
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A comparative survey: Benchmarking for pool-based active learning
Xueying Zhan, Huan Liu, Qing Li, and Antoni B Chan · 2021
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Bayesian optimization with safety constraints: safe and automatic parameter tuning in robotics
Felix Berkenkamp, Andreas Krause, and Angela P Schoellig · 2021
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Automatic tuning of hyperparameters using bayesian optimization
A Helen Victoria and Ganesh Maragatham · 2021
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Bayesian optimization is superior to random search for machine learning hyperparameter tuning: Analysis of the black-box optimization challenge 2020
Ryan Turner, David Eriksson, Michael McCourt, Juha Kiili, Eero Laaksonen, Zhen Xu, and Isabelle Guyon · 2021
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Multifidelity do-main-aware learning for the design of re-entry vehicles
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Scalable inverse reinforcement learning through multifidelity bayesian optimization
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Human-in-the-Loop Machine Learning: Active learning and annotation for human-centered AI
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Non-myopic multifidelity bayesian optimization
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On the convergence of adaptive stochastic collocation for elliptic partial differential equations with affine diffusion
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