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Bayesian optimization (BO) has become a popular strategy for global optimization of expensive real-world functions.
An Introduction to Multivariate Statistical Analysis
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A conservation law for generalization performance
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A Bayesian/information theoretic model of bias learning
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Multitask learning
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Learning how to learn is learning with point sets
Thomas P. Minka and Rosalind W. Picard · 1997
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No free lunch theorems for optimization
David H Wolpert and William G Macready · 1997
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Adaptive estimation of a quadratic functional by model selection
Beatrice Laurent and Pascal Massart · 2000
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Using confidence bounds for exploitation-exploration tradeoffs
Peter Auer · 2002
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Benchmarking optimization software with performance profiles
Elizabeth D Dolan and Jorge J Moré · 2002
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Probability theory: The logic of science
E. T. Jaynes · 2003
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Artificial intelligence: A modern approach
Stuart J. Russell and Peter Norvig · 2003
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Memory-based Language Processing
Walter Daelemans, Antal Van den Bosch, et al · 2005
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Elements of Information Theory
Thomas M Cover and Joy A Thomas · 2006
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Gaussian processes for machine learning
Carl Edward Rasmussen and Christopher K.I. Williams · 2006
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Multi-task Gaussian process prediction
Edwin V Bonilla, Kian Chai, and Christopher Williams · 2007
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Multivariate statistics: A vector space approach
Morris L. Eaton · 2007
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An overview of clustering methods
Mahamed GH Omran, Andries P Engelbrecht, and Ayed Salman · 2007
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Random features for large-scale kernel machines
Ali Rahimi, Benjamin Recht, et al · 2007
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Modeling human function learning with gaussian processes
Thomas Griffiths, Chris Lucas, Joseph Williams, and Michael Kalish · 2008
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The discovery of structural form
Charles Kemp and Joshua B Tenenbaum · 2008
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Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
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Variational learning of inducing variables in sparse Gaussian processes
Michalis Titsias · 2009
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MNIST handwritten digit database
Yann LeCun, Corinna Cortes, and CJ Burges · 2010
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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 Seeger · 2010
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Graph kernels
S Vichy N Vishwanathan, Nicol N Schraudolph, Risi Kondor, and Karsten M Borgwardt · 2010
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Contextual Gaussian process bandit optimization
Andreas Krause and Cheng S Ong · 2011
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Entropy search for information-efficient global optimization
Philipp Hennig and Christian J Schuler · 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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Collaborative hyperparameter tuning
Rémi Bardenet, Mátyás Brendel, Balázs Kégl, and Michele Sebag · 2013
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Bayesian Optimization and Semiparametric Models with Applications to Assistive Technology
Jasper Snoek · 2013
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On the importance of initialization and momentum in deep learning
Ilya Sutskever, James Martens, George Dahl, and Geoffrey Hinton · 2013
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Multi-task Bayesian optimization
Kevin Swersky, Jasper Snoek, and Ryan P Adams · 2013
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Gaussian process optimization with mutual information
Emile Contal, Vianney Perchet, and Nicolas Vayatis · 2014
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DeCAF: A deep convolutional activation feature for generic visual recognition
Jeff Donahue, Yangqing Jia, Oriol Vinyals, Judy Hoffman, Ning Zhang, Eric Tzeng, and Trevor Darrell · 2014
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Bayesian optimization with inequality constraints
Jacob Gardner, Matt Kusner, Zhixiang, Kilian Weinberger, and John Cunningham · 2014
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Bayesian optimization with unknown constraints
Michael A. Gelbart, Jasper Snoek, and Ryan P. Adams · 2014
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Rich feature hierarchies for accurate object detection and semantic segmentation
Ross Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik · 2014
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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
Cited alongside, same era.
Efficient transfer learning method for automatic hyperparameter tuning
Dani Yogatama and Gideon Mann · 2014
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Efficient and robust automated machine learning
Matthias Feurer, Aaron Klein, Katharina Eggensperger, Jost Springenberg, Manuel Blum, and Frank Hutter · 2015
No-regret Bayesian optimization with unknown hyperparameters
Felix Berkenkamp, Angela P Schoellig, and Andreas Krause · 2019
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On empirical comparisons of optimizers for deep learning
Dami Choi, Christopher J Shallue, Zachary Nado, Jaehoon Lee, Chris J Maddison, and George E Dahl · 2019
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BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
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Learning to guide task and motion planning using score-space representation
Beomjoon Kim, Zi Wang, Leslie Pack Kaelbling, and Tomás Lozano-Pérez · 2019
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Hyperpriors for Matérn fields with applications in Bayesian inversion
Lassi Roininen, Mark Girolami, Sari Lasanen, and Markku Markkanen · 2019
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Cited alongside, same era.
Constrained Bayesian Optimization and Applications
Michael Gelbart · 2015
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2015
Cited alongside, same era.
ImageNet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, and Li Fei-Fei · 2015
Cited alongside, same era.
Scalable Bayesian optimization using deep neural networks
Jasper Snoek, Oren Rippel, Kevin Swersky, Ryan Kiros, Nadathur Satish, Narayanan Sundaram, Mostofa Patwary, Mr Prabhat, and Ryan Adams · 2015
Cited alongside, same era.
The human kernel
Andrew G Wilson, Christoph Dann, Chris Lucas, and Eric P Xing · 2015
Cited alongside, same era.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Cited alongside, same era.
Functional variational Bayesian neural networks
Shengyang Sun, Guodong Zhang, Jiaxin Shi, and Roger Grosse · 2019
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Which algorithmic choices matter at which batch sizes? insights from a noisy quadratic model
Guodong Zhang, Lala Li, Zachary Nado, James Martens, Sushant Sachdeva, George Dahl, Chris Shallue, and Roger B Grosse · 2019
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The DeepMind JAX Ecosystem, 2020
Igor Babuschkin, Kate Baumli, Alison Bell, Surya Bhupatiraju, Jake Bruce, Peter Buchlovsky, David Budden, Trevor Cai, Aidan Clark, Ivo Danihelka, Claudio Fantacci, Jonathan Godwin, Chris Jones, Ross Hemsley, Tom Hennigan, Matteo Hessel, Shaobo Hou, Steven Kapturowski, Thomas Keck, Iurii Kemaev, Michael King, Markus Kunesch, Lena Martens, Hamza Merzic, Vladimir Mikulik, Tamara Norman, John Quan, George Papamakarios, Roman Ring, Francisco Ruiz, Alvaro Sanchez, Rosalia Schneider, Eren Sezener, Stephen Spencer, Srivatsan Srinivasan, Luyu Wang, Wojciech Stokowiec, and Fabio Viola · 2020
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Survey of machine-learning experimental methods at NeurIPS2019 and ICLR2020
Xavier Bouthillier and Gaël Varoquaux · 2020
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Language models are few-shot learners
Tom B Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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Understanding variational inference in function-space
David R Burt, Sebastian W Ober, Adrià Garriga-Alonso, and Mark van der Wilk · 2020
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Constrained Bayesian optimization for automatic chemical design using variational autoencoders
Ryan-Rhys Griffiths and José Miguel Hernández-Lobato · 2020
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Flax: A neural network library and ecosystem for JAX, 2020
Jonathan Heek, Anselm Levskaya, Avital Oliver, Marvin Ritter, Bertrand Rondepierre, Andreas Steiner, and Marc van Zee · 2020
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Simple and principled uncertainty estimation with deterministic deep learning via distance awareness
Jeremiah Zhe Liu, Zi Lin, Shreyas Padhy, Dustin Tran, Tania Bedrax-Weiss, and Balaji Lakshminarayanan · 2020
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Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu · 2020
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Distributionally robust neural networks for group shifts: On the importance of regularization for worst-case generalization
Shiori Sagawa, Pang Wei Koh, Tatsunori B Hashimoto, and Percy Liang · 2020
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A quantile-based approach for hyperparameter transfer learning
David Salinas, Huibin Shen, and Valerio Perrone · 2020
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Meta-learning acquisition functions for transfer learning in Bayesian optimization
Michael Volpp, Lukas P Fröhlich, Kirsten Fischer, Andreas Doerr, Stefan Falkner, Frank Hutter, and Christian Daniel · 2020
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Asymptotic analysis of maximum likelihood estimation of covariance parameters for Gaussian processes: an introduction with proofs
François Bachoc · 2021
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Misspecified Gaussian process bandit optimization
Ilija Bogunovic and Andreas Krause · 2021
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init2winit: a JAX codebase for initialization, optimization, and tuning research, 2021
Justin M. Gilmer, George E. Dahl, and Zachary Nado · 2021
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COCO: A platform for comparing continuous optimizers in a black-box setting
Nikolaus Hansen, Anne Auger, Raymond Ros, Olaf Mersmann, Tea Tušar, and Dimo Brockhoff · 2021
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Training independent subnetworks for robust prediction
Marton Havasi, Rodolphe Jenatton, Stanislav Fort, Jeremiah Zhe Liu, Jasper Snoek, Balaji Lakshminarayanan, Andrew M Dai, and Dustin Tran · 2021
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Ten lessons from three generations shaped Google’s TPUv4i
Norman P Jouppi, Doe Hyun Yoon, Matthew Ashcraft, Mark Gottscho, Thomas B Jablin, George Kurian, James Laudon, Sheng Li, Peter Ma, Xiaoyu Ma, et al · 2021
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HPO-B: A large-scale reproducible benchmark for black-box HPO based on OpenML
Sebastian Pineda-Arango, Hadi S. Jomaa, Martin Wistuba, and Josif Grabocka · 2021
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PACOH: Bayes-optimal meta-learning with PAC-guarantees
Jonas Rothfuss, Vincent Fortuin, Martin Josifoski, and Andreas Krause · 2021
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Bayesian reaction optimization as a tool for chemical synthesis
Benjamin J Shields, Jason Stevens, Jun Li, Marvin Parasram, Farhan Damani, Jesus I Martinez Alvarado, Jacob M Janey, Ryan P Adams, and Abigail G Doyle · 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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Learning compositional models of robot skills for task and motion planning
Zi Wang, Caelan Reed Garrett, Leslie Pack Kaelbling, and Tomás Lozano-Pérez · 2021
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Few-shot Bayesian optimization with deep kernel surrogates
Martin Wistuba and Josif Grabocka · 2021
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Towards learning universal hyperparameter optimizers with transformers
Yutian Chen, Xingyou Song, Chansoo Lee, Zi Wang, Qiuyi Zhang, David Dohan, Kazuya Kawakami, Greg Kochanski, Arnaud Doucet, Marc’Aurelio Ranzato, Sagi Perel, and Nando de Freitas · 2022
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HEBO: Pushing the limits of sample-efficient hyperparameter optimisation
Alexander I Cowen-Rivers, Wenlong Lyu, Rasul Tutunov, Zhi Wang, Antoine Grosnit, Ryan Rhys Griffiths, Alexandre Max Maraval, Hao Jianye, Jun Wang, Jan Peters, and Haitham Bou-Ammar · 2022
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HyperBO+: Pre-training a universal hierarchical Gaussian process prior for Bayesian optimization
Zhou Fan, Xinran Han, and Zi Wang · 2022
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Deep learning for Bayesian optimization of scientific problems with high-dimensional structure
Samuel Kim, Peter Y Lu, Charlotte Loh, Jamie Smith, Jasper Snoek, and Marin Soljačić · 2022
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Bayesian optimization allowing for common random numbers
Michael Arthur Leopold Pearce, Matthias Poloczek, and Juergen Branke · 2022
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Transfer learning with gaussian processes for bayesian optimization
Petru Tighineanu, Kathrin Skubch, Paul Baireuther, Attila Reiss, Felix Berkenkamp, and Julia Vinogradska · 2022
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All you need is a good functional prior for Bayesian deep learning
Ba-Hien Tran, Simone Rossi, Dimitrios Milios, and Maurizio Filippone · 2022
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Bayesian optimization
Roman Garnett · 2023
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Transfer learning for bayesian optimization on heterogeneous search spaces
Zhou Fan, Xinran Han, and Zi Wang · 2024
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Numerically stable sparse Gaussian processes via minimum separation using cover trees
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