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Bayesian optimisation is a sample-efficient search methodology that holds great promise for accelerating drug and materials discovery programs.
On the likelihood that one unknown probability exceeds another in view of the evidence of two samples
William R Thompson · 1933
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A New Method of Locating the Maximum Point of an Arbitrary Multipeak Curve in the Presence of Noise
H. J. Kushner · 1964
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The application of Bayesian methods for seeking the extremum
Tiesis V. Mockus J. and Žilinskas · 1978
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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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Efficient global optimization of expensive black-box functions
Donald R Jones, Matthias Schonlau, and William J Welch · 1998
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Heteroscedastic Gaussian process regression
Quoc V Le, Alex J Smola, and Stéphane Canu · 2005
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Gaussian Processes for Machine Learning
C. E. Rasmussen and C. K. I. Williams · 2006
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Global optimization of stochastic black-box systems via sequential kriging meta-models
Deng Huang, Theodore T Allen, William I Notz, and Ning Zeng · 2006
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Most likely heteroscedastic Gaussian process regression
Kristian Kersting, Christian Plagemann, Patrick Pfaff, and Wolfram Burgard · 2007
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Global optimization based on noisy evaluations: An empirical study of two statistical approaches
Emmanuel Vazquez, Julien Villemonteix, Maryan Sidorkiewicz, and Eric Walter · 2008
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The knowledge-gradient policy for correlated normal beliefs
Peter Frazier, Warren Powell, and Savas Dayanik · 2009
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Variational heteroscedastic Gaussian process regression
Miguel Lázaro-Gredilla and Michalis K Titsias · 2011
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Heteroscedastic Gaussian process regression using expectation propagation
Luis Muñoz-González, Miguel Lázaro-Gredilla, and Aníbal R Figueiras-Vidal · 2011
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Gaussian process regression with heteroscedastic or non-Gaussian residuals
Chunyi Wang and Radford M Neal · 2012
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Antifragile: Things That Gain from Disorder
Nassim Nicholas Taleb · 2012
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Variable risk control via stochastic optimization
Scott R Kuindersma, Roderic A Grupen, and Andrew G Barto · 2013
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A benchmark of kriging-based infill criteria for noisy optimization
Victor Picheny, Tobias Wagner, and David Ginsbourger · 2013
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Heteroscedastic treed Bayesian optimisation
John-Alexander M Assael, Ziyu Wang, Bobak Shahriari, and Nando de Freitas · 2014
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Expensive multiobjective optimization for robotics with consideration of heteroscedastic noise
Ryo Ariizumi, Matthew Tesch, Howie Choset, and Fumitoshi Matsuno · 2014
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Freesolv: a database of experimental and calculated hydration free energies, with input files
David L Mobley and J Peter Guthrie · 2014
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What is high-throughput virtual screening? a perspective from organic materials discovery
Edward O Pyzer-Knapp, Changwon Suh, Rafael Gómez-Bombarelli, Jorge Aguilera-Iparraguirre, and Alán Aspuru-Guzik · 2015
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Safe exploration for optimization with Gaussian processes
Yanan Sui, Alkis Gotovos, Joel Burdick, and Andreas Krause · 2015
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Bayesian optimization for learning gaits under uncertainty
Roberto Calandra, André Seyfarth, Jan Peters, and Marc Peter Deisenroth · 2016
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Bayesian optimization with safety constraints: safe and automatic parameter tuning in robotics
Felix Berkenkamp, Andreas Krause, and Angela P Schoellig · 2016
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What uncertainties do we need in Bayesian deep learning for computer vision?
Alex Kendall and Yarin Gal · 2017
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Approaches for calculating solvation free energies and enthalpies demonstrated with an update of the freesolv database
Guilherme Duarte Ramos Matos, Daisy Y Kyu, Hannes H Loeffler, John D Chodera, Michael R Shirts, and David L Mobley · 2017
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Parallel and distributed Thompson sampling for large-scale accelerated exploration of chemical space
José Miguel Hernández-Lobato, James Requeima, Edward O Pyzer-Knapp, and Alán Aspuru-Guzik · 2017
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Bayesian Modeling for Optimization and Control in Robotics
Roberto Calandra · 2017
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Heteroscedastic Gaussian processes for uncertain and incomplete data
Ibrahim Almosallam · 2017
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Optimizing molecules using efficient queries from property evaluations
Samuel Hoffman, Vijil Chenthamarakshan, Kahini Wadhawan, Pin-Yu Chen, and Payel Das · 2020
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Florian Hase, Loic M Roch, and Alan Aspuru-Guzik · 2020
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Pushing property limits in materials discovery via boundless objective-free exploration
Kei Terayama, Masato Sumita, Ryo Tamura, Daniel T Payne, Mandeep K Chahal, Shinsuke Ishihara, and Koji Tsuda · 2020
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Solvent selection for Mitsunobu reaction driven by an active learning surrogate model
Chonghuan Zhang, Yehia Amar, Liwei Cao, and Alexei A. Lapkin · 2020
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Boss: Bayesian optimization over string spaces
Henry Moss, David Leslie, Daniel Beck, Javier Gonzalez, and Paul Rayson · 2020
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Zilong Wang and Marianthi Ierapetritou · 2017
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Automatic chemical design using a data-driven continuous representation of molecules
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
Cited alongside, same era.
Bayesian multiobjective optimisation with mixed analytical and black-box functions: Application to tissue engineering
Simon Olofsson, Mohammad Mehrian, Roberto Calandra, Liesbet Geris, Marc Peter Deisenroth, and Ruth Misener · 2018
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Dataset bias in the natural sciences: A case study in chemical reaction prediction and synthesis design
Ryan-Rhys Griffiths, Philippe Schwaller, and Alpha A. Lee · 2018
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A global dataset of plant available and unavailable phosphorus in natural soils derived by Hedley method
Enqing Hou, Xiang Tan, Marijke Heenan, and Dazhi Wen · 2018
Cited alongside, same era.
Practical heteroscedastic Gaussian process modeling for large simulation experiments
Mickael Binois, Robert B Gramacy, and Mike Ludkovski · 2018
Cited alongside, same era.
Heteroscedastic Gaussian processes for uncertainty modeling in large-scale crowdsourced traffic data
Filipe Rodrigues and Francisco C Pereira · 2018
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Array programming with NumPy
Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, Robert Kern, Matti Picus, Stephan Hoyer, Marten H. van Kerkwijk, Matthew Brett, Allan Haldane, Jaime Fernández del Río, Mark Wiebe, Pearu Peterson, Pierre Gérard-Marchant, Kevin Sheppard, Tyler Reddy, Warren Weckesser, Hameer Abbasi, Christoph Gohlke, and Travis E. Oliphant · 2020
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Improved most likely heteroscedastic Gaussian process regression via Bayesian residual moment estimator
Qiu-Hu Zhang and Yi-Qing Ni · 2020
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Probabilistic modelling of wind turbine power curves with application of heteroscedastic Gaussian process regression
TJ Rogers, P Gardner, N Dervilis, K Worden, AE Maguire, E Papatheou, and EJ Cross · 2020
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Large-scale heteroscedastic regression via Gaussian process
Haitao Liu, Yew-Soon Ong, and Jianfei Cai · 2020
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Efficiently sampling functions from Gaussian process posteriors
James T. Wilson, Viacheslav Borovitskiy, Alexander Terenin, Peter Mostowsky, and Marc Peter Deisenroth · 2020
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High-dimensional Bayesian optimization using low-dimensional feature spaces
Riccardo Moriconi, Marc Peter Deisenroth, and KS Sesh Kumar · 2020
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BoTorch: A framework for efficient Monte-Carlo Bayesian optimization
Maximilian Balandat, Brian Karrer, Daniel Jiang, Samuel Daulton, Ben Letham, Andrew G Wilson, and Eytan Bakshy · 2020
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Tuning hyperparameters without grad students: Scalable and robust Bayesian optimisation with dragonfly
Kirthevasan Kandasamy, Karun Raju Vysyaraju, Willie Neiswanger, Biswajit Paria, Christopher R. Collins, Jeff Schneider, Barnabas Poczos, and Eric P. Xing · 2020
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Are we forgetting about compositional optimisers in Bayesian optimisation?
Antoine Grosnit, Alexander I Cowen-Rivers, Rasul Tutunov, Ryan-Rhys Griffiths, Jun Wang, and Haitham Bou-Ammar · 2020
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Compositional adam: An adaptive compositional solver
Rasul Tutunov, Minne Li, Alexander I Cowen-Rivers, Jun Wang, and Haitham Bou-Ammar · 2020
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Global optimization of Gaussian processes
Artur M Schweidtmann, Dominik Bongartz, Daniel Grothe, Tim Kerkenhoff, Xiaopeng Lin, Jaromil Najman, and Alexander Mitsos · 2020
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A robust approach to warped Gaussian process-constrained optimization
Johannes Wiebe, Inês Cecílio, Jonathan Dunlop, and Ruth Misener · 2020
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An empirical study of assumptions in Bayesian optimisation
Alexander I Cowen-Rivers, Wenlong Lyu, Rasul Tutunov, Zhi Wang, Antoine Grosnit, Ryan Rhys Griffiths, Hao Jianye, Jun Wang, and Haitham Bou Ammar · 2020
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Gaussian process molecule property prediction with FlowMO
Henry B Moss and Ryan-Rhys Griffiths · 2020
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Aditya R Thawani, Ryan-Rhys Griffiths, Arian Jamasb, Anthony Bourached, Penelope Jones, William McCorkindale, Alexander A Aldrick, and Alpha A Lee · 2020
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Mapping materials and molecules
Bingqing Cheng, Ryan-Rhys Griffiths, Simon Wengert, Christian Kunkel, Tamas Stenczel, Bonan Zhu, Volker L Deringer, Noam Bernstein, Johannes T Margraf, Karsten Reuter, et al · 2020
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Olympus: a benchmarking framework for noisy optimization and experiment planning
Florian Hase, Matteo Aldeghi, Riley Hickman, Loic Roch, Elena Liles, Melodie Christensen, Jason Hein, and Alán Aspuru-Guzik · 2021
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High-dimensional Bayesian optimisation with variational autoencoders and deep metric learning
Antoine Grosnit, Rasul Tutunov, Alexandre Max Maraval, Ryan-Rhys Griffiths, Alexander I Cowen-Rivers, Lin Yang, Lin Zhu, Wenlong Lyu, Zhitang Chen, Jun Wang, et al · 2021
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Modeling the multiwavelength variability of mrk 335 using Gaussian processes
Ryan-Rhys Griffiths, Jiachen Jiang, Douglas JK Buisson, Dan Wilkins, Luigi C Gallo, Adam Ingram, Dirk Grupe, Erin Kara, Michael L Parker, William Alston, et al · 2021
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