Fetching the paper…
Reading the bibliography…
Numerous challenges in science and engineering can be framed as optimization tasks, including the maximization of reaction yields, the optimization of molecular and materials properties, and the fine-tuning of automated hardware protocols.
Linear programming under uncertainty
George B. Dantzig · 1955
Earlier work this paper cites.
Genetic algorithms: a survey
M. Srinivas and L. M. Patnaik · 1994
Earlier work this paper cites.
Bayesian analysis of errors-in-variables regression models
Petros Dellaportas and David A. Stephens · 1995
Earlier work this paper cites.
Robust and Optimal Control
Kemin Zhou, John C. Doyle, and Keith Glover · 1996
Earlier work this paper cites.
Comparing Probabilistic and Fuzzy Set Approaches for Designing in the Presence of Uncertainty
Qinghong Chen · 2000
Earlier work this paper cites.
Random Forests
Leo Breiman · 2001
Earlier work this paper cites.
Random Forests
Leo Breiman · 2001
Earlier work this paper cites.
The scaled unscented transformation
S. J. Julier · 2002
Earlier work this paper cites.
Learning a Gaussian Process Model with Uncertain Inputs
Agathe Girard and Roderick Murray-Smith · 2003
Earlier work this paper cites.
Genetic algorithms for modelling and optimisation
John McCall · 2005
Earlier work this paper cites.
Extremely randomized trees
Pierre Geurts, Damien Ernst, and Louis Wehenkel · 2006
Earlier work this paper cites.
Extremely randomized trees
Pierre Geurts, Damien Ernst, and Louis Wehenkel · 2006
Earlier work this paper cites.
A discrete analogue of the laplace distribution
Seidu Inusah and Tomasz J. Kozubowski · 2006
Earlier work this paper cites.
Robust optimization - A comprehensive survey
Hans Georg Beyer and Bernhard Sendhoff · 2007
Earlier work this paper cites.
Robust optimization - A comprehensive survey
Hans Georg Beyer and Bernhard Sendhoff · 2007
Earlier work this paper cites.
Genetic Algorithms
Mitsuo Gen and Lin Lin · 2008
Earlier work this paper cites.
Simulating sensitivities of conditional value at risk
L. Jeff Hong and Guangwu Liu · 2009
Earlier work this paper cites.
Robust Optimization for Unconstrained Simulation-Based Problems
Dimitris Bertsimas, Omid Nohadani, and Kwong Meng Teo · 2009
Earlier work this paper cites.
Robust Optimization for Unconstrained Simulation-Based Problems
Dimitris Bertsimas, Omid Nohadani, and Kwong Meng Teo · 2009
Earlier work this paper cites.
Theory and applications of robust optimization
Dimitris Bertsimas, David B. Brown, and Constantine Caramanis · 2011
Earlier work this paper cites.
Scikit-learn: Machine learning in Python
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay · 2011
Earlier work this paper cites.
Fuzzy programming approaches to robust optimization
Masahiro Inuiguchi · 2012
Earlier work this paper cites.
DEAP: Evolutionary algorithms made easy
Félix-Antoine Fortin, François-Michel De Rainville, Marc-André Gardner, Marc Parizeau, and Christian Gagné · 2012
Earlier work this paper cites.
Multi-objective optimization methods in drug design
Christos A. Nicolaou and Nathan Brown · 2013
Earlier work this paper cites.
Making a science of model search: Hyperparameter optimization in hundreds of dimensions for vision architectures
J. S. Bergstra, D. Yamins, and D. D. Cox · 2013
Earlier work this paper cites.
George Box and robust design
Stephen P. Jones · 2014
Earlier work this paper cites.
Variational Inference for Uncertainty on the Inputs of Gaussian Process Models
Andreas C. Damianou, Michalis K. Titsias, and Neil D Lawrence · 2014
Cited alongside, same era.
Taking the Human Out of the Loop: A Review of Bayesian Optimization Bobak
Bobak Shahriari, Kevin Swersky, Ziyu Wang, Ryan P Adams, and Nando De Freitas · 2015
Cited alongside, same era.
Autonomy in materials research: a case study in carbon nanotube growth
Pavel Nikolaev, Daylond Hooper, Frederick Webber, Rahul Rao, Kevin Decker, Michael Krein, Jason Poleski, Rick Barto, and Benji Maruyama · 2016
Cited alongside, same era.
Unscented bayesian optimization for safe robot grasping
J. Nogueira, R. Martinez-Cantin, A. Bernardino, and L. Jamone · 2016
Cited alongside, same era.
Gpyopt: A bayesian optimization framework in python
The GPyOpt authors · 2016
Cited alongside, same era.
Autonomous experimentation applied to carbon nanotube synthesis
Autonomous discovery in the chemical sciences part i: Progress
Connor W. Coley, Natalie S. Eyke, and Klavs F. Jensen · 2020
Later among the works it cites.
Autonomous discovery in the chemical sciences part ii: Outlook
Connor W. Coley, Natalie S. Eyke, and Klavs F. Jensen · 2020
Later among the works it cites.
Materials acceleration platforms: On the way to autonomous experimentation
Martha M. Flores-Leonar, Luis M. Mejía-Mendoza, Andrés Aguilar-Granda, Benjamin Sanchez-Lengeling, Hermann Tribukait, Carlos Amador-Bedolla, and Alán Aspuru-Guzik · 2020
Later among the works it cites.
Self-driving laboratory for accelerated discovery of thin-film materials
B. P. MacLeod, F. G. L. Parlane, T. D. Morrissey, F. Häse, L. M. Roch, K. E. Dettelbach, R. Moreira, L. P. E. Yunker, M. B. Rooney, J. R. Deeth, V. Lai, G. J. Ng, H. Situ, R. H. Zhang, M. S. Elliott, T. H. Haley, D. J. Dvorak, A. Aspuru-Guzik, J. E. Hein, and C. P. Berlinguette · 2020
Later among the works it cites.
Beyond Ternary OPV: High-Throughput Experimentation and Self-Driving Laboratories Optimize Multicomponent Systems
Stefan Langner, Florian Häse, José Darío Perea, Tobias Stubhan, Jens Hauch, Loïc M Roch, Thomas Heumueller, Alán Aspuru-Guzik, and Christoph J Brabec · 2020
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Benji Maruyama, Kevin Decker, Michael Krein, Jason Poleski, Rick Barto, Fred Webber, and Pavel Nikolaev · 2017
Cited alongside, same era.
Bayesian simulation optimization with input uncertainty
M. Pearce and J. Branke · 2017
Cited alongside, same era.
Bayesian Optimization Under Uncertainty
Justin J Beland and Prasanth B Nair · 2017
Cited alongside, same era.
Smac v3: Algorithm configuration in python
Marius Lindauer, Katharina Eggensperger, Matthias Feurer, Stefan Falkner, André Biedenkapp, and Frank Hutter · 2017
Cited alongside, same era.
Bayesian Optimization Under Uncertainty
Justin J Beland and Prasanth B Nair · 2017
Cited alongside, same era.
Automatic chemical design using a data-driven continuous representation of molecules
Rafael Gómez-Bombarelli, Jennifer N. Wei, David Duvenaud, JoséMiguel 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.
Controlling an organic synthesis robot with machine learning to search for new reactivity
Jarosław M Granda, Liva Donina, Vincenza Dragone, De-Liang Long, and Leroy Cronin · 2018
Cited alongside, same era.
Later among the works it cites.
A curious formulation robot enables the discovery of a novel protocell behavior
Jonathan Grizou, Laurie J Points, Abhishek Sharma, and Leroy Cronin · 2020
Later among the works it cites.
Olympus: a benchmarking framework for noisy optimization and experiment planning, 2020
Florian Häse, Matteo Aldeghi, Riley J. Hickman, Loïc M. Roch, Melodie Christensen, Elena Liles, Jason E. Hein, and Alán Aspuru-Guzik · 2020
Later among the works it cites.
Summit: Benchmarking Machine Learning Methods for Reaction Optimisation
Kobi Felton, Jan Rittig, and Alexei Lapkin · 2020
Later among the works it cites.
Benchmarking the acceleration of materials discovery by sequential learning
Brian Rohr, Helge S. Stein, Dan Guevarra, Yu Wang, Joel A. Haber, Muratahan Aykol, Santosh K. Suram, and John M. Gregoire · 2020
Later among the works it cites.
A mobile robotic chemist
Benjamin Burger, Phillip M. Maffettone, Vladimir V. Gusev, Catherine M. Aitchison, Yang Bai, Xiaoyan Wang, Xiaobo Li, Ben M. Alston, Buyi Li, Rob Clowes, Nicola Rankin, Brandon Harris, Reiner Sebastian Sprick, and Andrew I. Cooper · 2020
Later among the works it cites.
A bayesian experimental autonomous researcher for mechanical design
Aldair E. Gongora, Bowen Xu, Wyatt Perry, Chika Okoye, Patrick Riley, Kristofer G. Reyes, Elise F. Morgan, and Keith A. Brown · 2020
Later among the works it cites.
Bayesian optimization of risk measures
Sait Cakmak, Raul Astudillo Marban, Peter Frazier, and Enlu Zhou · 2020
Later among the works it cites.
Noisy-input entropy search for efficient robust bayesian optimization, 2020
Lukas P. Fröhlich, Edgar D. Klenske, Julia Vinogradska, Christian Daniel, and Melanie N. Zeilinger · 2020
Later among the works it cites.
Dealing with categorical and integer-valued variables in Bayesian Optimization with Gaussian processes
Eduardo C Garrido-Merchán and Daniel Hernández-Lobato · 2020
Later among the works it cites.
ENTMOOT: A Framework for Optimization over Ensemble Tree Models
Miten Mistry Robert M. Lee Nathan Sudermann-Merx Alexander Thebelt, Jan Kronqvist and Ruth Misener · 2020
Later among the works it cites.
Augmenting genetic algorithms with deep neural networks for exploring the chemical space, 2020
AkshatKumar Nigam, Pascal Friederich, Mario Krenn, and Alán Aspuru-Guzik · 2020
Later among the works it cites.
Chemos: An orchestration software to democratize autonomous discovery
Loïc M. Roch, Florian Häse, Christoph Kreisbeck, Teresa Tamayo-Mendoza, Lars P. E. Yunker, Jason E. Hein, and Alán Aspuru-Guzik · 2020
Later among the works it cites.
Olympus: a benchmarking framework for noisy optimization and experiment planning, 2020
Florian Häse, Matteo Aldeghi, Riley J. Hickman, Loïc M. Roch, Melodie Christensen, Elena Liles, Jason E. Hein, and Alán Aspuru-Guzik · 2020
Later among the works it cites.
Data-science driven autonomous process optimization
Melodie Christensen, Lars Yunker, Folarin Adedeji, Florian Häse, Loïc Roch, Tobias Gensch, Gabriel dos Passos Gomes, Tara Zepel, Matthew Sigman, Alán Aspuru-Guzik, and Jason Hein · 2021
Closest in time.
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
Closest in time.
Inverse design of nanoporous crystalline reticular materials with deep generative models
Zhenpeng Yao, Benjamín Sánchez-Lengeling, N. Scott Bobbitt, Benjamin J. Bucior, Sai Govind Hari Kumar, Sean P. Collins, Thomas Burns, Tom K. Woo, Omar K. Farha, Randall Q. Snurr, and Alán Aspuru-Guzik · 2021
Closest in time.
Self-driving platform for metal nanoparticle synthesis: Combining microfluidics and machine learning
Huachen Tao, Tianyi Wu, Sina Kheiri, Matteo Aldeghi, Alán Aspuru-Guzik, and Eugenia Kumacheva · 2021
Closest in time.
Gryffin: An algorithm for bayesian optimization of categorical variables informed by expert knowledge
Florian Häse, Matteo Aldeghi, Riley J. Hickman, Loïc M. Roch, and Alán Aspuru-Guzik · 2021
Closest in time.
Golem: An algorithm for robust experiment and process optimization
M. Aldeghi, F. Häse, R.J. Hickman, I. Tamblyn, and A. Aspuru-Guzik · 2021
Closest in time.
Golem: An algorithm for robust experiment and process optimization
M. Aldeghi, F. Häse, R.J. Hickman, I. Tamblyn, and A. Aspuru-Guzik · 2021
Closest in time.