Optimization of time-consuming experimental conditions using pseudo-experimental data guided by adaptive polynomial regression | Paper Citation Record

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This paper cites Scientific discovery in the age of artificial intelligence.Nature, 620(7972):47–60, 2023.

Optimization of time-consuming experimental conditions using pseudo-experimental data guided by adaptive polynomial regression Scientific discovery in the age of artificial intelligence.Nature, 620(7972):47–60, 2023

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Optimization of time-consuming experimental conditions using pseudo-experimental data guided by adaptive polynomial regression Unresolved cited work

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This paper cites Schmid, Sterling G.

Optimization of time-consuming experimental conditions using pseudo-experimental data guided by adaptive polynomial regression Schmid, Sterling G

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Optimization of time-consuming experimental conditions using pseudo-experimental data guided by adaptive polynomial regression Directedevolution: bringingnewchemistrytolife.AngewandteChemie(InternationalEd.in English), 57(16):4143, 2017

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Optimization of time-consuming experimental conditions using pseudo-experimental data guided by adaptive polynomial regression Exploring cost reduction strategies for serum free media development

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Optimization of time-consuming experimental conditions using pseudo-experimental data guided by adaptive polynomial regression Biopharmaceutical benchmarks 2022.Nature Biotechnology, 40(12):1722–1760, 2022

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Optimization of time-consuming experimental conditions using pseudo-experimental data guided by adaptive polynomial regression Employing active learning in the optimization of culture medium for mammalian cells.npj Systems Biology and Applications, 9(1):20, 2023

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Optimization of time-consuming experimental conditions using pseudo-experimental data guided by adaptive polynomial regression Talanta, 76(5):965–977, 2008

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This paper cites Stogios, Margrethe Therkildsen, and Martin Krøyer Rasmussen.

Optimization of time-consuming experimental conditions using pseudo-experimental data guided by adaptive polynomial regression Stogios, Margrethe Therkildsen, and Martin Krøyer Rasmussen

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Optimization of time-consuming experimental conditions using pseudo-experimental data guided by adaptive polynomial regression Frazier, Keith Baar, and David E

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Optimization of time-consuming experimental conditions using pseudo-experimental data guided by adaptive polynomial regression Challengesindevelopingcellculturemediausingmachinelearning

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Optimization of time-consuming experimental conditions using pseudo-experimental data guided by adaptive polynomial regression IntegrationofBayesianoptimizationandsolutionthermodynamicstooptimizemediadesignformammalian biomanufacturing.iScience, 28(8):112944, 2025

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Optimization of time-consuming experimental conditions using pseudo-experimental data guided by adaptive polynomial regression Policy optimization in dynamic Bayesian network hybrid models of biomanufacturing processes.INFORMS Journal on Computing, 35(1):66–82, 2023

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Optimization of time-consuming experimental conditions using pseudo-experimental data guided by adaptive polynomial regression Cambridge University Press, 2023

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Optimization of time-consuming experimental conditions using pseudo-experimental data guided by adaptive polynomial regression Machine-learning-guideddirectedevolutionforprotein engineering.Nature Methods, 16(8):687–694, 2019

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Optimization of time-consuming experimental conditions using pseudo-experimental data guided by adaptive polynomial regression Protein Engineering via Bayesian Optimization-Guided Evolutionary Algorithm and Robotic Experiments.Briefings in Bioinformat- ics, 24(1):bbac570, 2022

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Optimization of time-consuming experimental conditions using pseudo-experimental data guided by adaptive polynomial regression Bayesian Optimization in Bioprocess Engineering—Where Do We Stand Today?Biotechnology and Bioengineering, 122(6):1313–1325, 2025

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Optimization of time-consuming experimental conditions using pseudo-experimental data guided by adaptive polynomial regression Recent Advances in Bayesian Optimization

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Optimization of time-consuming experimental conditions using pseudo-experimental data guided by adaptive polynomial regression Hinckley, Rachel Barry, Brendan Dang, Lenna A

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Optimization of time-consuming experimental conditions using pseudo-experimental data guided by adaptive polynomial regression High Dimensional Bayesian Optimisation and Bandits via Additive Models

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Optimization of time-consuming experimental conditions using pseudo-experimental data guided by adaptive polynomial regression Sample-efficientoptimizationinthelatent space of deep generative models via weighted retraining

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Optimization of time-consuming experimental conditions using pseudo-experimental data guided by adaptive polynomial regression Wei, David Duvenaud, José Miguel Hernández-Lobato, Benjamín Sánchez-Lengeling,DennisSheberla,JorgeAguilera-Iparraguirre,TimothyD.Hirzel,RyanP.Adams,andAlán 16 Aspuru-Guzik

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Optimization of time-consuming experimental conditions using pseudo-experimental data guided by adaptive polynomial regression High-Dimensional Bayesian Optimisation with Variational Autoencoders and Deep Metric Learning

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Optimization of time-consuming experimental conditions using pseudo-experimental data guided by adaptive polynomial regression Auto-Encoding Variational Bayes

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Optimization of time-consuming experimental conditions using pseudo-experimental data guided by adaptive polynomial regression Development of the membrane ceiling method for in vitro spermatogenesis.Scientific Reports, 15(1):625, Jan 2025

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Optimization of time-consuming experimental conditions using pseudo-experimental data guided by adaptive polynomial regression Mater., 37(17):6629–6641, 2025

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Optimization of time-consuming experimental conditions using pseudo-experimental data guided by adaptive polynomial regression High-DimensionalBayesianOptimizationviaSemi-SupervisedLearning withOptimizedUnlabeledDataSampling

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Optimization of time-consuming experimental conditions using pseudo-experimental data guided by adaptive polynomial regression Bayesian optimization using pseudo-points

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Optimization of time-consuming experimental conditions using pseudo-experimental data guided by adaptive polynomial regression Real-Parameter Black-Box Optimization Benchmarking 2009: Noiseless Functions Definitions

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Optimization of time-consuming experimental conditions using pseudo-experimental data guided by adaptive polynomial regression Močkus.On Bayesian Methods for Seeking the Extremum, pages 400–404

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Optimization of time-consuming experimental conditions using pseudo-experimental data guided by adaptive polynomial regression Gaussian process optimization in the bandit setting: no regret and experimental design

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Optimization of time-consuming experimental conditions using pseudo-experimental data guided by adaptive polynomial regression A limited memory algorithm for bound constrained optimization.SIAM Journal on scientific computing, 16(5):1190–1208, 1995

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Optimization of time-consuming experimental conditions using pseudo-experimental data guided by adaptive polynomial regression Hansen, A

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Optimization of time-consuming experimental conditions using pseudo-experimental data guided by adaptive polynomial regression Salmon, Mark A

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Optimization of time-consuming experimental conditions using pseudo-experimental data guided by adaptive polynomial regression Yamada, and Akira Funahashi

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Optimization of time-consuming experimental conditions using pseudo-experimental data guided by adaptive polynomial regression Visualizing data using t-SNE.Journal of machine learning research, 9(Nov):2579–2605, 2008

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Optimization of time-consuming experimental conditions using pseudo-experimental data guided by adaptive polynomial regression UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction

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Optimization of time-consuming experimental conditions using pseudo-experimental data guided by adaptive polynomial regression Exploring Exploration in Bayesian Optimization

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Optimization of time-consuming experimental conditions using pseudo-experimental data guided by adaptive polynomial regression Unleashing the Potential of Acquisition Functions in High-Dimensional Bayesian Optimization.Transactions on Machine Learning Research, 2024

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Optimization of time-consuming experimental conditions using pseudo-experimental data guided by adaptive polynomial regression Unlocking the black box beyond Bayesian global optimization for materials design using reinforcement learning.npj Computational Materials, 11(1):143, 2025

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Optimization of time-consuming experimental conditions using pseudo-experimental data guided by adaptive polynomial regression A neural network model for high entropy alloy design.npj Computational Materials, 9(1):60, 2023

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Optimization of time-consuming experimental conditions using pseudo-experimental data guided by adaptive polynomial regression Multi-fidelity gaussian process bandit optimisation.Journal of Artificial Intelligence Research, 66:151–196, 2019

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Optimization of time-consuming experimental conditions using pseudo-experimental data guided by adaptive polynomial regression Multi-fidelity cost- aware Bayesian optimization.Computer Methods in Applied Mechanics and Engineering, 407:115937, 2023

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Optimization of time-consuming experimental conditions using pseudo-experimental data guided by adaptive polynomial regression Multifidelity Bayesian Optimization: A Review.AIAA Journal, 63(6):2286–2322, 2025

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