Results 31 to 40 of about 56,129 (265)
Bayesian optimization on networks
Journal of Computational PhysicsThis paper studies optimization on networks modeled as metric graphs. Motivated by applications where the objective function is expensive to evaluate or only available as a black box, we develop Bayesian optimization algorithms that sequentially update a Gaussian process surrogate model of the objective to guide the acquisition of query points.
W. Li, D. Sanz-Alonso, R. Yang
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Bayesian Optimization with Expensive Integrands
SIAM Journal on Optimization, 2022 We propose a Bayesian optimization algorithm for objective functions that are sums or integrals of expensive-to-evaluate functions, allowing noisy evaluations. These objective functions arise in multi-task Bayesian optimization for tuning machine learning hyperparameters, optimization via simulation, and sequential design of experiments with random ...
Saul Toscano-Palmerin, Peter I. Frazier
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Exploratory Landscape Validation for Bayesian Optimization Algorithms
MathematicsBayesian optimization algorithms are widely used for solving problems with a high computational complexity in terms of objective function evaluation. The efficiency of Bayesian optimization is strongly dependent on the quality of the surrogate models of ...
Taleh Agasiev, Anatoly Karpenko
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MethodsX, 2019 Accurate and reliable predictions of bacterial growth and metabolism from unstructured kinetic models are critical to the proper operation and design of engineered biological treatment and remediation systems. As such, parameter estimation has progressed
Derek C. Manheim, Russell L. Detwiler
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Bayesian Optimization for Conformer Generation [PDF]
Journal of Cheminformatics, 2018 Generating low-energy molecular conformers is a key task for many areas of computational chemistry, molecular modeling and cheminformatics. Most current conformer generation methods primarily focus on generating geometrically diverse conformers rather than finding the most probable or energetically lowest minima.
Lucian Chan +2 more
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FEBS Open Bio, EarlyView.This protocol paper outlines methods to establish the success of a time‐resolved serial crystallographic experiment, by means of statistical analysis of timepoint data in reciprocal space and models in real space. We show how to amplify the signal from excited states to visualise structural changes in successful experiments.
Jake Hill +4 more
wiley +1 more source
Bayesian Optimization Based on K-Optimality [PDF]
Entropy, 2018 Bayesian optimization (BO) based on the Gaussian process (GP) surrogate model has attracted extensive attention in the field of optimization and design of experiments (DoE). It usually faces two problems: the unstable GP prediction due to the ill-conditioned Gram matrix of the kernel and the difficulty of determining the trade-off parameter between ...
Liang Yan 0003 +3 more
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Functional Causal Bayesian Optimization
CoRR, 2023 We propose functional causal Bayesian optimization (fCBO), a method for finding interventions that optimize a target variable in a known causal graph. fCBO extends the CBO family of methods to enable functional interventions, which set a variable to be a deterministic function of other variables in the graph.
Limor Gultchin +3 more
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FEBS Open Bio, EarlyView.Evolutionary analysis across 32 placental mammals identified positive selection at residues H148 and W149 in the immune receptor FcγR1. Ancestral reconstruction combined with molecular dynamics simulations reveals how these mutations may influence receptor structure and dynamics, providing insight into the evolution of antibody recognition and immune ...
David A. Young +7 more
wiley +1 more source