Bayesian approach to global optimization and application to multiobjective and constrained problems

By Jonas Mockus, L. Mockus. 1991

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Citations in the corpus

Neural Architecture Search with Bayesian Optimisation and Optimal Transport (2018, neutral): “The expected improvement acquisition [28],”
A Tutorial on Bayesian Optimization (2018, background): “…literature include multi-ﬁdelity optimization (Huang et al., 2006; S´obester et al., 2004), multi-objective optimization (Keane, 2006; Knowles, 2006; Moˇckus and Moˇckus, 1991), and a study of convergence rates (Calvin, 1997; Calvin and ˇZilinskas, 2000; Calvin and ˇZilinskas, 2005; Calvin and…”
Generating Adversarial Driving Scenarios in High-Fidelity Simulators (2019, background): “Bayesian optimization (BO) [6], [7], [8] is an approach for finding global optima of a noisy black box function.”
A Tabu-Search Hyperheuristic for Timetabling and Rostering (2003, background): “who advocated a Bayesian mechanism for searching between low-level heuristics [19, 20, 23].”
In Silico Predictive Modeling of CRISPR/Cas9 guide efficiency (2015, background): “35 We investigated only on-target predictions, whereas in reality, for some applications, prediction of off-target effects are just as important.”
Learnable Embedding Space for Efficient Neural Architecture Compression (2019, background): “The expected improvement function EI t ( x ) (Mockus & Mockus, 1991) measures the expected improvement over the current maximum value according to the posterior distribution: where E t indicates the expectation is taken with respect to the posterior distribution at step t p ( f ( x ) | f ( x 1: t…”