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A Novel Framework for Improving the Breakdown Point of Robust Regression Algorithms
Finally, we demonstrate that the breakdown point of CORALS is indeed higher than that of the algorithm from which it is derived.
A model aggregation approach for high-dimensional large-scale optimization
In this study, we propose a model aggregation method in the Bayesian optimization (MamBO) algorithm for efficiently solving high-dimensional large-scale optimization problems.
Adjusted Expected Improvement for Cumulative Regret Minimization in Noisy Bayesian Optimization
To adapt the EI for better performance under cumulative regret, we introduce a novel quantity called the evaluation cost which is compared against the acquisition function, and with this, develop the expected improvement-cost (EIC) algorithm.
Combined Global and Local Search for Optimization with Gaussian Process Models
Based on this AGLGP model, we propose a Combined Global and Local search for Optimization (CGLO) algorithm.
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