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Found 6 papers, 0 papers with code
Trajectory-Based Multi-Objective Hyperparameter Optimization for Model Retraining
Training machine learning models inherently involves a resource-intensive and noisy iterative learning procedure that allows epoch-wise monitoring of the model performance.
Minimizing UCB: a Better Local Search Strategy in Local Bayesian Optimization
Through this insight, we propose a new local Bayesian optimization algorithm, MinUCB, which replaces the gradient descent step with minimizing UCB in GIBO.
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.
