L Papenmeier, L Nardi… - Advances in Neural …, 2022 - proceedings.neurips.cc
Recent advances have extended the scope of Bayesian optimization (BO) to expensive-to- evaluate black-box functions with dozens of dimensions, aspiring to unlock impactful …
Bayesian optimization (BO) is a popular paradigm for global optimization of expensive black- box functions, but there are many domains where the function is not completely a black-box …
M Zhang, H Li, S Su - arXiv preprint arXiv:1907.08953, 2019 - arxiv.org
Bayesian optimization (BO) has been broadly applied to computational expensive problems, but it is still challenging to extend BO to high dimensions. Existing works are usually under …
Scaling Bayesian optimization to high dimensions is challenging task as the global optimization of high-dimensional acquisition function can be expensive and often infeasible …
Bayesian Optimisation (BO) refers to a suite of techniques for global optimisation of expensive black box functions, which use introspective Bayesian models of the function to …
L Song, K Xue, X Huang… - Advances in Neural …, 2022 - proceedings.neurips.cc
Bayesian optimization (BO) is a class of popular methods for expensive black-box optimization, and has been widely applied to many scenarios. However, BO suffers from the …
Bayesian Optimization (BO) is an efficient method to optimize an expensive black-box function with continuous variables. However, in many cases, the function has only discrete …
Bayesian optimization (BO) is a powerful approach for seeking the global optimum of expensive black-box functions and has proven successful for fine tuning hyper-parameters …
Applying Bayesian optimization in problems wherein the search space is unknown is challenging. To address this problem, we propose a systematic volume expansion strategy …