Bayesian optimization in high-dimensional spaces: A brief survey
Bayesian optimization (BO) has been widely applied to several modern science and
engineering applications such as machine learning, neural networks, robotics, aerospace …
engineering applications such as machine learning, neural networks, robotics, aerospace …
Increasing the scope as you learn: Adaptive Bayesian optimization in nested subspaces
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 …
evaluate black-box functions with dozens of dimensions, aspiring to unlock impactful …
Deep learning for Bayesian optimization of scientific problems with high-dimensional structure
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 …
box functions, but there are many domains where the function is not completely a black-box …
High dimensional Bayesian optimization via supervised dimension reduction
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 …
but it is still challenging to extend BO to high dimensions. Existing works are usually under …
High dimensional Bayesian optimization using dropout
Scaling Bayesian optimization to high dimensions is challenging task as the global
optimization of high-dimensional acquisition function can be expensive and often infeasible …
optimization of high-dimensional acquisition function can be expensive and often infeasible …
Tuning hyperparameters without grad students: Scalable and robust bayesian optimisation with dragonfly
K Kandasamy, KR Vysyaraju, W Neiswanger… - Journal of Machine …, 2020 - jmlr.org
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 …
expensive black box functions, which use introspective Bayesian models of the function to …
Monte carlo tree search based variable selection for high dimensional bayesian optimization
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 …
optimization, and has been widely applied to many scenarios. However, BO suffers from the …
Bayesian optimization with discrete variables
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 …
function with continuous variables. However, in many cases, the function has only discrete …
High-dimensional Bayesian optimization using low-dimensional feature spaces
R Moriconi, MP Deisenroth, KS Sesh Kumar - Machine Learning, 2020 - Springer
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 …
expensive black-box functions and has proven successful for fine tuning hyper-parameters …
Bayesian optimization with unknown search space
Applying Bayesian optimization in problems wherein the search space is unknown is
challenging. To address this problem, we propose a systematic volume expansion strategy …
challenging. To address this problem, we propose a systematic volume expansion strategy …