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Tree-structured parzen estimator: Understanding its algorithm components and their roles for better empirical performance
S Watanabe - arxiv preprint arxiv:2304.11127, 2023 - arxiv.org
Recent advances in many domains require more and more complicated experiment design.
Such complicated experiments often have many parameters, which necessitate parameter …
Such complicated experiments often have many parameters, which necessitate parameter …
Bayesian optimisation against climate change: Applications and benchmarks
Bayesian optimisation is a powerful method for optimising black-box functions, popular in
settings where the true function is expensive to evaluate and no gradient information is …
settings where the true function is expensive to evaluate and no gradient information is …
Variational Search Distributions
We develop variational search distributions (VSD), a method for finding discrete,
combinatorial designs of a rare desired class in a batch sequential manner with a fixed …
combinatorial designs of a rare desired class in a batch sequential manner with a fixed …
MALIBO: Meta-learning for likelihood-free Bayesian optimization
Bayesian optimization (BO) is a popular method to optimize costly black-box functions. While
traditional BO optimizes each new target task from scratch, meta-learning has emerged as a …
traditional BO optimizes each new target task from scratch, meta-learning has emerged as a …
Bayesian Optimization by Kernel Regression and Density-based Exploration
Bayesian optimization is highly effective for optimizing expensive-to-evaluate black-box
functions, but it faces significant computational challenges due to the high computational …
functions, but it faces significant computational challenges due to the high computational …
Density ratio estimation-based bayesian optimization with semi-supervised learning
J Kim - arxiv preprint arxiv:2305.15612, 2023 - arxiv.org
Bayesian optimization has attracted huge attention from diverse research areas in science
and engineering, since it is capable of efficiently finding a global optimum of an expensive-to …
and engineering, since it is capable of efficiently finding a global optimum of an expensive-to …
A Sequential Optimisation Framework for Adaptive Model Predictive Control in Robotics
R Guzmán Apaza - 2023 - alicia.concytec.gob.pe
State-of-the-art control and robotics challenges have long been tackled using model-based
control methods like model predictive control (MPC) and reinforcement learning (RL). These …
control methods like model predictive control (MPC) and reinforcement learning (RL). These …