Results 11 to 20 of about 2,251,103 (302)
Extrinsic Bayesian Optimization on Manifolds [PDF]
Algorithms, 2023 We propose an extrinsic Bayesian optimization (eBO) framework for general optimization problems on manifolds. Bayesian optimization algorithms build a surrogate of the objective function by employing Gaussian processes and utilizing the uncertainty in ...
Yihao Fang +3 more
doaj +5 more sources
Bayesian optimization with exponential convergence
CoRR, 2015 This paper presents a Bayesian optimization method with exponential convergence without the need of auxiliary optimization and without the delta-cover sampling.
Kawaguchi, Kenji +2 more
core +5 more sources
Multi-Task Bayesian Optimization [PDF]
, 2014 Bayesian optimization has recently been proposed as a framework for automatically tuning the hyperparameters of machine learning models and has been shown to yield state-of-the-art performance with impressive ease and efficiency.
Snoek, Jasper +2 more
core +7 more sources
Combining Bayesian optimization and Lipschitz optimization [PDF]
Machine Learning, 2020 Bayesian optimization and Lipschitz optimization have developed alternative techniques for optimizing black-box functions. They each exploit a different form of prior about the function. In this work, we explore strategies to combine these techniques for better global optimization.
Mohamed Osama Ahmed +2 more
openaire +3 more sources
CoRR, 2022 Bayesian optimization (BO) is a powerful approach to sample-efficient optimization of black-box objective functions. However, the application of BO to areas such as recommendation systems often requires taking the interpretability and simplicity of the configurations into consideration, a setting that has not been previously studied in the BO ...
Sulin Liu +4 more
openaire +4 more sources
Multi-task Bayesian Optimization of Chemical Reactions [PDF]
, 2021 Recent work has shown how Bayesian optimization (BO) is an efficient method for optimizing expensive experiments such as chemical reactions. However, in previous studies, each optimization has been started from scratch with no information about previous ...
Kobi, Felton +2 more
core +1 more source
Fair Bayesian Optimization [PDF]
Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society, 2021 Given the increasing importance of machine learning (ML) in our lives, several algorithmic fairness techniques have been proposed to mitigate biases in the outcomes of the ML models. However, most of these techniques are specialized to cater to a single family of ML models and a specific definition of fairness, limiting their adaptibility in practice ...
Valerio Perrone +5 more
openaire +3 more sources
CoRR, 2023 Bayesian Optimization is a popular approach for optimizing expensive black-box functions. Its key idea is to use a surrogate model to approximate the objective and, importantly, quantify the associated uncertainty that allows a sequential search of query points that balance exploitation-exploration.
Haoxian Chen 0002, Henry Lam
openaire +2 more sources
CoRR, 2020 This paper studies the problem of globally optimizing a variable of interest that is part of a causal model in which a sequence of interventions can be performed. This problem arises in biology, operational research, communications and, more generally, in all fields where the goal is to optimize an output metric of a system of interconnected nodes. Our
Virginia Aglietti +3 more
openaire +3 more sources
Bayesian-Optimization-Based Improvement of Cu-CHA Catalysts for Direct Partial Oxidation of CH4
, 2022 In the present study, the catalytic performance of a Cu zeolite for partial CH4 oxidation is improved by refinement of catalyst composition using the Bayesian optimization method. For application to this challenging reaction, the activity of the catalyst
Keisuke Takahashi (1409992) +5 more
core +1 more source