Results 1 to 10 of about 56,129 (265)

Extrinsic Bayesian Optimization on Manifolds

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, Mu Niu, Pokman Cheung, Lizhen Lin   +3 more
doaj   +3 more sources

Bayesian Distributionally Robust Optimization

SIAM Journal on Optimization, 2023
We introduce a new framework, Bayesian Distributionally Robust Optimization (Bayesian-DRO), for data-driven stochastic optimization where the underlying distribution is unknown. Bayesian-DRO contrasts with most of the existing DRO approaches in the use of Bayesian estimation of the unknown distribution.
Alexander Shapiro, Enlu Zhou, Yifan Lin   +2 more
exaly   +3 more sources

Bayesian Optimization Of NeuroStimulation (BOONStim). [PDF]

Brain Stimul
Abstract Background Transcranial magnetic stimulation (TMS) treatment response is influenced by individual variability in brain structure and function. Sophisticated, user-friendly approaches, incorporating both established functional magnetic resonance imaging (fMRI) and TMS simulation tools, to ...
Oliver LD, Jeyachandra J, Dickie EW, Hawco C, Mansour S, Hare SM, Buchanan RW, Malhotra AK, Blumberger DM, Deng ZD, Voineskos AN.   +10 more
europepmc   +5 more sources

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, Michele Donini, Muhammad Bilal Zafar, Robin Schmucker, Krishnaram Kenthapadi, Cédric Archambeau   +5 more
openaire   +2 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, Sharan Vaswani, Mark Schmidt 0001   +2 more
openaire   +2 more sources

An improved hyperparameter optimization framework for AutoML systems using evolutionary algorithms

Scientific Reports, 2023
For any machine learning model, finding the optimal hyperparameter setting has a direct and significant impact on the model’s performance. In this paper, we discuss different types of hyperparameter optimization techniques.
Amala Mary Vincent, P. Jidesh
doaj   +1 more source

Sparse Bayesian Optimization

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, Qing Feng, David Eriksson, Benjamin Letham, Eytan Bakshy   +4 more
openaire   +3 more sources

Tuning of Bayesian optimization for materials synthesis: simulation of the one-dimensional case

Science and Technology of Advanced Materials: Methods, 2022
Materials exploration requires the optimization of a multidimensional space including the chemical composition and synthesis parameters such as temperature and pressure.
Ryo Nakayama, Ryota Shimizu, Taishi Haga, Takefumi Kimura, Yasunobu Ando, Shigeru Kobayashi, Nobuaki Yasuo, Masakazu Sekijima, Taro Hitosugi   +8 more
doaj   +1 more source

Pseudo-Bayesian Optimization

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

Enhanced machine learning tree classifiers for lithology identification using Bayesian optimization

Applied Computing and Geosciences, 2022
Lithology identification is a fundamental activity in oil and gas exploration. The application of artificial intelligence (AI) is currently being adopted as a state-of-the-art means of automating lithology identification.
Solomon Asante-Okyere, Chuanbo Shen, Harrison Osei   +2 more
doaj   +1 more source