Results 11 to 20 of about 56,420 (266)

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

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

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

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

Quantum Bayesian Optimization

Advances in Neural Information Processing Systems 36, 2023
Accepted to NeurIPS ...
Zhongxiang Dai, Gregory Kang Ruey Lau, Arun Verma, Yao Shu, Bryan Kian Hsiang Low, Patrick Jaillet   +5 more
openaire   +3 more sources

Causal Bayesian Optimization

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, Xiaoyu Lu, Andrei Paleyes, Javier González 0002   +3 more
openaire   +3 more sources

Application of Improved LightGBM Model in Blood Glucose Prediction

Applied Sciences, 2020
In recent years, with increasing social pressure and irregular schedules, many people have developed unhealthy eating habits, which has resulted in an increasing number of patients with diabetes, a disease that cannot be cured under the current medical ...
Yan Wang, Tao Wang
doaj   +1 more source

Asynchronous batch Bayesian optimization with pipelining evaluations in experimental equipment-limited situations

SLAS Technology
Bayesian optimization is efficient even with a small amount of data and is used in engineering and in science, including biology and chemistry. In Bayesian optimization, a parameterized model with an uncertainty is fitted to explain the experimental data,
Yujin Taguchi, Yusuke Shibuya, Yusuke Hiki, Takashi Morikura, Takahiro G. Yamada, Akira Funahashi   +5 more
doaj   +1 more source

Triangulation Candidates for Bayesian Optimization

Advances in Neural Information Processing Systems 35, 2022
Bayesian optimization involves "inner optimization" over a new-data acquisition criterion which is non-convex/highly multi-modal, may be non-differentiable, or may otherwise thwart local numerical optimizers. In such cases it is common to replace continuous search with a discrete one over random candidates.
Robert B. Gramacy, Annie Sauer, Nathan Wycoff   +2 more
openaire   +3 more sources