Results 11 to 20 of about 288,690 (232)

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
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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
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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
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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
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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
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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
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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 that surrogate by deriving an acquisition function.
Yihao Fang, Mu Niu, Pokman Cheung, Lizhen Lin   +3 more
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Bayesian Functional Optimization

Proceedings of the AAAI Conference on Artificial Intelligence, 2018
Bayesian optimization (BayesOpt) is a derivative-free approach for sequentially optimizing stochastic black-box functions. Standard BayesOpt, which has shown many successes in machine learning applications, assumes a finite dimensional domain which often is a parametric space.
Vien, Ngo Anh, Zimmermann, Heiko, Toussaint, Marc   +2 more
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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

Stratified Bayesian Optimization [PDF]

, 2018
We consider derivative-free black-box global optimization of expensive noisy functions, when most of the randomness in the objective is produced by a few influential scalar random inputs. We present a new Bayesian global optimization algorithm, called Stratified Bayesian Optimization (SBO), which uses this strong dependence to improve performance.
Saul Toscano-Palmerin, Peter I. Frazier
openaire   +2 more sources