Efficient Multi-Information Source Multiobjective Bayesian Optimization

Khatamsaz, Danial; Peddareddygari, Lalith; Friedman, Samuel; Allaire, Douglas

doi:10.2514/6.2020-2127

Cited by 8 publications

(3 citation statements)

References 40 publications

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“…We adopt the methodology presented in refs. 17,73 to conduct Bayesian optimization of multiobjective functions in multi-fidelity settings. For a detailed explanation of the calculation of the EHVI, we refer readers to ref.…”

Section: Multi-objective Optimizationmentioning

confidence: 99%

Bayesian optimization with active learning of design constraints using an entropy-based approach

et al. 2023

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The design of alloys for use in gas turbine engine blades is a complex task that involves balancing multiple objectives and constraints. Candidate alloys must be ductile at room temperature and retain their yield strength at high temperatures, as well as possess low density, high thermal conductivity, narrow solidification range, high solidus temperature, and a small linear thermal expansion coefficient. Traditional Integrated Computational Materials Engineering (ICME) methods are not sufficient for exploring combinatorially-vast alloy design spaces, optimizing for multiple objectives, nor ensuring that multiple constraints are met. In this work, we propose an approach for solving a constrained multi-objective materials design problem over a large composition space, specifically focusing on the Mo-Nb-Ti-V-W system as a representative Multi-Principal Element Alloy (MPEA) for potential use in next-generation gas turbine blades. Our approach is able to learn and adapt to unknown constraints in the design space, making decisions about the best course of action at each stage of the process. As a result, we identify 21 Pareto-optimal alloys that satisfy all constraints. Our proposed framework is significantly more efficient and faster than a brute force approach.

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Section: Multi-objective Optimizationmentioning

confidence: 99%

Bayesian optimization with active learning of design constraints using an entropy-based approach

et al. 2023

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“…Contrary to other recent approaches which propose to query the ground-truth on a regular basis, such as in (Khatamsaz et al, 2020), at each iteration FanG-HPO adaptively chooses among all the sources, including the ground-truth. However, just to ensure a sufficient quality of the approximation provided by the AGPs, before solving (12) FanG-HPO checks if the number of augmenting observations coming from anyone of the cheap sources is larger than those from the ground-truth: in that case s = 1 is selected, instead of solving (12).…”

Section: Deriving the Next Querymentioning

confidence: 99%

“…Thanks to its sample-efficiency, BO is the core component of most of the current Automated Machine Learning (AutoML) (Hutter et al, 2019;He et al, 2021) solutions, both open-source and commercial. BO has been recently extended to also deal with multiple objectives (Hernández-Lobato et al, 2016;Paria et al, 2020), as well as multiple information sources with different computational cost can be accessed (Ghoreishi & Allaire, 2019;Belakaria et al, 2020a;Candelieri et al, 2021;Candelieri & Archetti, 2021b;Khatamsaz et al, 2020). A special case is when sources can be organized hierarchically depending on their quality of approximation (aka fidelity), leading to the so-called multifidelity optimization, originally proposed in (Kennedy & O'Hagan, 2000).…”

Section: Introduction 1rationale and Motivationsmentioning

confidence: 99%

Fair and Green Hyperparameter Optimization via Multi-objective and Multiple Information Source Bayesian Optimization

Candelieri¹,

Ponti²,

Archetti³

2022

Preprint

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There is a consensus that focusing only on accuracy in searching for optimal machine learning models amplifies biases contained in the data, leading to unfair predictions and decision supports. Recently, multi-objective hyperparameter optimization has been proposed to search for machine learning models which offer equally Paretoefficient trade-offs between accuracy and fairness. Although these approaches proved to be more versatile than fairness-aware machine learning algorithms -which optimize accuracy constrained to some threshold on fairness -they could drastically increase the energy consumption in the case of large datasets. In this paper we propose FanG-HPO, a Fair and Green Hyperparameter Optimization (HPO) approach based on both multi-objective and multiple information source Bayesian optimization. FanG-HPO uses subsets of the large dataset (aka information sources) to obtain cheap approximations of both accuracy and fairness, and multi-objective Bayesian Optimization to efficiently identify Pareto-efficient machine learning models. Experiments consider two benchmark (fairness) datasets and two machine learning algorithms (XGBoost and Multi-Layer Perceptron), and provide an assessment of FanG-HPO against both fairness-aware machine learning algorithms and hyperparameter optimization via a multi-objective single-source optimization algorithm in BoTorch, a state-of-the-art platform for Bayesian Optimization.

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Fair and green hyperparameter optimization via multi-objective and multiple information source Bayesian optimization

Candelieri,

Ponti,

Archetti

2024

Mach Learn

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It has been recently remarked that focusing only on accuracy in searching for optimal Machine Learning models amplifies biases contained in the data, leading to unfair predictions and decision supports. Recently, multi-objective hyperparameter optimization has been proposed to search for Machine Learning models which offer equally Pareto-efficient trade-offs between accuracy and fairness. Although these approaches proved to be more versatile than fairness-aware Machine Learning algorithms—which instead optimize accuracy constrained to some threshold on fairness—their carbon footprint could be dramatic, due to the large amount of energy required in the case of large datasets. We propose an approach named FanG-HPO: fair and green hyperparameter optimization (HPO), based on both multi-objective and multiple information source Bayesian optimization. FanG-HPO uses subsets of the large dataset to obtain cheap approximations (aka information sources) of both accuracy and fairness, and multi-objective Bayesian optimization to efficiently identify Pareto-efficient (accurate and fair) Machine Learning models. Experiments consider four benchmark (fairness) datasets and four Machine Learning algorithms, and provide an assessment of FanG-HPO against both fairness-aware Machine Learning approaches and two state-of-the-art Bayesian optimization tools addressing multi-objective and energy-aware optimization.

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Efficient Multi-Information Source Multiobjective Bayesian Optimization

Cited by 8 publications

References 40 publications

Bayesian optimization with active learning of design constraints using an entropy-based approach

Bayesian optimization with active learning of design constraints using an entropy-based approach

Fair and Green Hyperparameter Optimization via Multi-objective and Multiple Information Source Bayesian Optimization

Fair and green hyperparameter optimization via multi-objective and multiple information source Bayesian optimization

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