Decreased Max-value Entropy Search for Multi-fidelity Bayesian Optimization

Gong, Zhuo; Wang, Binglin; Duan, Xiaojun; Chen, Jiangtao; Yan, Liang

doi:10.21203/rs.3.rs-3118480/v1

Cited by 3 publications

(4 citation statements)

References 20 publications

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“…Our acquisition function can be viewed as a version of BAX [32], which aims to greedily gain information about a computed function property via running an algorithm on posterior samples; here, the associated algorithm is an emittance optimization procedure, and the associated function property (computed by the algorithm) is the emittance minimizer over the control variables, x * ∈ X . Our acquisition function is discussed in more detail in appendix D. Timing results for this algorithm are given in appendix E. We note that the multipoint query optimization setting we consider in this paper-and the information-based acquisition function that we develop for this setting-are distinct from the settings (and corresponding methods) of several recent developments in BO, including standard optimization with entropy search methods [43][44][45], multi-objective optimization [42,46], and multi-fidelity optimization [47,48]. In particular, multi-fidelity Bayesian optimization (MFBO) settings are related in that they also involve an auxiliary input space (i.e.…”

Section: Multipoint-bax For Efficient Emittance Optimizationmentioning

confidence: 99%

Multipoint-BAX: a new approach for efficiently tuning particle accelerator emittance via virtual objectives

Ayoub Miskovich,

Neiswanger,

Colocho

et al. 2024

Mach. Learn.: Sci. Technol.

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Although beam emittance is critical for the performance of high-brightness accelerators, optimization is often time limited as emittance calculations, commonly done via quadrupole scans, are typically slow. Such calculations are a type of multipoint query, i.e. each query requires multiple secondary measurements. Traditional black-box optimizers such as Bayesian optimization are slow and inefficient when dealing with such objectives as they must acquire the full series of measurements, but return only the emittance, with each query. We propose a new information-theoretic algorithm, Multipoint-BAX, for black-box optimization on multipoint queries, which queries and models individual beam-size measurements using techniques from Bayesian Algorithm Execution (BAX). Our method avoids the slow multipoint query on the accelerator by acquiring points through a virtual objective, i.e. calculating the emittance objective from a fast learned model rather than directly from the accelerator. We use Multipoint-BAX to minimize emittance at the Linac Coherent Light Source (LCLS) and the Facility for Advanced Accelerator Experimental Tests II (FACET-II). In simulation, our method is 20x faster and more robust to noise compared to existing methods. In live tests, it matched the hand-tuned emittance at FACET-II and achieved a 24% lower emittance than hand-tuning at LCLS. Our method represents a conceptual shift for optimizing multipoint queries, and we anticipate that it can be readily adapted to similar problems in particle accelerators and other scientific instruments.

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Section: Multipoint-bax For Efficient Emittance Optimizationmentioning

confidence: 99%

Multipoint-BAX: a new approach for efficiently tuning particle accelerator emittance via virtual objectives

Ayoub Miskovich,

Neiswanger,

Colocho

et al. 2024

Mach. Learn.: Sci. Technol.

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“…A large body of research identifies the complete Pareto front using entropy-based acquisition functions. For example, MESMO (Wang & Jegelka, 2017;Belakaria et al, 2019), MESMOC (Belakaria et al, 2020), and PESMO (Hernández-Lobato et al, 2016) determine the Pareto front by reducing posterior entropy. SMSego uses the maximum hypervolume improvement acquisition function to choose the next sample (Ponweiser et al, 2008).…”

Section: Related Workmentioning

confidence: 99%

“…This is used to score the utility of evaluating a candidate design x ∈ X based on the statistical model. Some popular acquisition functions in the SOBO litera-ture include expected improvement (EI) (Emmerich & Klinkenberg, 2008), upper confidence bound (UCB) (Srinivas et al, 2012), predictive entropy search (PES) (Hernández-Lobato et al, 2014), and max-value entropy search (MES) (Wang & Jegelka, 2017).…”

Section: Background and Definitionsmentioning

confidence: 99%

FlexiBO: A Decoupled Cost-Aware Multi-Objective Optimization Approach for Deep Neural Networks

Shahriar

Su²,

Kotthoff

et al. 2023

jair

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The design of machine learning systems often requires trading off different objectives, for example, prediction error and energy consumption for deep neural networks (DNNs). Typically, no single design performs well in all objectives; therefore, finding Pareto-optimal designs is of interest. The search for Pareto-optimal designs involves evaluating designs in an iterative process, and the measurements are used to evaluate an acquisition function that guides the search process. However, measuring different objectives incurs different costs. For example, the cost of measuring the prediction error of DNNs is orders of magnitude higher than that of measuring the energy consumption of a pre-trained DNN as it requires re-training the DNN. Current state-of-the-art methods do not consider this difference in objective evaluation cost, potentially incurring expensive evaluations of objective functions in the optimization process. In this paper, we develop a novel decoupled and cost-aware multi-objective optimization algorithm, which we call Flexible Multi-Objective Bayesian Optimization (FlexiBO) to address this issue. For evaluating each design, FlexiBO selects the objective with higher relative gain by weighting the improvement of the hypervolume of the Pareto region with the measurement cost of each objective. This strategy, therefore, balances the expense of collecting new information with the knowledge gained through objective evaluations, preventing FlexiBO from performing expensive measurements for little to no gain. We evaluate FlexiBO on seven state-of-the-art DNNs for image recognition, natural language processing (NLP), and speech-to-text translation. Our results indicate that, given the same total experimental budget, FlexiBO discovers designs with 4.8% to 12.4% lower hypervolume error than the best method in state-of-the-art multi-objective optimization.

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“…Furthermore, recent entropy-based acquisition functions have been proposed: the basic idea is determining the next test point based on the expected information gained about the location or the value of the optimum of a function; see [35] for details. As entropy search may involve nested Monte Carlo algorithms, the added complexity makes these options not popular in actual applications, given that the other above enumerated acquisition functions do yield good performance in most cases, so entropy-based acquisition functions will not be discussed further in this work.…”

Section: Gaussian Processes and Bayesian Optimizationmentioning

confidence: 99%

Cautious Bayesian Optimization: A Line Tracker Case Study

Girbés-Juan,

Moll,

Sala

et al. 2023

Sensors

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In this paper, a procedure for experimental optimization under safety constraints, to be denoted as constraint-aware Bayesian Optimization, is presented. The basic ingredients are a performance objective function and a constraint function; both of them will be modeled as Gaussian processes. We incorporate a prior model (transfer learning) used for the mean of the Gaussian processes, a semi-parametric Kernel, and acquisition function optimization under chance-constrained requirements. In this way, experimental fine-tuning of a performance objective under experiment-model mismatch can be safely carried out. The methodology is illustrated in a case study on a line-follower application in a CoppeliaSim environment.

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Decreased Max-value Entropy Search for Multi-fidelity Bayesian Optimization

Cited by 3 publications

References 20 publications

Multipoint-BAX: a new approach for efficiently tuning particle accelerator emittance via virtual objectives

Multipoint-BAX: a new approach for efficiently tuning particle accelerator emittance via virtual objectives

FlexiBO: A Decoupled Cost-Aware Multi-Objective Optimization Approach for Deep Neural Networks

Cautious Bayesian Optimization: A Line Tracker Case Study

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