Bayesian Optimization for auto-tuning GPU kernels

Willemsen, Floris-Jan; Nieuwpoort, Rob V. van; Werkhoven, Ben van

doi:10.48550/arxiv.2111.14991

Cited by 2 publications

(2 citation statements)

References 29 publications

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“…They can provide levels of uncertainty for predictions. However, Gaussian processes are computationally intensive and may not scale well to large datasets, and they offer a probabilistic interpretation [96,97]. Multivariate adaptive regression splines are non-linear models that can capture complex relationships between variables and they are useful when the dataset is large and computation time is not an issue [98].…”

Section: Comparison Of Algorithmsmentioning

confidence: 99%

Applications of different machine learning methods on nuclear charge radius estimations

Bayram,

Yeşilkanat,

Akkoyun

2023

Phys. Scr.

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Theoretical models come into play when the radius of nuclear charge, one of the most fundamental properties of atomic nuclei, cannot be measured using different experimental techniques. As an alternative to these models, machine learning (ML) can be considered as a different approach. In this study, ML techniques were performed using the experimental charge radius of 933 atomic nuclei (A ≥ 40 and Z ≥ 20) available in the literature. In the calculations in which eight different approaches were discussed, the obtained outcomes were compared with the experimental data, and the success of each ML approach in estimating the charge radius was revealed. As a result of the study, it was seen that the Cubist model approach was more successful than the others. It has also been observed that ML methods do not miss the different behavior in the magic numbers region.

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Section: Comparison Of Algorithmsmentioning

confidence: 99%

Applications of different machine learning methods on nuclear charge radius estimations

Bayram,

Yeşilkanat,

Akkoyun

2023

Phys. Scr.

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“…The first line involves the development of new acquisition functions in the GP surrogate model. Certain designs of acquisition functions aim to solve specific problems encountered in discrete or categorical space (Deshwal et al, 2021;Oh et al, 2021;Willemsen et al, 2021). The advantage of this line of methods is that the BO framework is intact and the GP surrogate is preserved.…”

Section: Literature Reviewmentioning

confidence: 99%

Hybrid Models for Mixed Variables in Bayesian Optimization

Luo¹,

Cho²,

Demmel³

et al. 2022

Preprint

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We systematically describe the problem of simultaneous surrogate modeling of mixed variables (i.e., continuous, integer and categorical variables) in the Bayesian optimization (BO) context. We provide a unified hybrid model using both Monte Carlo tree search (MCTS) and Gaussian processes (GP) that encompasses and generalizes multiple state-of-the-art mixed BO surrogates.Based on the architecture, we propose applying a new dynamic model selection criterion among novel candidate families of covariance kernels, including non-stationary kernels and associated families. Different benchmark problems are studied and presented to support the superiority of our model, along with results highlighting the effectiveness of our method compared to most state-of-the-art mixed-variable methods in BO.

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Bayesian Optimization for auto-tuning GPU kernels

Cited by 2 publications

References 29 publications

Applications of different machine learning methods on nuclear charge radius estimations

Applications of different machine learning methods on nuclear charge radius estimations

Hybrid Models for Mixed Variables in Bayesian Optimization

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