Zobrazeno 1 - 10
of 46
pro vyhledávání: '"Bartels, Simon"'
Autor:
González-Duque, Miguel, Michael, Richard, Bartels, Simon, Zainchkovskyy, Yevgen, Hauberg, Søren, Boomsma, Wouter
Optimizing discrete black-box functions is key in several domains, e.g. protein engineering and drug design. Due to the lack of gradient information and the need for sample efficiency, Bayesian optimization is an ideal candidate for these tasks. Seve
Externí odkaz:
http://arxiv.org/abs/2406.04739
Autor:
Michael, Richard, Bartels, Simon, González-Duque, Miguel, Zainchkovskyy, Yevgen, Frellsen, Jes, Hauberg, Søren, Boomsma, Wouter
To optimize efficiently over discrete data and with only few available target observations is a challenge in Bayesian optimization. We propose a continuous relaxation of the objective function and show that inference and optimization can be computati
Externí odkaz:
http://arxiv.org/abs/2404.17452
Autor:
Bartels, Simon, Stensbo-Smidt, Kristoffer, Moreno-Muñoz, Pablo, Boomsma, Wouter, Frellsen, Jes, Hauberg, Søren
Publikováno v:
Proceedings of The 26th International Conference on Artificial Intelligence and Statistics (2023)
We present a method to approximate Gaussian process regression models for large datasets by considering only a subset of the data. Our approach is novel in that the size of the subset is selected on the fly during exact inference with little computat
Externí odkaz:
http://arxiv.org/abs/2202.10769
Algorithms involving Gaussian processes or determinantal point processes typically require computing the determinant of a kernel matrix. Frequently, the latter is computed from the Cholesky decomposition, an algorithm of cubic complexity in the size
Externí odkaz:
http://arxiv.org/abs/2107.10587
Autor:
Bartels, Simon, Hennig, Philipp
Regularized least-squares (kernel-ridge / Gaussian process) regression is a fundamental algorithm of statistics and machine learning. Because generic algorithms for the exact solution have cubic complexity in the number of datapoints, large datasets
Externí odkaz:
http://arxiv.org/abs/1911.06048
Akademický článek
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Several recent works have developed a new, probabilistic interpretation for numerical algorithms solving linear systems in which the solution is inferred in a Bayesian framework, either directly or by inferring the unknown action of the matrix invers
Externí odkaz:
http://arxiv.org/abs/1810.03398
Bayesian optimization has become a successful tool for hyperparameter optimization of machine learning algorithms, such as support vector machines or deep neural networks. Despite its success, for large datasets, training and validating a single conf
Externí odkaz:
http://arxiv.org/abs/1605.07079
Akademický článek
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Autor:
Bartels, Simon, Stensbo-Smidt, Kristoffer, Moreno-Munoz, Pablo, Boomsma, Wouter, Frellsen, Jes, Hauberg, Søren
Publikováno v:
Bartels, S, Stensbo-Smidt, K, Moreno-Munoz, P, Boomsma, W, Frellsen, J & Hauberg, S 2023, Adaptive Cholesky Gaussian Processes . in Proceedings of the 26th International Conference on Artificial Intelligence and Statistics . vol. 206, Proceedings of Machine Learning Research, Proceedings of Machine Learning Research, pp. 408-452, 26 th International Conference on Artificial Intelligence and Statistics, Valencia, Spain, 25/04/2023 . < https://proceedings.mlr.press/v206/bartels23a.html >
We present a method to approximate Gaussian process regression models to large datasets by considering only a subset of the data. Our approach is novel in that the size of the subset is selected on the fly during exact inference with little computati
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______1202::fad444db5f83acce6494f3fd27646bea
https://orbit.dtu.dk/en/publications/2cdb9b51-9f86-4beb-9abc-f7d5b5c3dd5c
https://orbit.dtu.dk/en/publications/2cdb9b51-9f86-4beb-9abc-f7d5b5c3dd5c