Scalable Bayesian Optimization with Sparse Gaussian Process Models
Fuente:
arXiv
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| Autore principale: | |
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| Natura: | Preprint |
| Pubblicazione: |
2020
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866916509427695616 |
|---|---|
| author | Yang, Ang |
| author_facet | Yang, Ang |
| contents | This thesis focuses on Bayesian optimization with the improvements coming from two aspects:(i) the use of derivative information to accelerate the optimization convergence; and (ii) the consideration of scalable GPs for handling massive data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2010_13301 |
| institution | arXiv |
| publishDate | 2020 |
| record_format | arxiv |
| spellingShingle | Scalable Bayesian Optimization with Sparse Gaussian Process Models Yang, Ang Machine Learning Information Retrieval This thesis focuses on Bayesian optimization with the improvements coming from two aspects:(i) the use of derivative information to accelerate the optimization convergence; and (ii) the consideration of scalable GPs for handling massive data. |
| title | Scalable Bayesian Optimization with Sparse Gaussian Process Models |
| topic | Machine Learning Information Retrieval |
| url | https://arxiv.org/abs/2010.13301 |