Accurate and Scalable Stochastic Gaussian Process Regression via Learnable Coreset-based Variational Inference. Ketenci, M., Perotte, A. J, Elhadad, N., & \textbfIñigo Urteaga In Proceedings of the Forty-first Conference on Uncertainty in Artificial Intelligence, volume 286, of Proceedings of Machine Learning Research, pages 2101–2142, 21–25 Jul, 2025. PMLR. ![Accurate and Scalable Stochastic Gaussian Process Regression via Learnable Coreset-based Variational Inference [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper bibtex @InProceedings{pmlr-v286-ketenci25a,
title = {{Accurate and Scalable Stochastic Gaussian Process Regression via Learnable Coreset-based Variational Inference}},
author = {Mert Ketenci and Adler J Perotte and No\'{e}mie Elhadad and \textbf{I{\~{n}}igo Urteaga}},
booktitle = {Proceedings of the Forty-first Conference on Uncertainty in Artificial Intelligence},
pages = {2101--2142},
year = {2025},
volume = {286},
series = {Proceedings of Machine Learning Research},
month = {21--25 Jul},
publisher = {PMLR},
pdf = {https://raw.githubusercontent.com/mlresearch/v286/main/assets/ketenci25a/ketenci25a.pdf},
url = {https://proceedings.mlr.press/v286/ketenci25a.html},
}
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