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AutoGP: Exploring the capabilities and limitations of Gaussian process models

, , , and . arXiv preprint arXiv:1610.05392, (2016)

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Learning Efficient and Robust Ordinary Differential Equations via Invertible Neural Networks., , , , and . ICML, volume 162 of Proceedings of Machine Learning Research, page 27060-27074. PMLR, (2022)Transformed Distribution Matching for Missing Value Imputation., , , and . ICML, volume 202 of Proceedings of Machine Learning Research, page 42159-42186. PMLR, (2023)Efficient Variational Inference for Gaussian Process Regression Networks., and . AISTATS, volume 31 of JMLR Workshop and Conference Proceedings, page 472-480. JMLR.org, (2013)Rethinking Sparse Gaussian Processes: Bayesian Approaches to Inducing-Variable Approximations., , , , and . CoRR, (2020)Log Gaussian Cox Process Networks., , and . CoRR, (2018)Learning to Counter: Stochastic Feature-based Learning for Diverse Counterfactual Explanations., , , , , , and . CoRR, (2022)Grouped Gaussian processes for solar power prediction., and . Mach. Learn., 108 (8-9): 1287-1306 (2019)Decision-Theoretic Sparsification for Gaussian Process Preference Learning., , and . ECML/PKDD (2), volume 8189 of Lecture Notes in Computer Science, page 515-530. Springer, (2013)Efficient Inference in Multi-task Cox Process Models., , and . AISTATS, volume 89 of Proceedings of Machine Learning Research, page 537-546. PMLR, (2019)Kernel Multi-task Learning using Task-specific Features, , and . In Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics AISTATS'07, (2007)