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Spatial Decompositions for Large Scale SVMs., , , and . AISTATS, volume 54 of Proceedings of Machine Learning Research, page 1329-1337. PMLR, (2017)Amortized Inference for Gaussian Process Hyperparameters of Structured Kernels., , and . UAI, volume 216 of Proceedings of Machine Learning Research, page 184-194. PMLR, (2023)Safe Active Learning for Time-Series Modeling with Gaussian Processes., , and . NeurIPS, page 2735-2744. (2018)Hierarchical-Hyperplane Kernels for Actively Learning Gaussian Process Models of Nonstationary Systems., , and . AISTATS, volume 206 of Proceedings of Machine Learning Research, page 7897-7912. PMLR, (2023)Safe Active Learning for Time-Series Modeling with Gaussian Processes., , and . GI-Jahrestagung, volume P-294 of LNI, page 281. GI, (2019)Spatial Decompositions for Large Scale SVMs., , , and . CoRR, (2016)Adaptive Discretization for Evaluation of Probabilistic Cost Functions., , , and . AISTATS, volume 108 of Proceedings of Machine Learning Research, page 2098-2108. PMLR, (2020)Structural Kernel Search via Bayesian Optimization and Symbolical Optimal Transport., , and . NeurIPS, (2022)Optimal Learning Rates for Localized SVMs., and . J. Mach. Learn. Res., (2016)Safe Active Learning for Time-Series Modeling with Gaussian Processes., , and . CoRR, (2024)