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Data-Efficient Reinforcement Learning in Continuous State-Action Gaussian-POMDPs.

, and . NIPS, page 2040-2049. (2017)

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Clustering Protein Sequence and Structure Space with Infinite Gaussian Mixture Models., , , , , and . Pacific Symposium on Biocomputing, page 399-410. World Scientific, (2004)Variational Orthogonal Features., , and . CoRR, (2020)Deep Structured Mixtures of Gaussian Processes., , , and . AISTATS, volume 108 of Proceedings of Machine Learning Research, page 2251-2261. PMLR, (2020)Improving Sample-Efficiency in Reinforcement Learning for Dialogue Systems by Using Trainable-Action-Mask., , and . ICASSP, page 8024-8028. IEEE, (2020)Lazily Adapted Constant Kinky Inference for nonparametric regression and model-reference adaptive control., , , and . Autom., (2020)PIPPS: Flexible Model-Based Policy Search Robust to the Curse of Chaos., , , and . CoRR, (2019)State-Space Inference and Learning with Gaussian Processes., , and . AISTATS, volume 9 of JMLR Proceedings, page 868-875. JMLR.org, (2010)Overcoming Mean-Field Approximations in Recurrent Gaussian Process Models., , , and . CoRR, (2019)Adaptive Sequential Bayesian Change Point Detection, , and . Advances in Neural Information Processing Systems (NIPS): Temporal Segmentation Workshop, (2009)Closed-form Inference and Prediction in Gaussian Process State-Space Models., , and . CoRR, (2018)