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Andreas Krause | Felix Berkenkamp | Sebastian Curi | Silvan Melchior | Andreas Krause | Felix Berkenkamp | Sebastian Curi | Silvan Melchior
[1] R. Khasminskii. Stochastic Stability of Differential Equations , 1980 .
[2] Kilian Q. Weinberger,et al. On Calibration of Modern Neural Networks , 2017, ICML.
[3] Felix Berkenkamp,et al. Safe Exploration in Reinforcement Learning: Theory and Applications in Robotics , 2019 .
[4] Alexander G. de G. Matthews,et al. Scalable Gaussian process inference using variational methods , 2017 .
[5] N. Bershad,et al. Random differential equations in science and engineering , 1975, Proceedings of the IEEE.
[6] Sergey Levine,et al. Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models , 2018, NeurIPS.
[7] Carl E. Rasmussen,et al. Overcoming Mean-Field Approximations in Recurrent Gaussian Process Models , 2019, ICML.
[8] Jürgen Schmidhuber,et al. Long Short-Term Memory , 1997, Neural Computation.
[9] Yoshua Bengio,et al. Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation , 2014, EMNLP.
[10] Uri Shalit,et al. Structured Inference Networks for Nonlinear State Space Models , 2016, AAAI.
[11] Roland Siegwart,et al. Towards Efficient Full Pose Omnidirectionality with Overactuated MAVs , 2018, ISER.
[12] S. Billings. Nonlinear System Identification: NARMAX Methods in the Time, Frequency, and Spatio-Temporal Domains , 2013 .
[13] David M. Blei,et al. Variational Inference: A Review for Statisticians , 2016, ArXiv.
[14] Carl E. Rasmussen,et al. Bayesian Inference and Learning in Gaussian Process State-Space Models with Particle MCMC , 2013, NIPS.
[15] Marc Peter Deisenroth,et al. Doubly Stochastic Variational Inference for Deep Gaussian Processes , 2017, NIPS.
[16] Il Memming Park,et al. BLACK BOX VARIATIONAL INFERENCE FOR STATE SPACE MODELS , 2015, 1511.07367.
[17] Carl E. Rasmussen,et al. Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.
[18] Paolo Rapisarda,et al. Data-driven simulation and control , 2008, Int. J. Control.
[19] T. Başar,et al. A New Approach to Linear Filtering and Prediction Problems , 2001 .
[20] Marc Peter Deisenroth,et al. Data-Efficient Reinforcement Learning with Probabilistic Model Predictive Control , 2017, AISTATS.
[21] Carl E. Rasmussen,et al. Non-Factorised Variational Inference in Dynamical Systems , 2018, ArXiv.
[22] David J. Fleet,et al. Gaussian Process Dynamical Models , 2005, NIPS.
[23] Andreas Krause,et al. Safe Model-based Reinforcement Learning with Stability Guarantees , 2017, NIPS.
[24] Neil D. Lawrence,et al. Recurrent Gaussian Processes , 2015, ICLR.
[25] Zoubin Ghahramani,et al. Sparse Gaussian Processes using Pseudo-inputs , 2005, NIPS.
[26] N. Gordon,et al. Novel approach to nonlinear/non-Gaussian Bayesian state estimation , 1993 .
[27] Carl E. Rasmussen,et al. Variational Gaussian Process State-Space Models , 2014, NIPS.
[28] Stefano Ermon,et al. Calibrated Model-Based Deep Reinforcement Learning , 2019, ICML.
[29] Michalis K. Titsias,et al. Variational Learning of Inducing Variables in Sparse Gaussian Processes , 2009, AISTATS.
[30] Max Welling,et al. Auto-Encoding Variational Bayes , 2013, ICLR.
[31] Subhashini Venugopalan,et al. Translating Videos to Natural Language Using Deep Recurrent Neural Networks , 2014, NAACL.
[32] Oliver Brock,et al. Differentiable Particle Filters: End-to-End Learning with Algorithmic Priors , 2018, Robotics: Science and Systems.
[33] Chong Wang,et al. Stochastic variational inference , 2012, J. Mach. Learn. Res..
[34] Christopher Burgess,et al. beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework , 2016, ICLR 2016.
[35] James Hensman,et al. Identification of Gaussian Process State Space Models , 2017, NIPS.
[36] Roger Frigola,et al. Bayesian Time Series Learning with Gaussian Processes , 2015 .
[37] Duy Nguyen-Tuong,et al. Probabilistic Recurrent State-Space Models , 2018, ICML.
[38] Neil D. Lawrence,et al. Gaussian Processes for Big Data , 2013, UAI.
[39] Kuldip K. Paliwal,et al. Bidirectional recurrent neural networks , 1997, IEEE Trans. Signal Process..