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Gaussian Process Regression is a popular nonparametric regression method based on Bayesian principles that provides uncertainty estimates for its predictions.
Gaussian Processes for Machine Learning
Rasmussen, C. E.; and Williams, C. K. 2006 · 2006
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Multivariable feedback control: analysis and design , volume 2
Skogestad, S.; and Postlethwaite, I. 2007 · 2007
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Deterministic error bounds for kernel-based learning techniques under bounded noise
Maddalena, E. T.; Scharnhorst, P.; and Jones, C. N. 2020 · 2008
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Support vector machines
Steinwart, I.; and Christmann, A. 2008 · 2008
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Feedback systems: an introduction for scientists and engineers
Åström, K. J.; and Murray, R. M. 2010 · 2010
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Gaussian process optimization in the bandit setting: no regret and experimental design
Srinivas, N.; Krause, A.; Kakade, S.; and Seeger, M. 2010 · 2010
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Reproducing kernel Hilbert spaces in probability and statistics
Berlinet, A.; and Thomas-Agnan, C. 2011 · 2011
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A tail inequality for quadratic forms of subgaussian random vectors
Hsu, D.; Kakade, S.; Zhang, T.; et al. 2012 · 2012
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Machine learning: a probabilistic perspective
Murphy, K. P. 2012 · 2012
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Online learning for linearly parametrized control problems
Abbasi-Yadkori, Y. 2013 · 2013
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Large-scale log-determinant computation through stochastic Chebyshev expansions
Han, I.; Malioutov, D.; and Shin, J. 2015 · 2015
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High dimensional Bayesian optimisation and bandits via additive models
Kandasamy, K.; Schneider, J.; and Póczos, B. 2015 · 2015
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Taking the human out of the loop: A review of Bayesian optimization
Shahriari, B.; Swersky, K.; Wang, Z.; Adams, R. P.; and De Freitas, N. 2015 · 2015
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Frequentist coverage of adaptive nonparametric Bayesian credible sets
Szabó, B.; Van Der Vaart, A. W.; van Zanten, J.; et al. 2015 · 2015
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Safe learning of regions of attraction for uncertain, nonlinear systems with gaussian processes
Berkenkamp, F.; Moriconi, R.; Schoellig, A. P.; and Krause, A. 2016 · 2016
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Safe controller optimization for quadrotors with Gaussian processes
Berkenkamp, F.; Schoellig, A. P.; and Krause, A. 2016 · 2016
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Modelling and control of dynamic systems using Gaussian process models
Kocijan, J. 2016 · 2016
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Safe model-based reinforcement learning with stability guarantees
Berkenkamp, F.; Turchetta, M.; Schoellig, A.; and Krause, A. 2017 · 2017
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On Kernelized Multi-armed Bandits
Chowdhury, S. R.; and Gopalan, A. 2017 · 2017
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Scalable log determinants for Gaussian process kernel learning
Dong, K.; Eriksson, D.; Nickisch, H.; Bindel, D.; and Wilson, A. G. 2017 · 2017
Algorithmic linearly constrained Gaussian processes
Lange-Hegermann, M. 2018 · 2018
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Gaussian processes for learning and control: A tutorial with examples
Liu, M.; Chowdhary, G.; Da Silva, B. C.; Liu, S.-Y.; and How, J. P. 2018 · 2018
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Learning-based robust model predictive control with state-dependent uncertainty
Soloperto, R.; Müller, M. A.; Trimpe, S.; and Allgöwer, F. 2018 · 2018
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Safe Exploration in Reinforcement Learning: Theory and Applications in Robotics
Berkenkamp, F. 2019 · 2019
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Gaussian Process Optimization with Adaptive Sketching: Scalable and No Regret
Calandriello, D.; Carratino, L.; Lazaric, A.; Valko, M.; and Rosasco, L. 2019 · 2019
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Provably robust learning-based approach for high-accuracy tracking control of lagrangian systems
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Cited alongside, same era.
Linearly constrained Gaussian processes
Jidling, C.; Wahlström, N.; Wills, A.; and Schön, T. B. 2017 · 2017
Cited alongside, same era.
Model predictive control: theory, computation, and design , volume 2
Rawlings, J. B.; Mayne, D. Q.; and Diehl, M. 2017 · 2017
Cited alongside, same era.
Feedback linearization using Gaussian processes
Umlauft, J.; Beckers, T.; Kimmel, M.; and Hirche, S. 2017 · 2017
Cited alongside, same era.
Mean square prediction error of misspecified Gaussian process models
Beckers, T.; Umlauft, J.; and Hirche, S. 2018 · 2018
Cited alongside, same era.
Learning and control using Gaussian processes
Jain, A.; Nghiem, T.; Morari, M.; and Mangharam, R. 2018 · 2018
Cited alongside, same era.
Gaussian processes and kernel methods: A review on connections and equivalences
Kanagawa, M.; Hennig, P.; Sejdinovic, D.; and Sriperumbudur, B. K. 2018 · 2018
Cited alongside, same era.
Helwa, M. K.; Heins, A.; and Schoellig, A. P. 2019 · 2019
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Learning-Based Model Predictive Control: Toward Safe Learning in Control
Hewing, L.; Wabersich, K. P.; Menner, M.; and Zeilinger, M. N. 2019 · 2019
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Scalable Gaussian Process Inference with Finite-data Mean and Variance Guarantees
Huggins, J. H.; Campbell, T.; Kasprzak, M.; and Broderick, T. 2019 · 2019
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Uniform Error Bounds for Gaussian Process Regression with Application to Safe Control
Lederer, A.; Umlauft, J.; and Hirche, S. 2019 · 2019
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On prediction properties of kriging: Uniform error bounds and robustness
Wang, W.; Tuo, R.; and Jeff Wu, C. 2019 · 2019
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Smoothing Splines and Rank Structured Matrices: Revisiting the Spline Kernel
Andersen, M. S.; and Chen, T. 2020 · 2020
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On Semiseparable Kernels and Efficient Computation of Regularized System Identification and Function Estimation
Chen, T.; and Andersen, M. 2020 · 2020
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Learning Constrained Dynamics with Gauss Principle adhering Gaussian Processes
Geist, A. R.; and Trimpe, S. 2020 · 2020
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