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Gaussian Process (GP) regression has seen widespread use in robotics due to its generality, simplicity of use, and the utility of Bayesian predictions.
C. N. Morris, “Parametric empirical bayes inference: theory and applications,” Journal of the American Statistical Association
1983
Earlier work this paper cites.
MIT press, 1983
L. Ljung and T. Söderström, Theory and practice of recursive identification · 1983
Earlier work this paper cites.
J. Cioffi and T. Kailath, “Fast, recursive-least-squares transversal filters for adaptive filtering,” IEEE Transactions on Acoustics, Speech, and Signal Processing
1984
Earlier work this paper cites.
R. Kulhavỳ, “Restricted exponential forgetting in real-time identification,” Automatica
1987
Earlier work this paper cites.
Princeton university press, 1994
J. D. Hamilton, Time series analysis · 1994
Earlier work this paper cites.
R. J. Santos, “Equivalence of regularization and truncated iteration for general ill-posed problems,” Linear algebra and its applications
1996
Earlier work this paper cites.
S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural computation
1997
Earlier work this paper cites.
D. Mackay, “Introduction to Gaussian process,” Neural Networks and Machine Learning
1998
Earlier work this paper cites.
T. Minka, “Bayesian linear regression,” MIT Technical Report
2000
Earlier work this paper cites.
A. J. Smola and P. L. Bartlett, “Sparse greedy Gaussian process regression,” Neural Information Processing Systems (NIPS)
2001
Earlier work this paper cites.
L. Birgé and P. Massart, “Gaussian model selection,” Journal of the European Mathematical Society
2001
Earlier work this paper cites.
Springer, 2004
C. E. Rasmussen, Gaussian processes in machine learning · 2004
Earlier work this paper cites.
K. Yu, V. Tresp, and A. Schwaighofer, “Learning Gaussian processes from multiple tasks,” International Conference on Machine Learning (ICML)
2005
Earlier work this paper cites.
R. Urtasun, D. J. Fleet, and P. Fua, “3D people tracking with Gaussian process dynamical models,” IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
2006
Earlier work this paper cites.
E. Snelson and Z. Ghahramani, “Sparse Gaussian processes using pseudo-inputs,” Neural Information Processing Systems (NIPS)
2006
Earlier work this paper cites.
B. Ferris, D. Fox, and N. Lawrence, “WiFi-SLAM using Gaussian process latent variable models,” International Joint Conference on Artificial Intelligence (IJCAI)
2007
Earlier work this paper cites.
J. M. Wang, D. J. Fleet, and A. Hertzmann, “Gaussian process dynamical models for human motion,” IEEE Transactions on Pattern Analysis & Machine Intelligence
2008
Earlier work this paper cites.
G. E. Hinton and R. R. Salakhutdinov, “Using deep belief nets to learn covariance kernels for Gaussian processes,” Neural Information Processing Systems (NIPS)
2008
Earlier work this paper cites.
A. Rahimi and B. Recht, “Random features for large-scale kernel machines,” Neural Information Processing Systems (NIPS)
2008
Cited alongside, same era.
S. Vasudevan, F. Ramos, E. Nettleton, and H. Durrant-Whyte, “Gaussian process modeling of large-scale terrain,” Journal of Field Robotics
2009
Cited alongside, same era.
J. Ko and D. Fox, “GP-BayesFilters: Bayesian filtering using Gaussian process prediction and observation models,” Autonomous Robots
2009
Cited alongside, same era.
M. Deisenroth and C. E. Rasmussen, “PILCO: A model-based and data-efficient approach to policy search,” International Conference on Machine Learning (ICML)
2011
Cited alongside, same era.
T. B. Schön, A. Wills, and B. Ninness, “System identification of nonlinear state-space models,” Automatica
2011
Cited alongside, same era.
F. Berkenkamp, M. Turchetta, A. Schoellig, and A. Krause, “Safe model-based reinforcement learning with stability guarantees,” Neural Information Processing Systems (NIPS)
2017
Later among the works it cites.
M. Bauza and A. Rodriguez, “A probabilistic data-driven model for planar pushing,” IEEE International Conference on Robotics and Automation (ICRA)
2017
Later among the works it cites.
C. Finn, P. Abbeel, and S. Levine, “Model-agnostic meta-learning for fast adaptation of deep networks,” International Conference on Machine Learning (ICML)
2017
Later among the works it cites.
2017
Later among the works it cites.
A. Svensson and T. B. Schön, “A flexible state-space model for learning nonlinear dynamical systems,” Automatica
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S. T. O’Callaghan and F. T. Ramos, “Gaussian process occupancy maps,” International Journal of Robotics Research
2012
Cited alongside, same era.
J. Snoek, H. Larochelle, and R. P. Adams, “Practical Bayesian optimization of machine learning algorithms,” Neural Information Processing Systems (NIPS)
2012
Cited alongside, same era.
MIT Press, 2012
K. P. Murphy, Machine Learning: A Probabilistic Perspective · 2012
Cited alongside, same era.
J. Hensman, N. Fusi, and N. D. Lawrence, “Gaussian processes for big data,” Uncertainty in Artificial Intelligence (UAI)
2013
Cited alongside, same era.
D. P. Kingma and M. Welling, “Auto-encoding variational bayes,” International Conference on Learning Representations (ICLR)
2014
Cited alongside, same era.
A. Shah, A. Wilson, and Z. Ghahramani, “Student-t processes as alternatives to Gaussian processes,” Artificial Intelligence and Statistics (AISTATS)
2014
Cited alongside, same era.
J. Snoek, O. Rippel, K. Swersky, R. Kiros, N. Satish, N. Sundaram, M. Patwary, Prabhat, and R. Adams, “Scalable Bayesian optimization using deep neural networks,” International Conference on Machine Learning (ICML)
2015
Cited alongside, same era.
2017
Later among the works it cites.
2017
Later among the works it cites.
2017
Later among the works it cites.
M. Garnelo, J. Schwarz, D. Rosenbaum, F. Viola, D. J. Rezende, S. A. Eslami, and Y. W. Teh, “Neural processes,” International Conference on Machine Learning (ICML)
2018
Closest in time.
E. Grant, C. Finn, S. Levine, T. Darrell, and T. Griffiths, “Recasting gradient-based meta-learning as hierarchical bayes,” International Conference on Learning Representations (ICLR)
2018
Closest in time.
C. Finn, K. Xu, and S. Levine, “Probabilistic model-agnostic meta-learning,” Neural Information Processing Systems (NIPS)
2018
Closest in time.
T. Kim, J. Yoon, O. Dia, S. Kim, Y. Bengio, and S. Ahn, “Bayesian model-agnostic meta-learning,” Neural Information Processing Systems (NIPS)
2018
Closest in time.
2018
Closest in time.
2018
Closest in time.
2018
Closest in time.
E. Schmerling, K. Leung, W. Vollprecht, and M. Pavone, “Multimodal probabilistic model-based planning for human-robot interaction,” IEEE International Conference on Robotics and Automation (ICRA)
2018
Closest in time.
M. Al-Shedivat, T. Bansal, Y. Burda, I. Sutskever, I. Mordatch, and P. Abbeel, “Continuous adaptation via meta-learning in nonstationary and competitive environments,” International Conference on Learning Representations (ICLR)
2018
Closest in time.