Bayesian Optimization for Policy Search in High-Dimensional Systems via Automatic Domain Selection

H. J. Kushner, “A new method of locating the maximum point of an arbitrary multipeak curve in the presence of noise,” Journal of Basic Engineering , vol. 86, no. 1, pp. 97–106, 1964

1964

Earlier work this paper cites.

J. Močkus, “On Bayesian methods for seeking the extremum,” in Optimization Techniques IFIP Technical Conference , 1975, pp. 400–404

1975

Earlier work this paper cites.

R. F. Stengel, Optimal Control and Estimation . Courier Corporation, 1986

1986

Earlier work this paper cites.

D. D. Cox and S. John, “A statistical method for global optimization,” in IEEE Transactions on Systems, Man, and Cybernetics , 1992, pp. 1242–1246

1992

Earlier work this paper cites.

K. Furuta, M. Yamakita, and S. Kobayashi, “Swing-up control of inverted pendulum using pseudo-state feedback,” Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering , vol. 206, no. 4, pp. 263–269, 1992

Original

2001

Earlier work this paper cites.

C. E. Rasmussen and C. K. I. Williams, Gaussian Processes for Machine Learning . MIT Press, 2006

2006

Earlier work this paper cites.

C. M. Bishop, Pattern Recognition and Machine Learning . Springer, 2006

2006

Earlier work this paper cites.

R. Martinez-Cantin, N. de Freitas, A. Doucet, and J. A. Castellanos, “Active policy learning for robot planning and exploration under uncertainty,” in Proceedings of the Robotics: Science and Systems Conference (RSS) , 2007

2007

Earlier work this paper cites.

M. Frean and P. Boyle, “Using Gaussian processes to optimize expensive functions,” in Proceedings of the Australasian Joint Conference on Artificial Intelligence , 2008, pp. 258–267

2008

Cited alongside, same era.

N. Srinivas, A. Krause, S. M. Kakade, and M. Seeger, “Gaussian process optimization in the bandit setting: No regret and experimental design,” in Proceedings of the International Conference on Machine Learning (ICML) , 2010

2010

Cited alongside, same era.

J. Snoek, H. Larochelle, and R. P. Adams, “Practical Bayesian optimization of machine learning algorithms,” in Advances in Neural Information Processing Systems , 2012, pp. 2951–2959

2012

Cited alongside, same era.

J. Gonzalez, J. Longworth, D. James, and N. Lawrence, “Bayesian optimization for synthetic gene design,” NIPS Workshop on Bayesian Optimization in Academia and Industry , 2014

2014

Cited alongside, same era.

Z. Wang, F. Hutter, M. Zoghi, D. Matheson, and N. de Feitas, “Bayesian optimization in a billion dimensions via random embeddings,” Journal of Artificial Intelligence Research , vol. 55, pp. 361–387, 2016

2016

Later among the works it cites.

A. Marco, P. Hennig, J. Bohg, S. Schaal, and S. Trimpe, “Automatic LQR tuning based on Gaussian process global optimization,” in Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) , 2016

2016

Later among the works it cites.

B. Shahriari, K. Swersky, Z. Wang, R. P. Adams, and N. de Freitas, “Taking the human out of the loop: A review of Bayesian optimization,” Proceedings of the IEEE , vol. 104, no. 1, pp. 148–175, 2016

2016

Later among the works it cites.

B. Shahriari, A. Bouchard-Côté, and N. Freitas, “Unbounded Bayesian optimization via regularization,” in Artificial Intelligence and Statistics , 2016, pp. 1168–1176

2016

Later among the works it cites.