[32] Kandasamy, K., Schneider, J., and Poczos, B. High Dimensional Bayesian Optimisation and Bandits via Additive Models. In Bach, F. and Blei, D. (eds.), Proceedings of the 32nd International Conference on Machine Learning (ICML'15), volume 37, pp. 295-304. Omnipress, 2015.
[33] Kandasamy, K., Neiswanger, W., Schneider, J., Poczos, B., and Xing, E. P. Neural architecture search with bayesian optimisation and optimal transport. Advances in neural information processing systems, 31, 2018.
[34] Kirsch, A., Van Amersfoort, J., and Gal, Y. Batchbald: Efficient and diverse batch acquisition for deep bayesian active learning. Advances in neural information processing systems, 32, 2019.
[35] Lalchand, V. and Rasmussen, C. E. Approximate inference for fully bayesian gaussian process regression. In Symposium on Advances in Approximate Bayesian Inference, pp. 1-12. PMLR, 2020.
[36] Letham, B., Brian, K., Ottoni, G., and Bakshy, E. Constrained Bayesian optimization with noisy experiments. Bayesian Analysis, 2018.
[37] MacKay, D. J. Information-based objective functions for active data selection. Neural computation, 4(4):590-604, 1992.
[38] Malkomes, G. and Garnett, R. Automating bayesian optimization with bayesian optimization. In Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., and Garnett, R. (eds.), Advances in Neural Information Processing Systems, volume 31. Curran Associates, Inc., 2018. URL https://proceedings.neurips.cc/paper_files/paper/2018/file/2b64c2f19d868305aa8bbc2d72902cc5-Paper.pdf.
[39] Malkomes, G., Schaff, C., and Garnett, R. Bayesian optimization for automated model selection. In Hutter, F., Kotthoff, L., and Vanschoren, J. (eds.), Proceedings of the Workshop on Automatic Machine Learning, volume 64 of Proceedings of Machine Learning Research, pp. 41-47, New York, New York, USA, 24 Jun 2016. PMLR. URL https://proceedings.mlr.press/v64/malkomes_bayesian_2016.html.
[40] Mayr, M., Ahmad, F., Chatzilygeroudis, K. I., Nardi, L., and Krüger, V. Skill-based Multi-objective Reinforcement Learning of Industrial Robot Tasks with Planning and Knowledge Integration. CoRR, abs/2203.10033, 2022. URL https://doi.org/10.48550/arXiv.2203.10033.
[41] Mayr, M., Hvarfner, C., Chatzilygeroudis, K., Nardi, L., and Krueger, V. Learning skill-based industrial robot tasks with user priors. IEEE 18th International Conference on Automation Science and Engineering, 2022. URL https://arxiv.org/abs/2208.01605.
[42] Moss, H. B., Leslie, D. S., Gonzalez, J., and Rayson, P. Gibbon: General-purpose information-based bayesian optimisation. Journal of Machine Learning Research, 22(235):1-49, 2021. URL http://jmlr.org/papers/v22/21-0120.html.
[43] Nardi, L., Koeplinger, D., and Olukotun, K. Practical design space exploration. In 2019 IEEE 27th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS), pp. 347-358. IEEE, 2019.
[44] Nguyen, Q., Wu, K., Gardner, J., and Garnett, R. Local bayesian optimization via maximizing probability of descent. In Koyejo, S., Mohamed, S., Agarwal, A., Belgrave, D., Cho, K., and Oh, A. (eds.), Advances in Neural Information Processing Systems, volume 35, pp. 13190-13202. Curran Associates, Inc., 2022. URL https://proceedings.neurips.cc/paper_files/paper/2022/file/555479a201da27c97aaaed842d16ca49-Paper-Conference.pdf.
[45] Nguyen, Q. P., Low, B. K. H., and Jaillet, P. Rectified max-value entropy search for bayesian optimization, 2022. URL https://arxiv.org/abs/2202.13597.
[46] Osborne, M. A. Bayesian Gaussian processes for sequential prediction, optimisation and quadrature. PhD thesis, Oxford University, UK, 2010.