Publications

Ryan Turner, David Eriksson, Michael McCourt, Juha Kiili, Eero Laaksonen, Zhen Xu, and Isabelle Guyon. Bayesian Optimization is Superior to Random Search for Machine Learning Hyperparameter Tuning: Analysis of the Black-Box Optimization Challenge 2020. In arXiv:2104.10201, 2021.

• Under review for Proceedings of Machine Learning Research special issue.

Ryan Turner. Harnessing second order optimizers from deep learning frameworks. Twitter Engineering Blog, 2021.

David Eriksson, Matthias Poloczek, Jacob Gardner, Ryan Turner, and Michael Pearce. Scalable Global Optimization via Local Bayesian Optimization. In Advances in Neural Information Processing Systems (NIPS), 2019.

• arXiv version arXiv:1910.01739

Ryan Turner, Jane Hung, Yunus Saatci, and Jason Yosinski. Metropolis-Hastings Generative Adversarial Networks. In International Conference on Machine Learning (ICML), pages 6345-6353, 2019.

• Extension of paper at Bayesian Deep Learning (NIPS 2018 Workshop).

• arXiv version arxXiv:1811.11357

• Uber Eng blog post

Ryan Turner and Brady Neal. How Well Does Your Sampler Really Work? In Uncertainty in Artificial Intelligence, 2018.

• Extension of paper at Bayesian Deep Learning (NIPS 2017 Workshop).

• Presented at Bayes Comp 2018.

• Long version in arXiv:1712.06006 (2017).

David Krueger, Chin-Wei Huang, Riashat Islam, Ryan Turner, Alexandre Lacoste, and Aaron Courville. Bayesian Hypernetworks. In arXiv:1710.04759, 2017.

• Extension of paper at Bayesian Deep Learning (NIPS 2017 Workshop).

• Presented at the Deep Learning and Reinforcement Learning Summer School (2017).

• Presented at the Montreal AI Symposium (2017).

• Translated into Mandarin within 24 hours of being publicly released.

Ryan Turner. A Model Explanation System. In Machine Learning for Signal Processing, 2016.

• Extension of paper at Black Box Learning and Inference (NIPS 2015 Workshop)

Ryan Turner, Steven Bottone, and Bhargav Avasarala. A Complete Variational Tracker. In Advances in Neural Information Processing Systems (NIPS), pages 496-504, 2014.

Ryan Turner. Supervised Bayesian Online Change Point Detection. In BayLearn, 2014.

Ryan Turner, Steven Bottone, and Clay J. Stanek. Online Variational Approximations to non-Exponential Family Change Point Models: With Application to Radar Tracking. In Advances in Neural Information Processing Systems (NIPS), pages 306-314, 2013.

Marc P. Deisenroth, Ryan Turner, Marco Huber, Uwe D. Hanebeck, and Carl E. Rasmussen. Robust Filtering and Smoothing with Gaussian Processes. In IEEE Transactions on Automatic Control, volume 57, pages 1865-1871, 2012.

Ryan Turner, Steven Bottone, and Zoubin Ghahramani. Fast Online Anomaly Detection Using Scan Statistics. In Machine Learning for Signal Processing (MLSP), pages 385-390, 2010.

• Later extended as a chapter in Handbook of Scan Statistics, edited by Joseph Glaz and Markos V. Koutras, 2017.

• Extensions presented in invited talk at International Workshop on Applied Probability (2016).

Ryan Turner and Carl Edward Rasmussen. Model Based Learning of Sigma Points in Unscented Kalman Filtering. In Machine Learning for Signal Processing (MLSP), pages 178-183, 2010.

• Later selected to appear in journal Neurocomputing (2011).

Yunus Saatçi, Ryan Turner, and Carl Edward Rasmussen. Gaussian Process Change Point Models. In 27th International Conference on Machine Learning (ICML), pages 927-934, 2010.

Ryan Turner, Marc Peter Deisenroth, and Carl Edward Rasmussen. State-Space Inference and Learning with Gaussian Processes. In 13th International Conference on Artificial Intelligence and Statistics, pages 868-875, 2010. Journal of Machine Learning Research.

Ryan Turner, Marc Peter Deisenroth, and Carl Edward Rasmussen. System Identification in Gaussian Process Dynamical Systems. In Nonparametric Bayes (NIPS 2009 Workshop), 2009.

Ryan Turner, Yunus Saatçi, and Carl Edward Rasmussen. Adaptive Sequential Bayesian Change Point Detection. In Temporal Segmentation (NIPS 2009 Workshop), 2009.

PhD Thesis

Ryan Darby Turner. Gaussian processes for state space models and change point detection. PhD thesis, University of Cambridge, Department of Engineering, Cambridge, UK, 2011.