Publications

Main Publications

full bibtex file

Ryan Turner, Steven Bottone, and Bhargav Avasarala. A Complete Variational Tracker. In Advances in Neural Information Processing Systems, 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, 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 Samuel Kaski, David J. Miller, Erkki Oja, and Antti Honkela, editors, Machine Learning for Signal Processing (MLSP 2010), pages 385-390, Kittilä, Finland, August 2010.

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

Ryan Turner and Carl Edward Rasmussen. Model based learning of sigma points in unscented Kalman filtering. In Samuel Kaski, David J. Miller, Erkki Oja, and Antti Honkela, editors, Machine Learning for Signal Processing (MLSP 2010), pages 178-183, Kittilä, Finland, August 2010.

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

Yunus Saatçi, Ryan Turner, and Carl Edward Rasmussen. Gaussian process change point models. In 27th International Conference on Machine Learning, pages 927-934, Haifa, Israel, June 2010.

Ryan Turner, Marc Peter Deisenroth, and Carl Edward Rasmussen. State-space inference and learning with Gaussian processes. In Yee Whye Teh and Mike Titterington, editors, 13th International Conference on Artificial Intelligence and Statistics, volume 9 of W&CP, pages 868-875, Chia Laguna, Sardinia, Italy, May 2010. Journal of Machine Learning Research.

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.

Other Publications

(please cite the main publications when applicable.) 

Ryan Turner, Marc Peter Deisenroth, and Carl Edward Rasmussen. System identification in Gaussian process dynamical systems. In Dilan Görür, editor, NIPS Workshop on Nonparametric Bayes, Whistler, BC, Canada, December 2009.

Ryan Turner, Yunus Saatçi, and Carl Edward Rasmussen. Adaptive sequential Bayesian change point detection. In Zaïd Harchaoui, editor, NIPS Workshop on Temporal Segmentation, Whistler, BC, Canada, December 2009.

Ryan Turner. Bayesian Approaches to Rare Event Prediction in Multivariate Time Series. 1st Year Report, Cambridge, UK, 2008.

Contributed to:
KE Hild and V Calhoun. The Fourth Annual 2008 MLSP CompetitionIn Intl. Conf. on Machine Learning for Signal Processing (MLSP '08), Cancun, Mexico, pp. 38-42, Oct. 2008.

Publication Volunteer in:
A McCallum and S Roweis. Proceedings of the 25th International Conference on Machine Learning (ICML 2008). Helsinki, Finland.


Posters

R Turner, S Bottone, and B Avasarala
A Complete Variational Tracker
NIPS 2014, Montreal, QC, Canada, December 2014.
pdf

R Turner
Supervised Bayesian Online Change Point Detection
BayLearn 2014, Berkeley, CA, USA, October 2014.
pdf

R Turner, S Bottone, and C Stanek
Online Variational Approximations to non-Exponential Family Change Point Models: With Application to Radar Tracking
NIPS 2013, Lake Tahoe, NV, USA, December 2013.
pdf

R Turner, S Bottone, and Z Ghahramani
Fast online anomaly detection using scan statistics
Machine Learning for Signal Processing (MLSP '10), Kittilä, Finland, 385-390, August 2010.
pdf

Y Saatci, R Turner, and CE Rasmussen
Gaussian Process Change Point Models
ICML 2010, Haifa, Israel, June 2010.
pdf

R Turner, MP Deisenroth, and CE Rasmussen
State-Space Inference and Learning with Gaussian Processes
AIStats 2010, Sardinia, Italy, 9:868-875, May 2010.
pdf

R Turner, MP Deisenroth, and CE Rasmussen
System Identification in Gaussian Process Dynamical Systems
Nonparametric Bayes Workshop at NIPS 2009, Whistler, BC, Canada, December 2009.
pdf

R Turner, Y Saatci, and CE Rasmussen
Adaptive Sequential Bayesian Change Point Detection
Temporal Segmentation Workshop at NIPS 2009, Whistler, BC, Canada, December 2009.
pdf

Talks

R Turner
Advice for Engineering Senior Design students at UC Santa Cruz, CE 123A Guest Lecture
Santa Cruz, CA, January 2011.
pdf

R Turner and CE Rasmussen
Model Based Learning of Sigma Points in Unscented Kalman Filtering
Machine Learning for Signal Processing (MLSP '10), Kittilä, Finland, 178-183, August 2010.
pdf

Y Saatci, R Turner, and CE Rasmussen
Gaussian Process Change Point Models
ICML 2010, Haifa, Israel, June 2010.
pdf

R Turner, MP Deisenroth, and CE Rasmussen
State-Space Inference and Learning with Gaussian Processes
Fox Lab, Univeristy of Washington, Seattle, WA, March 2010.
pdf

R Turner, Y Saatci, and CE Rasmussen
Adaptive Sequential Bayesian Change Point Detection
Temporal Segmentation Workshop at NIPS 2009, Whistler, BC, Canada, December 2009.
pdf

Misc.

R Turner
Machine Learning Reviewer's Checklist
Cambridge, UK, September 2010.
pdf

R Turner
Advice for Engineering Senior Design students at UC Santa Cruz
Santa Cruz, CA, January 2010.
pdf

Featured in:
UC Santa Cruz Transfer Student View 2008-2009
Santa Cruz, CA, 2007.
pdf
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resume.pdf
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Ryan Turner,
Aug 23, 2015, 3:30 PM
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ryan_turner_pubs.bib
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Ryan Turner,
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