Ryan Turner
now a Machine Learning Solutions Engineer at Iterative
Publications
Gaussian Process Change Point Models
Yunus Saatçi, Ryan Turner, Carl Edward Rasmussen, June 2010. (In 27th International Conference on Machine Learning). Haifa, Israel.
Abstract▼ URL
We combine Bayesian online change point detection with Gaussian processes to create a nonparametric time series model which can handle change points. The model can be used to locate change points in an online manner; and, unlike other Bayesian online change point detection algorithms, is applicable when temporal correlations in a regime are expected. We show three variations on how to apply Gaussian processes in the change point context, each with their own advantages. We present methods to reduce the computational burden of these models and demonstrate it on several real world data sets.
Fast Online Anomaly Detection Using Scan Statistics
Ryan Turner, Steven Bottone, Zoubin Ghahramani, August 2010. (In Machine Learning for Signal Processing (MLSP 2010)). Edited by Samuel Kaski, David J. Miller, Erkki Oja, Antti Honkela. Kittilä, Finland. ISBN: 978-1-4244-7876-7.
Abstract▼ URL
We present methods to do fast online anomaly detection using scan statistics. Scan statistics have long been used to detect statistically significant bursts of events. We extend the scan statistics framework to handle many practical issues that occur in application: dealing with an unknown background rate of events, allowing for slow natural changes in background frequency, the inverse problem of finding an unusual lack of events, and setting the test parameters to maximize power. We demonstrate its use on real and synthetic data sets with comparison to other methods.
System Identification in Gaussian Process Dynamical Systems
Ryan Turner, Marc Peter Deisenroth, Carl Edward Rasmussen, December 2009. (In NIPS Workshop on Nonparametric Bayes). Edited by Dilan Görür. Whistler, BC, Canada.
Comment: poster.
State-Space Inference and Learning with Gaussian Processes
Ryan Turner, Marc Peter Deisenroth, Carl Edward Rasmussen, May 13–15 2010. (In 13th International Conference on Artificial Intelligence and Statistics). Edited by Yee Whye Teh, Mike Titterington. Chia Laguna, Sardinia, Italy. W & CP.
Abstract▼ URL
State-space inference and learning with Gaussian processes (GPs) is an unsolved problem. We propose a new, general methodology for inference and learning in nonlinear state-space models that are described probabilistically by non-parametric GP models. We apply the expectation maximization algorithm to iterate between inference in the latent state-space and learning the parameters of the underlying GP dynamics model.
Comment: poster.
Model Based Learning of Sigma Points in Unscented Kalman Filtering
Ryan Turner, Carl Edward Rasmussen, August 2010. (In Machine Learning for Signal Processing (MLSP 2010)). Edited by Samuel Kaski, David J. Miller, Erkki Oja, Antti Honkela. Kittilä, Finland. ISBN: 978-1-4244-7876-7.
Abstract▼ URL
The unscented Kalman filter (UKF) is a widely used method in control and time series applications. The UKF suffers from arbitrary parameters necessary for a step known as sigma point placement, causing it to perform poorly in nonlinear problems. We show how to treat sigma point placement in a UKF as a learning problem in a model based view. We demonstrate that learning to place the sigma points correctly from data can make sigma point collapse much less likely. Learning can result in a significant increase in predictive performance over default settings of the parameters in the UKF and other filters designed to avoid the problems of the UKF, such as the GP-ADF. At the same time, we maintain a lower computational complexity than the other methods. We call our method UKF-L.
Adaptive Sequential Bayesian Change Point Detection
Ryan Turner, Yunus Saatçi, Carl Edward Rasmussen, December 2009. (In NIPS Workshop on Temporal Segmentation). Edited by Zaïd Harchaoui. Whistler, BC, Canada.
Abstract▼ URL
Real-world time series are often nonstationary with respect to the parameters of some underlying prediction model (UPM). Furthermore, it is often desirable to adapt the UPM to incoming regime changes as soon as possible, necessitating sequential inference about change point locations. A Bayesian algorithm for online change point detection (BOCPD) has been introduced recently by Adams and MacKay (2007). In this algorithm, uncertainty about the last change point location is updated sequentially, and is integrated out to make online predictions robust to parameter changes. BOCPD requires a set of fixed hyper-parameters which allow the user to fully specify the hazard function for change points and the prior distribution over the parameters of the UPM. In practice, finding the “right” hyper-parameters can be quite difficult. We therefore extend BOCPD by introducing hyper-parameter learning, without sacrificing the online nature of the algorithm. Hyper-parameter learning is performed by optimizing the marginal likelihood of the BOCPD model, a closed-form quantity which can be computed sequentially. We illustrate performance on three real-world datasets.