Geoff Gordon and his group have papers appearing in the upcoming ICASSP 2010 and AAMAS 2010:
B. Boots, G. Gordon, and S. Siddiqi. Closing the Learning-Planning Loop with Predictive State Representations (extended abstract). To appear in 9th Intl. Conf. on Autonomous Agents and Multiagent Systems (AAMAS), 2010. [pdf]
J. Ramos, S. Siddiqi, A. Dubrawski, and G. Gordon. Automatic State Discovery for Unstructured Audio Scene Classification. To appear in 35th International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2010. [pdf]
Abstract: In this paper we present a novel scheme for unstructured audio scene classification that possesses three highly desirable and powerful features: autonomy, scalability, and robustness. Our scheme is based on our recently introduced machine learning algorithm called Simultaneous Temporal And Contextual Splitting (STACS) that discovers the appropriate number of states and efficiently learns accurate Hidden Markov Model (HMM) parameters for the given data. STACS-based algorithms train HMMs up to five times faster than Baum-Welch, avoid the overfitting problem commonly encountered in learning large state-space HMMs using Expectation Maximization (EM) methods such as Baum-Welch, and achieve superior classification results on a very diverse dataset with minimal pre-processing. Furthermore, our scheme has proven to be highly effective for building real-world applications and has been integrated into a commercial surveillance system as an event detection component.
You can see more publications from the RIRIS project here.