“Bayesian Robot System Identification with Input and Output Noise”, Jo-Anne Ting, Aaron D'Souza, Stefan Schaal, Neural Networks (2010) (to appear).
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“Efficient Learning and Feature Selection in High-Dimensional Regression”, Jo-Anne Ting, Aaron D'Souza, Stefan Schaal, Neural Computation, vol. 22(4) (2010), pp. 831-886.
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“A Bayesian Approach to Empirical Local Linearization for Robotics”, Jo-Anne Ting, Aaron D'Souza, Sethu Vijayakumar, Stefan Schaal, International Conference on Robotics and Automation (ICRA2008).
[homepages.inf.ed.ac.uk] [pdf] [search]
“Automatic outlier detection: A Bayesian approach”, Jo-Anne Ting, Aaron D'Souza, Stefan Schaal, International Conference on Robotics and Automation (ICRA 2007).
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“Bayesian Regression with Input Noise for High-Dimensional Data”, Jo-Anne Ting, Aaron D'Souza, Stefan Schaal, In Proceedings of the 23rd International Conference on Machine Learning, 2006.
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“Predicting EMG Data from M1 Neurons with Variational Bayesian Least Squares”, Jo-Anne Ting, Aaron D'Souza, Kenji Yamamoto, Toshinori Yoshioka, Donna Hoffman, Shinji Kakei, Lauren Sergio, John Kalaska, Mitsuo Kawato, Peter Strick, Stefan Schaal, Advances in Neural Information Processing Systems 18, 2006.
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“Predicting EMG Data from M1 Neurons with Variational Bayesian Least Squares”, Jo-Anne Ting, Aaron D'Souza, Kenji Yamamoto, Toshinori Yoshioka, Donna L. Hoffman, Lauren Sergio, Shinji Kakei, John Kalaska, Mitsuo Kawato, Peter Strick, Stefan Schaal, Neural Information Processing Systems, 2005.
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“The Bayesian backfitting relevance vector machine”, Aaron D'Souza, Sethu Vijayakumar, Stefan Schaal, International Conference on Machine Learning, 2004.
[doi.acm.org] [search]