|
Google Research
Other Google Resources
|
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) (to appear).
Automatic outlier detection: A Bayesian approach, Jo-Anne Ting, Aaron D'Souza, Stefan Schaal, International Conference on Robotics and Automation (ICRA 2007).
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.
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.
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.
The Bayesian backfitting relevance vector machine, Aaron D'Souza, Sethu Vijayakumar, Stefan Schaal, International Conference on Machine Learning, 2004.
|
|
