Learning inverse kinematics. - Aaron D'Souza; Sethu Vijayakumar; Stefan Schaal (IROS, 2001)

Statistical Learning for Humanoid Robots. - Sethu Vijayakumar; Aaron D'Souza; Tomohiro Shibata; Jörg Conradt; Stefan Schaal (Auton. Robots, 2002)

The Bayesian backfitting relevance vector machine. - Aaron D'Souza; Sethu Vijayakumar; Stefan Schaal (ICML, 2004)

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 (NIPS, 2005)

Incremental Online Learning in High Dimensions. - Sethu Vijayakumar; Aaron D'Souza; Stefan Schaal (Neural Comput., 2005)

Bayesian regression with input noise for high dimensional data. - Jo-Anne Ting; Aaron D'Souza; Stefan Schaal (ICML, 2006)

Automatic Outlier Detection: A Bayesian Approach. - Jo-Anne Ting; Aaron D'Souza; Stefan Schaal (ICRA, 2007)

A Bayesian approach to empirical local linearization for robotics. - Jo-Anne Ting; Aaron D'Souza; Sethu Vijayakumar; Stefan Schaal (ICRA, 2008)

Variational Bayesian least squares: An application to brain-machine interface data. - Jo-Anne Ting; Aaron D'Souza; Kenji Yamamoto; Toshinori Yoshioka; Donna L. Hoffman; Shinji Kakei; Lauren Sergio; John Kalaska; Mitsuo Kawato; Peter Strick; Stefan Schaal (Neural Networks, 2008)

Efficient Learning and Feature Selection in High-Dimensional Regression. - Jo-Anne Ting; Aaron D'Souza; Sethu Vijayakumar; Stefan Schaal (Neural Comput., 2010)

Bayesian robot system identification with input and output noise. - Jo-Anne Ting; Aaron D'Souza; Stefan Schaal (Neural Networks, 2011)