Aaron D'Souza

According to our database, Aaron D'Souza authored at least 13 papers between 2001 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2026

An Integrative Genome-Scale Metabolic Modeling and Machine Learning Framework for Predicting and Optimizing Biofuel-Relevant Biomass Production in Saccharomyces cerevisiae.

Neha K. Nair, Aaron D'Souza

CoRR, March, 2026

Active Learning Strategies for Efficient Machine-Learned Interatomic Potentials Across Diverse Material Systems.

Mohammed Azeez Khan, Aaron D'Souza, Vijay Choyal

CoRR, January, 2026

2011

Bayesian robot system identification with input and output noise.

Jo-Anne Ting, Aaron D'Souza, Stefan Schaal

Neural Networks, 2011

2010

Efficient Learning and Feature Selection in High-Dimensional Regression.

Jo-Anne Ting, Aaron D'Souza, Sethu Vijayakumar, Stefan Schaal

Neural Comput., 2010

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

A Bayesian approach to empirical local linearization for robotics.

Jo-Anne Ting, Aaron D'Souza, Sethu Vijayakumar, Stefan Schaal

Proceedings of the 2008 IEEE International Conference on Robotics and Automation, 2008

2007

Automatic Outlier Detection: A Bayesian Approach.

Jo-Anne Ting, Aaron D'Souza, Stefan Schaal

Proceedings of the 2007 IEEE International Conference on Robotics and Automation, 2007

2006

Bayesian regression with input noise for high dimensional data.

Jo-Anne Ting, Aaron D'Souza, Stefan Schaal

Proceedings of the Machine Learning, 2006

2005

Incremental Online Learning in High Dimensions.

Sethu Vijayakumar, Aaron D'Souza, Stefan Schaal

Neural Comput., 2005

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

Proceedings of the Advances in Neural Information Processing Systems 18 [Neural Information Processing Systems, 2005

2004

The Bayesian backfitting relevance vector machine.

Aaron D'Souza, Sethu Vijayakumar, Stefan Schaal

Proceedings of the Machine Learning, 2004

2002

Statistical Learning for Humanoid Robots.

Sethu Vijayakumar, Aaron D'Souza, Tomohiro Shibata, Jörg Conradt, Stefan Schaal

Auton. Robots, 2002

2001

Learning inverse kinematics.

Aaron D'Souza, Sethu Vijayakumar, Stefan Schaal

Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2001