Research
I spent most of grad-school rummaging through books and papers on applied statistics. I particularly like tinkering with Bayesian nonparametric statistics, and countably infinite models like Gaussian/Dirichlet processes.
Publications
- Ting, J., D'Souza, A., Yamamoto, K., Yoshioka, T., Hoffman, D., Kakei, S., Sergio, L., Kalaska, J., Kawato, M., Strick, P., and Schaal, S. Variational Bayesian Least Squares: An Application to Brain-Machine Interface Data. In Neural Networks: Special Issue on Neuroinformatics, 21(8), 1112-1131. 2008. [pdf]
- Ting, J., D'Souza, A., Vijayakumar, S. and Schaal, S. A Bayesian approach to empirical local linearization for robotics. In Proceedings of the International Conference on Robotics and Automation. 2008. [pdf]
- Ting, J., D'Souza, A. and Schaal, S. Automatic outlier detection: A Bayesian approach. In Proceedings of the International Conference on Robotics and Automation. 2007. [pdf]
- Ting, J., D'Souza, A. and Schaal, S. Bayesian regression with input noise for high-dimensional data. In Proceedings of the International Conference on Machine Learning. 2006. [pdf]
- Vijayakumar, S., D'Souza, A. and Schaal, S. Approximate nearest-neighbor regression in high dimensions. In Nearest-Neighbor Methods in Learning and Vision: Theory and Practice. Greg Shakhnarovich, Trevor Darrell and Piotr Indyk (eds.). MIT Press, Cambridge, MA. 2005.
- Ting, J., D'Souza, A., Yamamoto, K., Yoshioka, T., Hoffman, D., Kakei, S., Sergio, L., Kalaska, J., Kawato, M., Strick, P., and Schaal, S. Predicting EMG Data from M1 Neurons with Variational Bayesian Least Squares. In Advances in Neural Information Processing Systems 18 (NIPS 2005). [pdf]
- Vijayakumar, S., D'Souza, A. and Schaal, S. Incremental Online Learning in High Dimensions. Neural Computation, vol. 17 2005.
- D'Souza, A. Towards Tractable Parameter-Free Statistical Learning. Ph.D. Thesis, Department of Computer Science, University of Southern California, Los Angeles, CA. 2004. [pdf]
- Ting, J., D'Souza, A. and Schaal, S. Predicting EMG Activity from Neural Firing in M1 with Bayesian Backfitting. In Proceedings of the 11th Joint Symposium on Neural Computation (JSNC 2004). Los Angeles, CA. May 2004.
- D'Souza, A., Vijayakumar, S. and Schaal, S. The Bayesian Backfitting Relevance Vector Machine. In Proceedings of the International Conference on Machine Learning (ICML 2004). [pdf]
- D'Souza, A., Vijayakumar, S. and Schaal, S. Bayesian Backfitting. In Proceedings of the 10th Joint Symposium on Neural Computation (JSNC 2003). Irvine, CA. May 2003. [pdf]
- Vijayakumar, S., D'Souza, A., Shibata, T., Conradt, J. and Schaal, S. Statistical learning for humanoid robots. Autonomous Robots, 12, 55-69, 2002. [pdf]
- Schaal, S., Vijayakumar, S., D'Souza, A., Ijspeert, A. & Nakanishi, J. Real-time statistical learning for robotics and human augmentation. International Symposium of Robotics Research. Lorne, Victoria, Australia. Nov 2001.
- D'Souza, A., Vijayakumar, S. and Schaal, S. Are Internal Models of the Entire Body Learnable? In Society for Neuroscience Abstracts. Vol. 27, Program No. 406.2, 2001.
- D'Souza, A., Vijayakumar, S., and Schaal, S. Learning inverse kinematics. In Proceedings of the IEEE International Conference on Intelligent Robots and Systems (IROS 2001), Maui, HI, USA, October 2001. [pdf]
- D'Souza, A., Rickel, J., Herreros, B., and Johnson, L. An automated lab instructor for simulated science experiments. In Proceedings of the International Conference on Artificial Intelligence in Education (AI-ED 2001), pp. 65-76, San Antonio, TX, May 2001. (Recieved Distinguished Paper Award). [pdf]
Links
- My Ph.D. advisor and friend Stefan Schaal.