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Bio:
Aaron D'Souza completed his B.E. in Computer Engineering in 1998
from the Thadomal Shahani Engineering College of Mumbai University,
India. He joined the Computer Science department at USC as a Masters
student in Fall 1998. From Fall 1998 to Spring 2000, he worked as a
graduate research assistant at the Intelligent Systems Division of the
USC Information Sciences Institute
under the supervision of Dr. Lewis Johnson.
In Spring 2000 he joined the Ph.D. program in Computer Science, and
since Fall 2000 has been a graduate research assistant at the Computational Learning and Motor
Control Lab at USC, under the supervision of Dr. Stefan Schaal.
In October 2004 he successfully defended his dissertation
entitled Towards Tractable Parameter-Free
Statistical Learning.
Research Interests:
Machine Learning, specifically the automatic determination of model
complexity in Statistical Learning through Bayesian inference, with
an emphasis on high-dimensional learning applications such as Humanoid
Robot Control.
Address:
Aaron D'Souza
Google Inc.
1600 Amphitheatre Parkway,
Mountain View, CA 94043, USA
Publications:
- J. Ting, A. D'Souza, K. Yamamoto, T. Yoshioka, D. Hoffman,
S. Kakei, L. Sergio, J. Kalaska, M. Kawato, P. Strick and
S. Schaal. Predicting EMG Data from M1 Neurons with Variational
Bayesian Least Squares. In Advances in
Neural Information Processing Systems 18 (NIPS 2005) (to
appear).
- Sethu Vijayakumar, Aaron D'Souza and Stefan Schaal. Locally
weighted projection regression. Neural
Computation. (in press).
- Jo-Anne Ting, Aaron D'Souza and Stefan Schaal. 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.
- Aaron D'Souza. Towards Tractable
Parameter-Free Statistical Learning. Ph.D. Thesis, Department
of Computer Science, University of Southern California, Los Angeles,
CA. 2004.
[pdf]
- Aaron D'Souza, Sethu Vijayakumar and Stefan Schaal. The Bayesian
Backfitting Relevance Vector Machine. In Proceedings of the International Conference on
Machine Learning (ICML 2004).
[pdf],
[ps.gz],
poster [pdf]
- Aaron D'Souza, Sethu Vijayakumar and Stefan Schaal. Bayesian
Backfitting. In Proceedings of the 10th
Joint Symposium on Neural Computation (JSNC 2003). Irvine,
CA. May 2003
paper
[pdf],
poster [pdf]
- Sethu Vijayakumar, Aaron D'Souza, Tomohiro Shibata, Jörg
Conradt and Stefan Schaal. Statistical learning for humanoid
robots. Autonomous Robots, 12,
55-69, 2002
[pdf],
[ps.gz]
- Stefan Schaal, Sethu Vijayakumar, Aaron D'Souza, Auke Ijspeert &
Jun Nakanishi. Real-time statistical learning for robotics and human
augmentation. International Symposium of Robotics Research. Lorne,
Victoria, Australia. Nov 2001. Springer (In press)
[pdf]
- Aaron D'Souza, Sethu Vijayakumar and Stefan Schaal. Are Internal
Models of the Entire Body Learnable? In Society for Neuroscience
Abstracts. Vol. 27, Program No. 406.2, 2001.
(abstract)
[pdf],
[ps.gz]
- Aaron D'Souza, Sethu Vijayakumar, and Stefan Schaal. Learning
inverse kinematics. In Proceedings of the
IEEE International Conference on Intelligent Robots and Systems
(IROS 2001), Maui, HI, USA, October 2001.
[pdf], [ps.gz]
- Aaron D'Souza, Jeff Rickel, Bruno Herreros, and W. Lewis
Johnson. 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],
[ps.gz]
Notes to myself (and other students):
- Bayesian Factor Analysis
[pdf],
[ps.gz]
- Derivation of the Cramér-Rao bound for a single continous
variable.
[pdf],
[ps.gz]
- Probabilistic Derivation of the basic Kalman filter.
[pdf],
[ps.gz]
- Using EM to Estimate a Probability Distribution with a Mixture
of Gaussians.
[pdf],
[ps.gz]
- Deriving a marginal t-distribution.
[pdf],
[ps.gz]
Links:
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