Jo-Anne Ting
NSERC Postdoctoral Fellow, University of British Columbia
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Contact:
Department of Computer Science
University of British Columbia
201-2366 Main Mall, Vancouver, BC, Canada, V6T 1Z4
Office: ICCS 109
Work: +1 (604) 827 5554
Email: first initial + last name at acm dot org
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Bio:
I'm an NSERC postdoctoral fellow at the University of British Columbia working with Nando de Freitas & Kevin Murphy. I received my PhD in Computer Science from the University of Southern California in 2009, where I was advised by Stefan Schaal.
Prior to that, I graduated with a BASc in Computer Engineering from the University of Waterloo in 2003. I also spent time at the University of Edinburgh
in 2009, working with Sethu Vijayakumar.
Publications:
2010:
- Saal H.P., Ting J., and Vijayakumar S.
Active Sequential Learning with Tactile Feedback,
International Conference on Artificial Intelligence and Statistics (AISTATS 2010).
[pdf]
- Saal H.P., Ting J., and Vijayakumar S.
Active estimation of object dynamics parameters with tactile sensors,
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2010).
- Ting, J., D’Souza, A., Vijayakumar, S., and Schaal, S.
Efficient Learning and Feature Selection in High-Dimensional Regression,
Neural Computation, 22(4): 831-886.
[preprint]
- Ting, J., D’Souza, A., and Schaal, S.
Bayesian Robot System Identification with Input and Output Noise,
Neural Networks, accepted.
2009:
- Saal H.P., Ting J., and Vijayakumar S.
Active Filtering for Robotic Tactile Learning,
NIPS 2009 Workshop on Adaptive Sensing, Active Learning and Experimental Design:
Theory, Methods and Applications, Poster.
- Ting, J. Bayesian Methods for Autonomous Learning Systems, Phd Thesis,
Department of Computer Science, University of Southern California.
[pdf]
2008:
- Ting, J., Kalakrishnan, M., Vijayakumar, S., and Schaal, S.
Bayesian Kernel Shaping for Control, Advances in Neural Processing Systems (NIPS 2008).
[pdf]
[appendix]
- 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,
Neural Networks: Special Issue on Neuroinformatics, 21(8), 1112-1131.
[pdf]
- Ting, J., D’Souza, A., Vijayakumar, S., and Schaal, S.
A Bayesian Approach to Empirical Local Linearization for Robotics,
International Conferences on Robotics and Automation (ICRA 2008), Pasadena, CA.
[pdf]
[slides]
- Ting, J. and Schaal, S. Local Kernel Shaping for Function Approximation,
Learning Workshop, Snowbird, April 2008, Poster.
2007:
- Ting, J. and Schaal, S. Bayesian Nonparametric Regression with Local Models,
NIPS 2007 Workshop on Robotic Challenges for Machine Learning, Poster.
- Ting, J., D’Souza, A., Yamamoto, K., Yoshioka, T., Hoffman, D., Kakei, S., Sergio, L.,
Kalaska, J., Kawato, M., Strick, P., and Schaal, S. Using variational Bayesian least squares
for EMG data prediction from M1 and premotor cortex neural firing,
Abstracts of the 37th Meeting of the Society of Neuroscience (SFN 2007), San Diego, CA.
[poster]
- Ting, J., Theodorou, E., and Schaal, S. Learning an Outlier-Robust Kalman Filter,
European Conference on Machine Learning (ECML 2007), Warsaw, Poland.
[pdf]
[workshop slides]
- Ting, J., D’Souza, A., and Schaal, S. Automatic Outlier Detection: A Bayesian Approach,
International Conference on Robotics and Automation (ICRA 2007), Rome, Italy.
[pdf]
[slides]
- Ting, J., Theodorou, E., and Schaal, S. A Kalman filter for robust outlier detection,
IEEE International Conference on Intelligent Robotics Systems (IROS 2007), San Diego, CA.
[pdf]
[slides]
2006:
- Ting, J., D’Souza, A., and Schaal, S. Bayesian Regression with Input Noise for High Dimensional Data,
International Conference on Machine Learning (ICML 2006), Pittsburgh, PA.
[pdf]
[slides]
- Ting, J., Mistry, M., Peters, J., Schaal, S., and Nakanishi, J. A Bayesian Approach to Nonlinear Parameter
Identification for Rigid Body Dynamics, Robotics: Science and Systems (RSS 2006), Philadelphia, PA.
[pdf]
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,
Advances in Neural Information Processing Systems (NIPS 2005).
[pdf]
2004:
- Ting, J., D’Souza, A., and Schaal, S.
Predicting EMG Activity from Neural Firing in M1 with Bayesian Backfitting,
11th Joint Symposium of Neural Computation (JSNC 2004).
Workshops Organized:
Links:
