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Ph.D. Student Research School of Information Sciences & Engineering, Australian National University Statistical Machine Learning, National ICT Australia Phone: +61 2 6267 6302 Email: my_first_name {dot} my_surname {at} anu.edu.au Mail: SML, Locked Bag 8001, Canberra ACT 2601, Australia From Oct. 2009, I have joined the Uninversity of Adelaide for post-doctoral research with Professor David Suter. |
About Me
I obtained my Bachelor's degree in Electrical Engineering from the Civil Aviation University of China and Master's degree in Artificial Intelligence from the Katholieke Universiteit Leuven in Belgium. I'm now a Ph.D. student in the RSISE at the Australian National University. I am sponsored by NICTA, where I am working with the Statistical Machine Learning group under the supervision of A. Professor S.V.N. Vishwanathan.
Research Interests
My research interests are in stochastic (online) learning and nonsmooth optimization for machine learning.
Publications
2009
Jin Yu, S.V.N. Vishwanathan
and Jian
Zhang . The Entire Quantile Path of a Risk-Agnostic
SVM Classifier. To appear at the Conference on Uncertainty in
Artificial Intelligence, Montreal, Canada, 2009.
[pdf]
[Code]
2008
Jin Yu, S.V.N. Vishwanathan Simon Günter and Nicol N. Schraudolph. A quasi-Newton Approach to Nonsmooth Convex Optimization. Submitted to the Journal of Machine Learning, 2008. First revision under preparation.
Jin Yu, S.V.N. Vishwanathan
Simon Günter and Nicol
N. Schraudolph. A quasi-Newton Approach to Nonsmooth Convex
Optimization. In Proc. 25th Intl. Conf. Machine
Learning, Helsinki, Finland, 2008.
[pdf]
[Slide] [Code]
2007
Nicol
N. Schraudolph, Jin Yu, and Simon Günter. A
Stochastic Quasi-Newton Method for Online Convex
Optimization. In Proc. 11th Intl. Conf. Artificial
Intelligence and Statistics (AIstats), pp. 433–440,
Society for Artificial Intelligence and Statistics, San Juan,
Puerto Rico, March
2007.
Details
[bib] [pdf] [mov] [Code]
Silvia
Richter, Douglas
Aberdeen, and Jin Yu. Natural Actor-Critic for
Road Traffic
Optimisation. In Advances in
Neural Information Processing Systems, The MIT Press,
Cambridge, MA, 2007. Pre-proceedings
version
Details
[bib] [pdf]
2006
Nicol
N. Schraudolph, Jin Yu,
and Douglas
Aberdeen. Fast Online Policy Gradient Learning with
SMD Gain Vector
Adaptation. In Advances in
Neural Information Processing Systems,
pp. 1185–1192, The MIT Press, Cambridge, MA,
2006.
Details
[bib] [pdf]
Talks
Online
Limited-Memory Quasi-Newton Training of Support Vector
Machines. Presented at Snowbird, San Juan, Puerto Rico, March
2007.
[pdf]
[mov]
A New Quasi-Newton Method for Nonsmooth Optimization. Presented at Machine Learning Seminar, Purdue University, USA, January 2009
Previous Projects
Fun
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