Homepage of Jin Yu
Ph.D. Student
Research School of Information Sciences & Engineering, Australian National University
Statistical Machine Learning, National ICT Australia
Phone: +61 2 6267 6302
Email: jin {dot} yu {at} anu.edu.au
Mail:   SML, Locked Bag 8001, Canberra ACT 2601, Australia
From Oct. 2009, I have joined the University of Adelaide for post-doctoral research with Professor David Suter. Here is my new homepage.
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 Professor S.V.N. Vishwanathan.
Research Interests
My research interests are in stochastic (online) learning and nonsmooth optimization for machine learning.
Publications
2010
Jin Yu, S.V.N. Vishwanathan, Simon Günter and Nicol N. Schraudolph. A Quasi-Newton Approach to Nonsmooth Convex Optimization Problems in Machine Learning. Journal of Machine Learning Research, 11:1145-1200, 2010.
[pdf] [Code]2009
Jin Yu, S.V.N. Vishwanathan and Jian Zhang . The Entire Quantile Path of a Risk-Agnostic SVM Classifier. In Proc. 25th Conf. 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. 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
People
Alexander J. Smola S.V.N. Vishwanathan Peter Sunehag Owen Thomas Xinhua Zhang Choon Hui Teo Tim Sears Dima Kamenetsky
Fun
P.H.D. comics