Jie Tang
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I am currently a second year computer science Ph.D. student at UC Berkeley. My advisor is Pieter Abbeel. I am interested in applications of machine learning for robotics and control. Projects I have worked on include learning from demonstration for autonomous helicopter aerobatics and policy gradient methods for reinforcement learning. Please visit my publications page for more details. I graduated from Harvard University in Spring 2008 with a bachelor's in Computer Science and Economics. I worked with David Parkes on transitive trust mechanisms and accounting systems.


demonstration video
flag On a Connection between Importance Sampling and the Likelihood Ratio Policy Gradient
as author at  Video Journal of Machine Learning Abstracts - Volume 1,