research
Research interests include planning under uncertainty and
reinforcement learning.
A longer
research summary.
Advisor:
Ron Parr
pub
Refereed Conference Papers
Christopher Painter-Wakefield and Ronald Parr.
Greedy Algorithms for Sparse Reinforcement Learning.
International Conference on Machine Learning (ICML 2012).
Full version.
Jeff Johns, Christopher Painter-Wakefield, and Ronald Parr.
Linear Complementarity for Regularized
Policy Evaluation and Improvement. Advances in Neural Information Processing Systems 23 (NIPS 2010).
Ronald Parr, Lihong Li, Gavin Taylor,
Christopher Painter-Wakefield, and Michael Littman.
An Analysis of Linear Models,
Linear Value-Function Approximation, and Feature Selection
for Reinforcement Learning.
International Conference on Machine Learning (ICML 2008). Please also note this addendum.
Ronald Parr, Christopher Painter-Wakefield, Lihong Li,
and Michael Littman.
Analyzing Feature Generation
for Value-Function Approximation.
In Zoubin Ghahramani (Ed.) Proceedings of the 24th
International Conference on Machine Learning (ICML 2007).
Other Papers
Christopher Painter-Wakefield and Ronald Parr.
L1 Regularized Linear Temporal Difference Learning. Technical Report CS-2012-01.
bio
- Currently a PhD candidate in the Department of Computer Science,
Duke University. I am returning to school after
some 10 years as an applications programmer (see job for more).
I am married with two children, both boys, both delightful
and challenging.
- Hobbies (participation varies with season and availability) include gardening,
rock climbing, cooking, and
reading (especially science fiction).
edu
- Spring 2006 TA Assignments
job
- computer programmer from 12/1993 to 8/2005
- most recently (10/1996 - 8/2005) at Duke with
ADG, the
Administrative Development Group, part of
Duke Health Technology Solutions.
- skills (partial list): Java, Smalltalk, C/C++, MATLAB, XML/XSLT, UNIX/Linux, Oracle.
Christopher Painter-Wakefield