STatistical AI Reading Group previous readings: September-December, 2006

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September 22nd (Stairmaster: Paulina)

Jelle Kok, Nikos Vlassis.
Collaborative Multiagent Reinforcement Learning by Payoff Propagation.
In Journal of Machine Learning Research 7 (2006) 1789-1828.

October 6th (Stairmaster: Bonbon)

R. Fergus, P. Perona, A. Zisserman.
A Sparse Object Category Model for Efficient Learning and Exhaustive Recognition.
In Proceedings of the 2005 IEEE Conference on Computer Vision and Pattern Recognition (CVPR-05).

October 20th (Stairmasters: Emma and Bhaskara)

Lihong Li, Thomas J. Walsh, Michael L. Littman.
Towards a Unified Theory of State Abstraction for MDPs.
In Ninth International Symposium on Artificial Intelligence and Mathematics (2006).

Nicholas K. Jong, Peter Stone.
State Abstraction Discovery from Irrelevant State Variables.
In Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence (IJCAI-05).

November 3rd (Stairmaster: Natalia)

Charles Kemp, Joshua Tenenbaum, Thomas Griffiths, Takeshi Yamada, and Naonori Ueda.
Learning systems of concepts with an infinite relational model.
In Proceedings of the Twenty-First National Conference on Artificial Intelligence (AAAI-06)

November 17th (Stairmaster: Luke)

Point-Based Value Iteration for Continuous POMDPs.
Josep M. Porta, Nikos Vlassis, Matthijs T.J. Spaan, Pascal Poupart.
In Journal of Machine Learning Research (JMLR), 7(Nov):2329--2367, 2006.

December 15th (Stairmaster: )

Possible Future Readings:

Max Welling, Tom Minka, Yee Whye Teh
Structured Region Graphs: Morphing EP into GBP.
In Proceedings of the 21st Conference on Uncertainty in AI (UAI-05)

S. Sanner, D. McAllester.
Affine algebraic decision diagrams (AADDs) and their application to structured probabilistic inference.
In Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence (IJCAI-05).

Mark Chavira and Adnan Darwiche.
Compiling Bayesian networks with local structure.
In Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence (IJCAI-05).

Brian Milch and Stuart Russell.
General-purpose MCMC inference over relational structures.
In Proceedings of the 22nd Conference on Uncertainty in AI (UAI-06).

Arthur Choi and Adnan Darwiche.
A variational approach for approximating Bayesian networks by edge deletion.
In Proceedings of the 22nd Conference on Uncertainty in AI (UAI-06).

Ilya Shpitser and Judea Pearl.
Identification of conditional interventional distributions.
In Proceedings of the 22nd Conference on Uncertainty in AI (UAI-06).

Vikash Mansinghka, Charles Kemp, Thomas Griffiths, and Joshua Tenenbaum.
Structured priors for structure learning.
In Proceedings of the 22nd Conference on Uncertainty in AI (UAI-06).

Scott Sanner and Craig Boutilier.
Practical linear value-approximation techniques for first-order MDPs.
In Proceedings of the 22nd Conference on Uncertainty in AI (UAI-06).

Ricardo Silva and Zoubin Ghahramani.
Bayesian inference for Gaussian mixed graph models.
In Proceedings of the 22nd Conference on Uncertainty in AI (UAI-06).

Max Welling and Sridevi Parise.
Bayesian Random Fields: The Bethe-Laplace Approximation.
In Proceedings of the 22nd Conference on Uncertainty in AI (UAI-06).

Tal El-Hay, Nir Friedman, Daphne Koller, and Raz Kupferman.
Continuous time markov networks.
In Proceedings of the 22nd Conference on Uncertainty in AI (UAI-06).

Oren Etzioni, Michele Banko, and Michael J. Cafarella.
Machine reading.
In Proceedings of the Twenty-First National Conference on Artificial Intelligence (AAAI-06)