STatistical AI Reading Group previous readings: February-May, 2005

February 11th-18th (Stairmaster: Hanna):
  1. Parameter learning of logic programs for symbolic-statistical modeling, by Sato, T. and Kameya, Y., JAIR 2001.
  2. A dynamic programming approach to parameter learning of generative models with failure., by Sato, T. and Kameya, Y., Proceedings of ICML Workshop on Statistical Relational Learning, 2004.
February 25th (Stairmaster: Luke):
  1. The Statistical Approach to the Design of Spoken Dialogue Systems, by S. Young. Tech Report CUED/F-INFENG/TR.433, Cambridge University Engineering Department.
March 4th (Stairmaster: Yu-han):
  1. Online bounds for Bayesian Algorithms, by Sham Kakade and Andrew Ng, NIPS-2004/5.
March 11th (Stairmaster: Hanna):
  1. Conditional Models of Identity Uncertainty with Application to Noun Coreference, by Andrew McCallum and Ben Wellner, NIPS 2004
March 18th (Stairmaster: Yu-han):
  1. Online bounds for Bayesian Algorithms, by Sham Kakade and Andrew Ng, NIPS-2004/5.
March 25th : Spring Break
April 1st (Stairmaster: Natalia):
  1. Graph kernels and Gaussian processes for relational reinforcement learning, by Thomas Gartner, Kurt Driessens, and Jan Ramon.
April 8th (Stairmasters: Luiz and Natalia):
  1. Introduction to Gaussian Processes, by D.J.C. MacKay.
April 15th (Stairmaster: Mike):
  1. Tree-Based Reparameterization Framework for Analysis of Sum-Product and Related Algorithms, by Martin Wainwright, Tommi Jaakkola, and Alan Willsky (IEEE Transactions on Information Theory, May 2003)
April 29th (Stairmaster: John):
  1. Hierarchical Dirichlet Processes, by Y.W. Teh, M.I. Jordan, M.J. Beal and D.M. Blei. Technical Report 653, UC Berkeley Statistics, 2004.
May 6th (Stairmaster: John):
...the same as the previous week.
May 13th (Stairmaster: Luke):
  1. Learning Partially Observable Deterministic Action Models, by E. Amir, in 19th Intl' Joint Conference on Artificial Intelligence (IJCAI'05).
May 27th (Stairmaster: Emma):
  1. A Biologically Plausible Algorithm for Reinforcement-Shaped Representational Learning, by Maneesh Sahani.