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Machine Learning Journal Club Meetings

The meetings are every second friday, 5:00-6:00 PM in the Machine Learning Lab, Pratt 290.

For corrections, to be added to the mailing list, or to volunteer to present a paper please email ilya `at` cs `dot` utoronto.

Future Readings (2008)

• March 7, 2008:
  ???

Past Readings (2007)

• Feb 20, 2008:
  Papers: Probability, Frequency and Reasonable Expectation, by R. T. Cox,
  and
  Thinking: An Invitation to Cognitive Science, Vol 3, chapter 3: Judgement, by D. N Osherson
  (the missing text)
  Presented by Ilya Sutskever.
  
• November 27, 2007:
  Paper: N-Body Problems in Statistical Learning,
  by A. Gray and W. Moore
  Presented by Dustin Lang.
  
• November 13, 2007:
  Paper: Nested sampling for general Bayesian computation,
  by John Skilling
  Presented by Iain Murray.
  
• October 30, 2007:
  Paper: Gaussian Processes for Ordinal Regression,
  Wei Chu, Zoubin Ghahramani
  Presented by Jim Huang.
  
• October 16, 2007: An introduction to ROC analysis,
  Tom Fawcett
  Presented by David Warde-Farley.
  
• October 02, 2007: A New Class of Upper Bounds on the Log Partition Function,
  Wainwright, Jaakkola, Willsky
  Presented by Ilya Sutskever.
  
• July 02, 2007: Bayesian nonparametric latent feature models,
  Ghahramani, Z., Griffiths, T.L., Sollich, P.
  Presented by Ruslan Salakhutdinov.
  

Past Readings (2006)

• November 6, 2006: No meeting
  
• October 30, 2006: Hierarchical Dirichlet Processes,
  by Y.W. Teh, M.I. Jordan, M.J. Beal and D.M. Blei.
  Presented by Ilya Sutskever.
  
• October 23, 2006: Arithmetic coding for data compression,
  by Witten, I. H., Neal, R. M., and Cleary, J. G.
  Presented by Vinod Nair.
  
• October 16, 2006: No meeting.
  
• October 9, 2006: No meeting.
  
• October 2, 2006: Exact Sampling with Coupled Markov Chains and Applications to Statistical Mechanics,
  by James Gary Propp, David Bruce Wilson.
  Presented by Tijmen Tieleman.
• September 2006: No journal club meetings.
• August 29, 2006: PAC-Bayesian Stochastic Model Selection,
  by David A. McAllester.
  Presented by Ilya Sutskever.
• August 22, 2006: Propagation Algorithms for Variational Bayesian Learning,
  by Ghahramani, Z. and Beal, M.J. (NIPS 2001)
  Presented by Ilya Sutskever.
• August 15, 2006: CIAR summer school, thus no journal club.
• August 8, 2006: Reinforcement Learning with Hierarchies of Machines,
  by Ron Parr and Stuart Russell.
  Presented by Ilya Sutskever.
• August 1, 2006: Monte Carlo implementation of Gaussian process models for Bayesian regression and classification,
  by Radford Neal.
  Presented by Andriy Mnih.
• July 25, 2006: Observable Operator Models for Discrete Stochastic Time Series,
  by Herbert Jaeger (Neural Computation 1999).
  Presented by Ruslan Salakhutdinov.
• July 18, 2006: A generalized mean field algorithm for variational inference in exponential families,
  by E.P. Xing, M.I. Jordan, and S. Russell (UAI 2003).
  Presented by Ilya Sutskever.
• July 11, 2006: Bayesian parameter estimation via variational methods,
  by Tommi S. Jaakkola and Michael I. Jordan (Statistics and Computing).
  Presented by Jim Huang.
• July 4, 2006: A family of algorithms for approximate Bayesian inference,
  by Tom Minka.
  Presented by Andriy Mnih.
• June 27, 2006: From Linear Regression to Linear Prediction and Beyond,
  by C. K. I. Williams.
  Presented by Andriy Mnih.
• June 20, 2006: Convex Neural Networks,
  by Y. Bengio, N. Le Roux, P. Vincent, O. Delalleau, P. Marcotte (NIPS 2005).
  Presented by Ilya Sutskever.
• June 15, 2006: Factor Graphs and the Sum-Product Algorithm,
  by Frank R. Kschischang, Brendan J. Frey, Hans-Andrea Loeliger (IEEETIT 2001).
  Presented by Ilya Sutskever.

Past Readings (2005)

• July 26, 2005: Kernel conditional random fields: Representation and clique selection,
  by John Lafferty, Xiaojin Zhu, and Yan Liu (ICML 2004).
• July 19, 2005: Semi-Markov Conditional Random Fields for Information Extraction,
  by Sarawagi and Cohen (NIPS 2004).
• June 28, 2005: Max-Margin Markov Networks,
  by Taskar, Guestrin, and Koller (NIPS 2003).
• June 21, 2005: Hidden Markov Support Vector Machines,
  by Altun, Tsochantaridis, and Hofmann (ICML 2003).
• June 14, 2005: Discriminative Learning for Label Sequences via Boosting,
  by Altun, Hofmann, and Johnson (NIPS 2002).
  You can find a brief overview/refresher on boosting here.
• May 24, 2005: Discriminative Random Fields: A Discriminative Framework for Contextual Interaction in Classification,
  by S. Kumar and M. Hebert (ICCV 2003).
• May 17, 2005: Conditional Random Fields for Object Recognition,
  by Ariadna Quattoni, Michael Collins, and Trevor Darrell (NIPS 2004).
• May 10, 2005: Conditional random fields: Probabilistic models for segmenting and labeling sequence data,
  by John Lafferty, Andrew McCallum, and Fernando Pereira (ICML 2001).
  Supplemental reading (that might be helpful): Conditional Random Fields: An Introduction, by Hanna M. Wallach.