Statistical Learning: Modeling, Prediction and Computing
STAT 539 Spring Quarter 2012

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STAT 518 Web site

STAT/BIOST 572

UW Statistics

STAT 539 Presentations

    THURSDAY June 14
  • 10:00 am Evan Greene
    A survey of dimension estimation methods starting from "Maximum likelihood estimation of the dimension", by P. Bickel and L. Levina
  • 11:00 am Stefan Sharkansky
    Non-parametric quantile regression by Takeuchi
  • 3:00 pm Mike Karcher
    Exponentiated gradient algorithms for large-margin structured classification, P. Bartlett, M. Collins, B. Taskar and D. McAllester. Neural Information Processing Systems Conference (NIPS04), Vancouver, Canada, December 2004.
  • 4:00 pm Serge Sverdlov
    Tipping, M. E. (2001). Sparse Bayesian learning and the relevance vector machine. Journal of Machine Learning Research 1, 211-244.

    FRIDAY June 15
  • 9:00 am Fiona Grimson
    Max-Margin Markov Networks, B. Taskar, C. Guestrin and D. Koller. Neural Information Processing Systems Conference (NIPS03), Vancouver, Canada, December 2003.
  • 10:00 am Ayn Leslie-Cook
    The Entire Regularization Path for the Support Vector Machine jmlr.csail.mit.edu/papers/volume5/hastie04a/hastie04a.pdf
  • 11:00 am Ryan Kappedal
    Bi-Level Path Following for Cross Validated Solution of Kernel Quantile Regression
    Saharon Rosset; 10(Nov):2473−2505, 2009. Ryan Kappedal

  • 12:30 pm Adam Gustafson
    F. Bach. Learning with Submodular Functions: A Convex Optimization Perspective. Technical Report HAL 00645271, 2011. Submitted to Foundations and Trends in Machine Learning. (from http://www.di.ens.fr/~fbach/) and Structured sparsity
  • 1:30 pm Julie Michelman
    Least Angle Regression - Stanford University www.stanford.edu/~hastie/Papers/LARS/LeastAngle_2002.pdf
  • 2:30 pm Andrew McDavid
    V. Chandrasekaran, P. A. Parrilo, and A. S. Willsky, Latent Variable Graphical Model Selection via Convex Optimization, Preprint, August 2010. from http://ssg.mit.edu/~venkatc/

Contact the instructor at: mmp@stat.washington.edu