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

Home

Course Description

Resources

Class mailing list

 

Project Topics

Handouts/Course notes

STAT 518 Web site

STAT/BIOST 572

UW Statistics

STAT 539 Project topics

    REGULARIZATION/SVM
  • The Entire Regularization Path for the Support Vector Machine jmlr.csail.mit.edu/papers/volume5/hastie04a/hastie04a.pdf Ayn L-C
  • Bi-Level Path Following for Cross Validated Solution of Kernel Quantile Regression Saharon Rosset; 10(Nov):2473−2505, 2009. Ryan K.
  • Non-parametric quantile regression by Takeuchi Stefan S.
    STRUCTURED PREDICTION http://www.seas.upenn.edu/~taskar/#pubs
  • Max-Margin Markov Networks, B. Taskar, C. Guestrin and D. Koller. Neural Information Processing Systems Conference (NIPS03), Vancouver, Canada, December 2003. Fiona G.
  • Structured Prediction, Dual Extragradient and Bregman Projections, B. Taskar, S. Lacoste-Julien, and M. Jordan. Journal of Machine Learning Research (JMLR), Special Topic on Machine Learning and Large Scale Optimization.
  • 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. Mike K.
    COMPRESSED SENSING
  • 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/ Andrew M.
  • Least Angle Regression - Stanford University www.stanford.edu/~hastie/Papers/LARS/LeastAngle_2002.pdf Julie M
    SUBMODULAR OPTIMIZATION AND LEARNING
  • 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 sparsityAdam G.
  • Tipping, M. E. (2001). Sparse Bayesian learning and the relevance vector machine. Journal of Machine Learning Research 1, 211-244.Serge S. MANIFOLD LEARNING
  • The sample complexity of testing the manifold hypothesis, by Hariharan Narayanan De M.
  • Maximum likelihood estimation of the dimension, by P. Bickel and L. Levina Evan G.
    Other papers
  • Stagewise Lasso Peng Zhao, Bin Yu; 8(Dec):2701--2726, 2007. (Lasoo+Boosting)
  • Large Margin Methods for Structured and Interdependent Output Variables, Yasemin Altun and Thomas Hofmann and Ioannis Tsochantaridis in G. Bakir, T. Hofmann, B. Scholkopf, A.J. Smola, B. Taskar, and S.V.N. Vishwanathan, editors,
  • Machine Learning with Structured Outputs, MIT Press, 2006. from http://ttic.uchicago.edu/~altun/
  • Informative Sensing Hyun Sung Chang and Yair Weiss and William T. Freeman Submitted to IEEE Transactions on Info Theory ArXiv:0901.4275v1
  • Learning Fourier Sparse Set Functions Peter Stobbe, Andreas Krause ; JMLR 22: 1125-1133, 2012.


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