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
- 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
- 2:30 pm
V. Chandrasekaran, P. A. Parrilo, and A. S. Willsky, Latent Variable Graphical Model Selection via Convex Optimization, Preprint, August 2010.