December 1, 2000: Beaver Run Resort (Peaks 9/10),
Cross-Validation, Bootstrap and Model Selection
Cross-validation and bootstrap are popular methods for estimating
generalization error based on resampling a limited pool of data,
and have become widely-used for model selection. The aim of this
workshop is to bring together researchers from both matchine learning
and statistics in an informal setting to discuss current issues in
resampling-based techniques. These include:
This one-day workshop will begin with an invited talk by Brad
Efron, Professor of Statistics (Stanford) and inventor of
the bootstrap algorithm. The remainder of the morning
session will feature four talks focusing primarily on theoretical
issues. The evening session will consist of four talks describing
applications and new algorithms. Each 20 minute presentation
will be followed by 10 minutes of discussion where interesting
questions raised during the talk can be explored in some detail.
Extended abstracts and other materials will be made available here, in advance
so that workshop participants may prepare for the discussion sessions.
- Improving theoretical bounds on cross-validation, bootstrap or other resampling-based methods;
- Empirical or theoretical comparisons between resampling-based methods and other forms of model selection;
- Exploring the issue of overfitting in sampling-based methods;
- Efficient algorithms for estimating generalization error;
- Novel resampling-based approaches to model selection.
Call for Papers
The workshop submissions are closed. Accepted papers are listed in the
Extended abstracts are linked to the paper title above.
This section contains pre-prints to full papers and additional related work.
The workshop organizers can be contacted by email at
or at the phone/fax numbers listed below.
Last updated: November 26, 2000