
Machine learning reductions are about reusing solutions to
simple, core problems in order to solve more complex problems.
A basic difficulty in applying machine learning in practice is that we
often need to solve problems that don't quite match the problems
solved by standard machine learning algorithms.
Reductions are techniques that transform such practical problems into
core machine learning problems.
These can then be solved
using any existing learning algorithm whose solution can, in
turn, be used to solve the original problem.
The material that we plan to cover is
both algorithmic and analytic. We will discuss existing and new algorithms,
along with the methodology for analyzing and creating new reductions.
We will also discuss common design flaws in folklore reductions.
In our experience, this approach is an effective tool for designing
empirically successful, automated solutions to learning problems.


Slides


Alina Beygelzimer is a research
staff member at IBM Research, working on machine learning
(including reductions) and
algorithms. She has taught reductions as a part of a graduate
machine learning course at Columbia University.
John Langford
is a senior research scientist at Yahoo! Research.
His work includes research in machine learning, learning theory,
algorithms, game theory, steganography, and Captchas.
John has worked extensively on formalizing, analyzing, and teaching reductions.
Bianca Zadrozny
is a professor in the computer science department
at UFF in Brazil, where she has taught several classes and receives
support from CNPq (Brazil's National Research Council) to conduct
research in machine learning. Prior to that, she was a research staff
member at IBM Research where, among other things, she worked on reductions.


John's reductions page, with links to written and video tutorials, a workshop, and related papers.
Additional references:
Costsensitive classification:
P. Domingos. MetaCost: A general method for making classifiers costsensitive, KDD99.
Multiclass classification:
E. Allwein, R. Schapire, and Y. Singer.
Reducing multiclass to binary: A unifying approach for margin classifiers,
Journal of Machine Learning Research, 2000.
K. Crammer and Y. Singer. On the learnability and design of output codes for multiclass problems, Machine Learning Journal, 2002.
T. Dietterich and G. Bakiri. Solving Multiclass
Learning Problems via ErrorCorrecting Output Codes,
Journal of Artificial Intelligence Research, 1995.
V. Guruswami and A. Sahai.
Multiclass learning, boosting, and
errorcorrecting codes, COLT99.
T. Hastie and R. Tibshirani. Classification by Pairwise
Coupling, Annals of Statistics, 1998.
R. Rifkin and A. Klautau. In Defense of OneVsAll Classification,
Journal of Machine Learning Research, 2004.
Ranking:
N. Ailon and M. Mohri.
An Efficient Reduction of Ranking to Classification, COLT08.


Costing (from importance weighted classification to binary
classification by importanceproportionate rejection sampling)
Probing (from squared loss regression to classification)
Quanting (from quantile regression to classification)
Searn (from structured prediction to binary classification)
Regression Tree (from multiclass regression to binary regression in logarithmic time)
ErrorCorrecting Tournaments (Filter Tree) (from costsensitive multiclass to binary classification)


 ICML 2009
 Where: T1. McGill University, Montreal, Canada.
 When: Sunday, June 14, 911:30am

