There is now a bewildering array of inference problems that techniques from machine learning can address: classification, regression, density estimation, clustering, ranking, recommendation, feature selection, hypothesis testing, and more. Multiply this by some of the modes in which learning can occur (batch vs. online, active vs. passive, partial vs. full feedback, inductive vs. transductive, semi-/un-/supervised) and there is a veritable zoo of problems out there.
As part of a continuing effort to make sense of it all, Bob Williamson, John Langford, Ulrike von Luxburg, Jennifer Wortman Vaughan, and I are running a workshop at NIPS this year titled Relations Between Machine Learning Problems–an approach to unify the field:
This workshop will focus on relations between machine learning problems. The idea is that by better understanding how different machine learning problems relate to each other, we will be able to better understand the field as a whole.
At this stage we plan to have Peter Grünwald and Amos Storkey give invited talks and a panel discussion on “How to build a map of all of machine learning”.
Submissions are due by the end of September.
If you need any further encouragement to join us, I just note that the NIPS workshops are in Spain’s Sierra Nevada this year.
Hope to see you there!
Mark Reid September 2, 2011 Canberra, Australia