The extreme classification workshop at ICML 2015 this year was a blast. We started strong, with Manik Varma demonstrating how to run circles around other learning algorithms using a commodity laptop; and we finished strong, with Alexandru Niculescu delivering the elusive combination of statistical and computational benefit via “one-against-other-reasonable-choices” inference. Check out the entire program!
Regarding upcoming events, ECML 2015 will have an extreme learning workshop under the moniker of Big Multi-Target Prediction. Furthermore, there are rumors of a NIPS 2015 workshop.
In the meantime, I've pushed to github a reference implementation of the extreme embedding and classification techniques on which Nikos and I have been working. There are two very simple ideas at work here. The first is the use of the (randomized) SVD of a particular matrix as a label embedding, and the second is the use of randomized approximations to kernel machines.
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