Yesterday, I tagged VW version 8.5.0 which has many interactive learning improvements (both contextual bandit and active learning), better support for sparse models, and a new baseline reduction which I’m considering making a part of the default update rule.
If you want to know the details, we’ll be doing a mini-tutorial during the Friday lunch break at the Extreme Classification workshop at NIPS. Please join us if interested.
Edit: also announced at the Learning Systems workshop
Alekh and I have been polishin the Real World Interactive Learning tutorial for ICML 2017 on Sunday.
This tutorial should be of pretty wide interest. For data scientists, we are crossing a threshold into easy use of interactive learning while for researchers interactive learning is plausibly the most important frontier of understanding. Great progress on both the theory and especially on practical systems has been made since an earlier NIPS 2013 tutorial.
Please join us if you are interested 🙂
Andrew McCallum has been leading an initiative to update the bylaws of IMLS, the organization which runs ICML. I expect most people aren’t interested in such details. However, the bylaws change rarely and can have an impact over a long period of time so they do have some real importance. I’d like to hear comment from anyone with a particular interest before this year’s ICML.
In my opinion, the most important aspect of the bylaws is the at-large election of members of the board which is preserved. Most of the changes between the old and new versions are aimed at better defining roles, committees, etc… to leave IMLS/ICML better organized.
Anyways, please comment if you have a concern or thoughts.
This spring, I taught a class on Machine Learning the Future at Cornell Tech covering a number of advanced topics in machine learning including online learning, joint (structured) prediction, active learning, contextual bandit learning, logarithmic time prediction, and parallel learning. Each of these classes was recorded from the laptop via Zoom and I just uploaded the recordings to Youtube.
In some ways, this class is a followup to the large scale learning class I taught with Yann LeCun 4 years ago. The videos for that class were taken down(*) so these lectures both update and replace shared subjects as well as having some new subjects.
Much of this material is fairly close to research so to assist other machine learning lecturers around the world in digesting the material, I’ve made all the source available as well. Feel free to use and improve.
(*) The NYU policy changed so that students could not be shown in classroom videos.
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