It was a fine time for learning in Pittsburgh. John and Sam mentioned some of my favorites. Here’s a few more worth checking out:
Online Multitask Learning
Ofer Dekel, Phil Long, Yoram Singer
This is on my reading list. Definitely an area I’m interested in.
Maximum Entropy Distribution Estimation with Generalized Regularization
Miroslav DudÃƒÂk, Robert E. Schapire
Learning near-optimal policies with Bellman-residual minimization based fitted policy iteration and a single sample path
AndrÃƒÂ¡s Antos, Csaba SzepesvÃƒÂ¡ri, RÃƒÂ©mi Munos
Again, on the list to read. I saw Csaba and Remi talk about this and related work at an ICML Workshop on Kernel Reinforcement Learning. The big question in my head is how this compares/contrasts with existing work in reductions to reinforcement learning. Are there advantages/disadvantages?
Higher Order Learning On Graphs> by Sameer Agarwal, Kristin Branson, and Serge Belongie, looks to be interesteding. They seem to poo-poo “tensorization” of existing graph algorithms.
Cover Trees for Nearest Neighbor (Alina Beygelzimer, Sham Kakade, John Langford) finally seems to have gotten published. It’s an embarrassment to the community that it took this long– and a reminder of how diligent one has to be in ensuring good work gets published. This seems to happen on a regular basis. (See A New View of EM.)
Finally, I thought this one was very cool:
Constructing Informative Priors by Rajat Raina, Andrew Y. Ng, Daphne Koller.
Same interest as the first paper on the list.
Check them out!