Earlier this month, I traveled to Finland, where I attended ALT 2011 and presented a paper on learning sparse parity functions in the presence of noise.
a picture I took in Helsinki
COLT (Conference on Learning Theory) and ALT (Algorithmic Learning Theory) are the two main learning theory conferences. Though COLT is both the larger and better known of the two, it seems that ALT is growing stronger with every passing year, and this year was no exception. Not only was the program good, but Sébastien Bubeck gave a timely tutorial on bandits, which deservedly got their own session this year. (I, unfortunately, arrived too late to see the tutorial, but I read the nice slides afterwards.)
I wouldn't be the first to note that the other thing that differentiates ALT from its big brother is that ALT seems to have a broader definition of learning theory, and the conference is therefore more diverse in its topics than a typical COLT. This has clear benefits, but it also means that there are entire sessions where I am pretty lost. Of course, as I've previously noted, not feeling like you have to following everything could also be a benefit when you're attending a conference.
I want to mention one paper I particularly enjoyed, especially because of the model, which illustrates the importance of unlabeled data in the agnostic setting:
"Learning a Classifier when the Labeling is Known" by Shai Ben-David and Shalev Ben-David. This paper works in an agnostic learning model where the learner already knows the labeling function, but wants to find a good succinct representation (i.e. proper learning). How good a particular representation is depends on the distribution the data is coming from, and this is what the algorithm needs to learn by seeing examples. It turns out that this, more generous, model is subject to essentially the same sample complexity lower bound as the common agnostic PAC model.