I ran a grad seminar in reinforcement learning this past semester, which was a lot of fun and also gave me an opportunity to catch up on some stuff I'd been meaning to learn but haven't had a chance and old stuff I'd largely forgotten about. It's hard to believe, but my first RL paper was eleven years ago at a NIPS workshop where Daniel Marcu, John Langford and I had a first paper on reducing stru

my biased thoughts on the fields of natural language processing (NLP), computational linguistics (CL) and related topics (machine learning, math, funding, etc.) In terms of (importance divided-by glamour), hyperparameter (HP) search is probably pretty close to the top. We all hate finding hyperparameters. Default settings are usually good, but you're always left wondering: could I have done better

my biased thoughts on the fields of natural language processing (NLP), computational linguistics (CL) and related topics (machine learning, math, funding, etc.) Usual caveats: didn't see all papers, blah blah blah. Also look for #acl14nlp on twitter -- lots of papers were mentioned there too! A Tabular Method for Dynamic Oracles in Transition-Based Parsing; Yoav Goldberg, Francesco Sartorio, Giorg

my biased thoughts on the fields of natural language processing (NLP), computational linguistics (CL) and related topics (machine learning, math, funding, etc.) I took part in a wonderful Dagstuhl workshop this past February on translating morphologically rich languages. (Yeah, I also don't really know why I was invited :P.) But many thanks to Alex, Kevin, Philipp, Helmut and Hans for inviting me.

my biased thoughts on the fields of natural language processing (NLP), computational linguistics (CL) and related topics (machine learning, math, funding, etc.) One of the highlights for me at NAACL was the *SEM panel on "Toward Deep NLU", which had the following speakers: Kevin Knight (USC/ISI), Chris Manning (Stanford), Martha Palmer (UC Boulder), Owen Rambow (Columbia) and Dan Roth (UIUC). I wa

my biased thoughts on the fields of natural language processing (NLP), computational linguistics (CL) and related topics (machine learning, math, funding, etc.) I feel a bit odd doing my "what I liked at NAACL 2013" as one of the program chairs, but not odd enough to skip what seems to be the most popular type of post :). First, though, since Katrin Kirchhoff (my co-chair) and I never got a chance

my biased thoughts on the fields of natural language processing (NLP), computational linguistics (CL) and related topics (machine learning, math, funding, etc.) I had a post a while back on teaching ML that I still basically stand by.  I've taught intro grad NLP here at UMD twice now, and a sort-of-similar-course back at Utah once.  I find these courses really hard to teach.  And not for the usual

my biased thoughts on the fields of natural language processing (NLP), computational linguistics (CL) and related topics (machine learning, math, funding, etc.) NIPS is over as of last night.  Overall I thought the program was strong (though I think someone, somewhere, is trying to convince me I need to do deep learning -- or at least that was the topic d'jour... or I guess d'an? this time).  I wa

my biased thoughts on the fields of natural language processing (NLP), computational linguistics (CL) and related topics (machine learning, math, funding, etc.) Like many people, I spent last week in lovely Montreal (at least lovely for the second half) at NAACL.  Despite the somewhat unfortunate submission date, I thought the program this year was quite good.  Of course I didn't see every talk an

my biased thoughts on the fields of natural language processing (NLP), computational linguistics (CL) and related topics (machine learning, math, funding, etc.) By which I mean NIPS, and the incumbent exhaustion of 14 hour days.  (P.s., if you're at NIPS, see the meta-comment I have at the end of this post, even if you skip the main body :P.) Today I went to two tutorials: one by Yee Whye Teh and

my biased thoughts on the fields of natural language processing (NLP), computational linguistics (CL) and related topics (machine learning, math, funding, etc.) As Daniel Hsu and John Langford pointed out recently, there has been a lot of recent progress in active learning. This is to the point where I might actually be tempted to suggest some of these algorithms to people to use in practice, for

my biased thoughts on the fields of natural language processing (NLP), computational linguistics (CL) and related topics (machine learning, math, funding, etc.) My past master's student Adam Teichert (now at JHU) did some work on inducing part of speech taggers using typological information.  We wanted to compare the usefulness of using small amounts of linguistic information with small amounts of