I've spent most of my research career trying to build big, complex
nonparametric
models
[http://jmlr.csail.mit.edu/proceedings/papers/v22/steinhardt12/steinhardt12.pdf]
; however, I've more recently
(This is available in pdf form here
[http://web.mit.edu/jsteinha/www/stats-essay.pdf].)
If you are a newly initiated student into the field of machine learning, it
won't be
I just finished presenting my recent paper on stochastic verification at RSS
2011. There is a conference version online
[http://www.roboticsproceedings.org/rss07/p41.html], with a journal article to
come later.
I have spent the last several months doing applied math, culminating in a
submission of a paper to a robotics conference
[http://www.roboticsconference.org/] (although culminating might be the wrong
word, since
Humans are very good at correctly generalizing rules across categories (at
least, compared to computers). In this post I will examine mechanisms that would
allow us to do this in a reasonably rigorous