I wrote a BAIR Blog post about some of the work that I’ve been doing this past year with John Canny and other students on minibatch Metropolis-Hastings. Please check it out and let me know what you think. As a side note, we just got commenting enabled for the blog! But please don’t abuse it, of course. (I have had enough of dealing with Internet trolls in my past life, so I would rather not deal with them any more.)
Some context on the post:
It corresponds to a paper (arXiv link) that I’m presenting in Sydney, Australia for the 33rd Conference on Uncertainty in Artificial Intelligence. I’ve never been to Australia anywhere. In fact, I’ve only been to the United States (obviously), Canada (since my mother’s family lived on the border between New York and Canada), Japan (in part to visit a great uncle who lives there and because my grandmother was born and raised in Japan) and … yeah that’s it. My travel resume is embarrassing compared to other Berkeley EECS graduate students, so hopefully this trip will partially rectify that.
I’ve been working on this project for a while. In fact that was one of the reasons why I wrote this blog post back in June 2016 … that was a long time ago, before I really knew deep reinforcement learning and TensorFlow. Wow.
I was worried that it contains too much math and technical detail for a BAIR Blog post, but I figured that (a) you need to know some math to do AI, and (b) we needed a little more diversity in the research topics that we’re presenting, and this gives more of a statistical perspective on AI.
Finally, we didn’t get a blog post last week since many BAIR students were swamped with things to do such as attending conferences (e.g. CVPR) and we’re approaching ICML/UAI/IJCAI season now. As part of the editorial board, I felt “professionally” obligated to keep the blog moving so I instantly took the chance to write something.
Anyway, I hope you enjoy the post!