Adam Klivans, points out the COLT call for papers. The important points are:
- Due Feb 13.
- Montreal, June 18-21.
- This year, there is author feedback.
ICML Reviewing Criteria
Michael Littman and Leon Bottou have decided to use a franchise program chair approach to reviewing at ICML this year. I’ll be one of the area chairs, so I wanted to mention a few things if you are thinking about naming me.
- I take reviewing seriously. That means papers to be reviewed are read, the implications are considered, and decisions are only made after that. I do my best to be fair, and there are zero subjects that I consider categorical rejects. I don’t consider several arguments for rejection-not-on-the-merits reasonable.
- I am generally interested in papers that (a) analyze new models of machine learning, (b) provide new algorithms, and (c) show that they work empirically on plausibly real problems. If a paper has the trifecta, I’m particularly interested. With 2 out of 3, I might be interested. I often find papers with only one element harder to accept, including papers with just (a).
- I’m a bit tough. I rarely jump-up-and-down about a paper, because I believe that great progress is rarely made. I’m not very interested in new algorithms with the same theorems as older algorithms. I’m also cautious about new analysis for older algorithms, since I like to see analysis driving algorithm rather than vice-versa. I prioritize a proof-of-possibility over a quantitative improvement. I consider quantitative improvements of small constant factors in sample complexity significant. For computationaly complexity, I generally want to see at least an order of magnitude improvement. I generally disregard any experiments on toy data, because I’ve found that toy data and real data can too-easily differ in their behavior.
- My personal interests are pretty well covered by existing papers, but this is perhaps not too important a criteria, compared to the above, as I easily believe other subjects are interesting.
A Healthy COLT
A while ago, we discussed the health of COLT. COLT 2008 substantially addressed my concerns. The papers were diverse and several were interesting. Attendance was up, which is particularly notable in Europe. In my opinion, the colocation with UAI and ICML was the best colocation since 1998.
And, perhaps best of all, registration ended up being free for all students due to various grants from the Academy of Finland, Google, IBM, and Yahoo.
A basic question is: what went right? There seem to be several answers.
- Cost-wise, COLT had sufficient grants to alleviate the high cost of the Euro and location at a university substantially reduces the cost compared to a hotel.
- Organization-wise, the Finns were great with hordes of volunteers helping set everything up. Having too many volunteers is a good failure mode.
- Organization-wise, it was clear that all 3 program chairs were cooperating in designing the program.
- Facilities-wise, proximity in time and space made the colocation much more real than many others have been in the past.
- Program-wise, COLT notably had two younger program chairs, Tong and Rocco, which seemed to work well.
Rise of the Machines
On the enduring topic of how people deal with intelligent machines, we have this important election bulletin.
New York’s ML Day
I’m not as naturally exuberant as Muthu 2 or David about CS/Econ day, but I believe it and ML day were certainly successful.
At the CS/Econ day, I particularly enjoyed Toumas Sandholm’s talk which showed a commanding depth of understanding and application in automated auctions.
For the machine learning day, I enjoyed several talks and posters (I better, I helped pick them.). What stood out to me was number of people attending: 158 registered, a level qualifying as “scramble to find seats”. My rule of thumb for workshops/conferences is that the number of attendees is often something like the number of submissions. That isn’t the case here, where there were just 4 invited speakers and 30-or-so posters. Presumably, the difference is due to a critical mass of Machine Learning interested people in the area and the ease of their attendance.
Are there other areas where a local Machine Learning day would fly? It’s easy to imagine something working out in the San Francisco bay area and possibly Germany or England.
The basic formula for the ML day is a committee picks a few people to give talks, and posters are invited, with some of them providing short presentations. The CS/Econ day was similar, except they managed to let every submitter do a presentation. Are there tweaks to the format which would improve things?