The ICML 2019 Conference will be held from June 10-15 in Long Beach, CA — about a month earlier than last year. To encourage reproducibility as well as high quality submissions, this year we have three major changes in place.
There is an abstract submission deadline on Jan 18, 2019. Only submissions with proper abstracts will be allowed to submit a full paper, and placeholder abstracts will be removed. The full paper submission deadline is Jan 23, 2019.
This year, the author list at the paper submission deadline (Jan 23) is final. No changes will be permitted after this date for accepted papers.
Finally, to foster reproducibility, we highly encourage code submission with papers. Our submission form will have space for two optional supplementary files — a regular supplementary manuscript, and code. Reproducibility of results and easy accessibility of code will be taken into account in the decision-making process.
Our full Call for Papers is available here.
Kamalika Chaudhuri and Ruslan Salakhutdinov
ICML 2019 Program Chairs
The outcome of the election for the IMLS (which runs ICML) adds Emma Brunskill and Hugo Larochelle to the board. The current members of the board (and the reason for board membership) are:
President Elect is a 2-year position with little responsibility, but I decided to look into two things. One is the website which seems relatively difficult to navigate. Ideas for how to improve are welcome.
The other is creating a longitudinal reviewer profile. I keenly remember the day after reviews were due when I was program chair (in 2012) which left a panic-inducing number of unfinished reviews. To help with this, I’m planning to create a profile of reviewers which program chairs can refer to in making decisions about who to ask to review. There are a number of ways to do this wrong which I’m avoiding with the following procedure:
- After reviews are assigned, capture the reviewer/paper assignment. Call this set A.
- After reviews are due, capture the completed & incomplete reviews for papers. Call these sets B & C respectively.
- Strip the paper ids from B (completed reviews) turning it into a multiset D of reviewers completed reviews.
- Compute C-A (as a set difference) then turn it into a multiset E of reviewers incomplete reviews.
- Store D & E for long term reference.
- Is objectively defined. Approaches based on subjective measurements seem both fraught with judgment issues and inconsistent. Consider for example the impressive variation we all see in review quality.
- Does not record a review as late for reviewers who are assigned a paper late in the process via step (1) and (4). We want to encourage reviewers to take on the unusual but important late tasks that arrive.
- Does not record a review as late for reviewers who discover they are inappropriate after assignment and ask for reassignment. We want to encourage reviewers to look at their papers early and, if necessary, ask for a paper to be reassigned early.
- Preserves anonymity of paper/reviewer assignments for authors who later become program chairs. The conversion into a multiset removes the paper id entirely.
Overall, my hope is that several years of this will provide a good and useful tool enabling program chairs and good (or at least not-bad) reviewers to recognize each other.
Andrew McCallum has been leading an initiative to update the bylaws of IMLS, the organization which runs ICML. I expect most people aren’t interested in such details. However, the bylaws change rarely and can have an impact over a long period of time so they do have some real importance. I’d like to hear comment from anyone with a particular interest before this year’s ICML.
In my opinion, the most important aspect of the bylaws is the at-large election of members of the board which is preserved. Most of the changes between the old and new versions are aimed at better defining roles, committees, etc… to leave IMLS/ICML better organized.
Anyways, please comment if you have a concern or thoughts.
I went to the European Workshop on Reinforcement Learning and NIPS last month and saw several interesting things.
At EWRL, I particularly liked the talks from:
- Remi Munos on off-policy evaluation
- Mohammad Ghavamzadeh on learning safe policies
- Emma Brunskill on optimizing biased-but safe estimators (sense a theme?)
- Sergey Levine on low sample complexity applications of RL in robotics.
My talk is here. Overall, this was a well organized workshop with diverse and interesting subjects, with the only caveat being that they had to limit registration 🙂
At NIPS itself, I found the poster sessions fairly interesting.
- Allen-Zhu and Hazan had a new notion of a reduction (video).
- Zhao, Poupart, and Gordon had a new way to learn Sum-Product Networks
- Ho, Littman, MacGlashan, Cushman, and Austerwell, had a paper on how “Showing” is different from “Doing”.
- Toulis and Parkes had a paper on estimation of long term causal effects.
- Rae, Hunt, Danihelka, Harley, Senior, Wayne, Graves, and Lillicrap had a paper on large memories with neural networks.
- Hardt, Price, and Srebro, had a paper on Equal Opportunity in ML.
Format-wise, I thought the 2 sessions was better than 1, but I really would have preferred more. The recorded spotlights are also pretty cool.
The NIPS workshops were great, although I was somewhat reminded of kindergarten soccer in terms of lopsided attendance. This may be inevitable given how hot the field is, but I think it’s important for individual researchers to remember that:
- There are many important directions of research.
- You personally have a much higher chance of doing something interesting if everyone else is not doing it also.
During the workshops, I learned about ADAM (a momentum form of Adagrad), testing ML systems, and that even TenserFlow is finally looking into synchronous updates for parallel learning (allreduce is the way).
(edit: added one)
The ICML 2016 videos are out.
I also wanted to share some statistics from registration that might be of general interest.
The total number of people attending: 3103.
Industry: 47% University: 46%
Male: 83% Female: 14%
Local (NY, NJ, or CT): 27%
North America: 70% Europe: 18% Asia: 9% Middle East: 2% Remainder: <1% including 2 from Antarctica 🙂