Machine Learning (Theory)

5/2/2016

Room Sharing for ICML (and COLT, and ACL, and IJCAI)

My greatest concern with the many machine learning conferences in New York this year was the relatively high cost that implied, particularly for hotel rooms in Manhattan. Keeping the conference affordable for graduate students seems critical to what ICML is really about.

The price becomes much more reasonable if you can find roommates to share the price. For example, the conference hotel can have 3 beds in a room.

This still leaves a coordination problem: How do you find plausible roommates? If only there was a website where the participants in a conference could look for roommates. Oh wait, there is. Conferenceshare.co is something new which might measurably address the cost problem. Obviously, you’ll want to consider roommate possibilities carefully, but now at least there is a place to meet.

Note that the early registration deadline for ICML is May 7th.

4/8/2016

ICML registration is live

Here. I would recommend registering early because there is a difficult to estimate(*) chance you will not be able to register later.

The program is shaping up and should be of interest. The 9 Tutorials(**), 4 Invited Speakers, and 23 Workshops are all chosen, with paper decisions due out in a couple weeks.

Early Full (after May 7)
Student 510 640
Regular 840 1050

These numbers are as aggressively low as the local chairs and I can sleep with at night. The prices are higher than I’d like (New York is expensive), but a bit lower than last year, particularly for students(***).

(*) Relevant facts:

  1. ICML 2016: submissions up 30% to 1300.
  2. NIPS 2015 in Montreal: 3900 registrations (way up from last year).
  3. NIPS 2016 is in Barcelona.
  4. ICML 2015 in Lille: 1670 registrations.
  5. KDD 2014 in NYC: closed@3000 registrations 1 week before the conference.

I tried to figure out how to setup a prediction market to estimate what will happen this year, but didn’t find an easy-enough way to do that.

(**) I kind of wish we could make up the titles. How about: “Go is Too Easy” and “My Neural Network is Deeper than Yours”?

(***) Sponsors are very generous and are mostly giving to defray student costs. Approximately every dollar of the difference between Regular and Student registration is due to company donations. For students, also note that there will be some scholarship opportunities to defray costs coming out soon.

12/14/2015

Interesting things at NIPS 2015

Tags: Conferences,Machine Learning jl@ 1:43 pm

NIPS is getting big. If you think of each day as a conference crammed into a day, you get a good flavor of things. Here are some of the interesting things I saw.

Two other notable events happened during NIPS.

  1. The Imagenet challenge and MS COCO results came out. The first represents a significant improvement over previous years (details here).
  2. The Open AI initiative started. Concerned billionaires create a billion dollar endowment to advance AI in a public(NOT Private) way. What will be done better than NSF (which has a similar(ish) goal)? I can think of many possibilities.

See also Seb’s post.

10/30/2015

ICML 2016 in NYC and KDD Cup 2016

ICML 2016 is in New York City. I expect it to be the largest ICML by far given the destination—New York is the place which is perhaps easiest to reach from anywhere in the world and it has the largest machine learning meetup anywhere in the world.

I am the general chair this year, which is light in work but heavy in responsibilities. Some things I worry about:

  1. How many people will actually come? Numbers are difficult to guess with the field growing and the conference changing locations. I believe we need capacity for at least 3000 people based on everything I know.
  2. New York is expensive. What can be done about it? One thought is that we should actively setup a roommate finding system so the costs of hotels can be shared. Up to 3 people can share a hotel room for the conference hotel (yes, each with their own bed), and that makes the price much more reasonable. I’m also hoping donations will substantially defray the cost. If others have creative ideas, I’m definitely interested.

Markus Weimer also points out the 2016 KDD Cup which has a submission deadline of December 6. KDD Cup datasets have become common reference for many machine learning papers, so this is a good way to get your problem solved well by many people.

1/7/2015

The NIPS experiment

Tags: Conferences,Machine Learning jl@ 2:38 pm

Corinna Cortes and Neil Lawrence ran the NIPS experiment where 1/10th of papers submitted to NIPS went through the NIPS review process twice, and then the accept/reject decision was compared. This was a great experiment, so kudos to NIPS for being willing to do it and to Corinna & Neil for doing it.

The 26% disagreement rate presented at the conference understates the meaning in my opinion, given the 22% acceptance rate. The immediate implication is that between 1/2 and 2/3 of papers accepted at NIPS would have been rejected if reviewed a second time. For analysis details and discussion about that, see here.

Let’s give P(reject in 2nd review | accept 1st review) a name: arbitrariness. For NIPS 2014, arbitrariness was ~60%. Given such a stark number, the primary question is “what does it mean?”

Does it mean there is no signal in the accept/reject decision? Clearly not—a purely random decision would have arbitrariness of ~78%. It is however quite notable that 60% is much closer to 78% than 0%.

Does it mean that the NIPS accept/reject decision is unfair? Not necessarily. If a pure random number generator made the accept/reject decision, it would be ‘fair’ in the same sense that a lottery is fair, and have an arbitrariness of ~78%.

Does it mean that the NIPS accept/reject decision could be unfair? The numbers give no judgement here. It is however a natural fallacy to imagine that random judgements derived from people implies unfairness, so I would encourage people to withhold judgement on this question for now.

Is an arbitrariness of 0% the goal? Achieving 0% arbitrariness is easy: just choose all papers with an md5sum that ends in 00 (in binary). Clearly, there is something more to be desired from a reviewing process.

Perhaps this means we should decrease the acceptance rate? Maybe, but this makes sense only if you believe that arbitrariness is good, as it will almost surely increase the arbitrariness. In the extreme case where only one paper is accepted, the odds of it being the rejected on re-review are near 100%.

Perhaps this means we should increase the acceptance rate? If all papers submmitted were accepted, the arbitrariness would be 0, but as mentioned above arbitrariness 0 is not the goal.

Perhaps this means that NIPS is a very broad conference with substantial disagreement by reviewers (and attendees) about what is important? Maybe. This even seems plausible to me, given anecdotal personal experience. Perhaps small highly-focused conferences have a smaller arbitrariness?

Perhaps this means that researchers submit themselves to an arbitrary process for historical reasons? The arbitrariness is clear, but the reason less so. A mostly-arbitrary review process may be helpful in the sense that it gives authors a painful-but-useful opportunity to debug the easy ways to misinterpret their work. It may also be helpful in that it perfectly rejects the bottom 20% of papers which are actively wrong, and hence harmful to the process of developing knowledge. None of these reasons are confirmed of course.

Is it possible to do better? I believe the answer is “yes”, but it should be understood as a fundamentally difficult problem. Every program chair who cares tries to tweak the reviewing process to be better, and there have been many smart program chairs that tried hard. Why isn’t it better? There are strong nonvisible constraints on the reviewers time and attention.

What does it mean? In the end, I think it means two things of real importance.

  1. The result of the process is mostly arbitrary. As an author, I found rejects of good papers very hard to swallow, especially when the reviews were nonsensical. Learning to accept that the process has a strong element of arbitrariness helped me deal with that. Now there is proof, so new authors need not be so discouraged.
  2. CMT now has a tool for measuring arbitrariness that can be widely used by other conferences. Joelle and I changed ICML 2012 in various ways. Many of these appeared beneficial and some stuck, but others did not. In the long run, it’s the things which stick that matter. Being able to measure the review process in a more powerful way might be beneficial in getting good review practices to stick.

Other commentary from Lance, Bert, and Yisong.

Edit: Cross-posted on CACM.

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