Machine Learning (Theory)

6/4/2016

The ICML 2016 Space Fight

Tags: Conferences,Machine Learning jl@ 3:29 pm

The space problem started long ago.

At ICML last year and the year before the amount of capacity that needed to fit everyone on any single day was about 1500. My advice was to expect 2000 and have capacity for 2500 because “New York” and “Machine Learning”. Was history right? Or New York and buzz?

I was not involved in the venue negotiations, but my understanding is that they were difficult, with liabilities over $1M for IMLS the nonprofit which oversees ICML year to year. The result was a conference plan with a maximum capacity of 1800 for the main conference, a bit less for workshops, and perhaps 1000 for tutorials.

Then the NIPS registration numbers came in: 3900 last winter. It’s important to understand here that a registration is not a person since not everyone registers for the entire event. Nevertheless, NIPS was very large with perhaps 3K people attending at any one time. Historically, NIPS is the conference most similar to ICML with a history of NIPS being a bit larger. Most people I know treat these conferences as indistinguishable other than timing: ICML in the summer and NIPS in the winter.

Given this, I had to revise my estimate up: We should really have capacity for 3000, not 2500. It also convinced everyone that we needed to negotiate for more space with the Marriott. This again took quite awhile with the result being a modest increase in capacity for the conference (to 2100) and the workshops, but nothing for the tutorials.

The situation with tutorials looked terrible while the situation with workshops looked poor. Acquiring more space at the Marriott looked near impossible. Tutorials require a large room, so we looked into the Kimmel Center at NYU acquiring a large room and increasing capacity to 1450 for the tutorials. We also looked into additional rooms for workshops finding one at Columbia and another at the Microsoft Technology Center which has a large public use room 2 blocks from the Marriott. Other leads did not pan out.

This allowed us to cover capacity through early registration (May 7th). Based on typical early vs. late registration distributions I was expecting registrations might need to close a bit early similar to what happened with KDD in 2014.

Then things blew up. Tutorial registration reached capacity the week of May 23rd, and then all registration stopped May 28th, 3 weeks before the conference. Aside from simply failing to meet demand this also creates lots of problems. What do you do with authors? And when I looked into things in detail for workshops I realized we were badly oversubscribed for some workshops. It’s always difficult to guess which distribution of room sizes is needed to support the spectrum of workshop interests in advance so there were serious problems. What could we do?

The first step was tutorial and main conference registration which reopened last Tuesday using some format changes which allowed us to increase capacity further. We will use simulcast to extra rooms to support larger audiences for tutorials and plenary talks allowing us to up the limit for tutorials to 1590 and for the main conference to 2400. We’ve also shifted the poster session to run in parallel with main tracks rather than in the evening. Now, every paper will have 3-4 designated hours during the day (ending at 7pm) for authors to talk to people individually. As a side benefit, this will also avoid the competition between posters and company-sponsored parties which have become common. We’ll see how this works as a format, but it was unavoidable here: even without increasing registration the existing evening poster session plan was a space disaster.

The workshop situation was much more difficult. I walked all over the nearby area on Wednesday, finding various spaces and getting quotes. I also realized that the largest room at the Crown Plaza could help with our tutorials: it was both bigger and much closer than NYU. On Thursday, we got contract offers from the promising venues and debated into the evening. On Friday morning at 6am the Marriott suddenly gave us a bunch of additional space for the workshops. Looking through things, it was enough to shift us from ‘oversubscribed’ to ‘crowded’ with little capacity to register more given natural interests. We developed a new plan on the fly, changed contracts, negotiated prices down, and signed Friday afternoon.

The local chairs (Marek Petrik and Peder Olsen) and Mary Ellen were working hard with me through this process. Disruptive venue changes 3 weeks before the conference are obviously not the recommended way of doing things:-) And yet it seems to be working out now, much better than I expected last weekend. Here’s the situation:

  1. Tutorials ~1600 registered with capacity for 1850. I expect this to run out of capacity, but it will take a little while. I don’t see a good way to increase capacity further.
  2. The main conference has ~2200 registered with capacity for 2400. Maybe this can be increased a little bit, but it is quite possible the main conference will run out of capacity as well. If it does, only authors will be allowed to register.
  3. Workshops ~1900 registered with capacity for 3000. Only the Deep Learning workshop requires a simulcast. It seems very unlikely that we’ll run out of capacity so this should be the least crowded part of the conference. We even have some left-over little rooms (capacity for 125 or less) that are looking for a creative use if you have one.

In this particular case, “New York” was both part of the problem and much of the solution. Where else can you walk around and find large rooms on short notice within 3 short blocks? That won’t generally be true in the future, so we need to think carefully about how to estimate attendance.

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.

4/19/2016

Quora session

I’m doing a Quora Session today that may be of interest. I’m impressed with both the quality and quantity of questions.

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.

3/13/2016

AlphaGo is not the solution to AI

Congratulations are in order for the folks at Google Deepmind who have mastered Go.

However, some of the discussion around this seems like giddy overstatement. Wired says Machines have conquered the last games and Slashdot says We know now that we don’t need any big new breakthroughs to get to true AI. The truth is nowhere close.

For Go itself, it’s been well-known for a decade that Monte Carlo tree search (i.e. valuation by assuming randomized playout) is unusually effective in Go. Given this, it’s unclear that the AlphaGo algorithm extends to other board games where MCTS does not work so well. Maybe? It will be interesting to see.

Delving into existing computer games, the Atari results (see figure 3) are very fun but obviously unimpressive on about ¼ of the games. My hypothesis for why is that their solution does only local (epsilon-greedy style) exploration rather than global exploration so they can only learn policies addressing either very short credit assignment problems or with greedily accessible polices. Global exploration strategies are known to result in exponentially more efficient strategies in general for deterministic decision process(1993), Markov Decision Processes (1998), and for MDPs without modeling (2006).

The reason these strategies are not used is because they are based on tabular learning rather than function fitting. That’s why I shifted to Contextual Bandit research after the 2006 paper. We’ve learned quite a bit there, enough to start tackling a Contextual Deterministic Decision Process, but that solution is still far from practical. Addressing global exploration effectively is only one of the significant challenges between what is well known now and what needs to be addressed for what I would consider a real AI.

This is generally understood by people working on these techniques but seems to be getting lost in translation to public news reports. That’s dangerous because it leads to disappointment. The field will be better off without an overpromise/bust cycle so I would encourage people to keep and inform a balanced view of successes and their extent. Mastering Go is a great accomplishment, but it is quite far from everything.

Edit: Further discussion here, CACM, here, and KDNuggets.

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