Reinforcement learning is much discussed these days with successes like AlphaGo. Wouldn’t it be great if Reinforcement Learning algorithms could easily be used to solve all reinforcement learning problems? But there is a well-known problem: It’s very easy to create natural RL problems for which all standard RL algorithms (epsilon-greedy Q-learning, SARSA, etc…) fail catastrophically. That’s a serious limitation which both inspires research and which I suspect many people need to learn the hard way.
Removing the credit assignment problem from reinforcement learning yields the Contextual Bandit setting which we know is generically solvable in the same manner as common supervised learning problems. I know of about a half-dozen real-world successful contextual bandit applications typically requiring the cooperation of engineers and deeply knowledgeable data scientists.
Can we make this dramatically easier? We need a system that explores over appropriate choices with logging of features, actions, probabilities of actions, and outcomes. These must then be fed into an appropriate learning algorithm which trains a policy and then deploys the policy at the point of decision. Naturally, this is what we’ve done and now it can be used by anyone. This drops the barrier to use down to: “Do you have permissions? And do you have a reasonable idea of what a good feature is?”
A key foundational idea is Multiworld Testing: the capability to evaluate large numbers of policies mapping features to action in a manner exponentially more efficient than standard A/B testing. This is used pervasively in the Contextual Bandit literature and you can see it in action for the system we’ve made at Microsoft Research. The key design principles are:
- Contextual Bandits. Many people have tried to create online learning system that do not take into account the biasing effects of decisions. These fail near-universally. For example they might be very good at predicting what was shown (and hence clicked on) rather that what should be shown to generate the most interest.
- Data Lifecycle support. This system supports the entire process of data collection, joining, learning, and deployment. Doing this eliminates many stupid-but-killer bugs that I’ve seen in practice.
- Modularity. The system decomposes into pieces: exploration library, client library, online learner, join server, etc… because I’ve seen to many cases where the pieces are useful but the system is not.
- Reproducibility. Everything is logged in a fashion which makes online behavior offline reproducible. Consequently, the system is debuggable and hence improvable.
The system we’ve created is open source with system components in mwt-ds and the core learning algorithms in Vowpal Wabbit. If you use everything it enables a fully automatic causally sound learning loop for contextual control of a small number of actions. This is strongly scalable, for example a version of this is in use for personalized news on MSN. It can be either low-latency (with a client side library) or cross platform (with a JSON REST web interface). Advanced exploration algorithms are available to enable better exploration strategies than simple epsilon-greedy baselines. The system autodeploys into a chosen Azure account with a baseline cost of about $0.20/hour. The autodeployment takes a few minutes after which you can test or use the system as desired.
This system is open source and there are many ways for people to help if they are interested. For example, support for the client-side library in more languages, support of other learning algorithms & systems, better documentation, etc… are all obviously useful.
Following up on an interesting suggestion, we are creating a “Birds of a Feather Unworkshop” with a leftover room (Duffy/Columbia) on Thursday and Friday during the workshops. People interested in ad-hoc topics can post a time and place to meet and discuss. Details are here a little ways down.
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:
- 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.
- 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.
- 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.
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.