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Monday announcements

Various people want to use hunch.net to announce things. I’ve generally resisted this because I feared hunch becoming a pure announcement zone while I am much more interested contentful posts and discussion personally. Nevertheless there is clearly some value and announcements are easy, so I’m planning to summarize announcements on Mondays.

  1. D. Sculley points out an interesting Semisupervised feature learning competition, with a deadline of October 17.
  2. Lihong Li points out the webscope user interaction dataset which is the first high quality exploration dataset I’m aware of that is publicly available.
  3. Seth Rogers points out CrossValidated which looks similar in conception to metaoptimize, but directly using the stackoverflow interface and with a bit more of a statistics twist.
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Fall Machine Learning Events

Many Machine Learning related events are coming up this fall.

  1. September 9, abstracts for the New York Machine Learning Symposium are due. Send a 2 page pdf, if interested, and note that we:
    1. widened submissions to be from anybody rather than students.
    2. set aside a larger fraction of time for contributed submissions.
  2. September 15, there is a machine learning meetup, where I’ll be discussing terascale learning at AOL.
  3. September 16, there is a CS&Econ day at New York Academy of Sciences. This is not ML focused, but it’s easy to imagine interest.
  4. September 23 and later NIPS workshop submissions start coming due. As usual, there are too many good ones, so I won’t be able to attend all those that interest me. I do hope some workshop makers consider ICML this coming summer, as we are increasing to a 2 day format for you. Here are a few that interest me:
    1. Big Learning is about dealing with lots of data. Abstracts are due September 30.
    2. The Bayes Bandits workshop. Abstracts are due September 23.
    3. The Personalized Medicine workshop
    4. The Learning Semantics workshop. Abstracts are due September 26.
    5. The ML Relations workshop. Abstracts are due September 30.
    6. The Hierarchical Learning workshop. Challenge submissions are due October 17, and abstracts are due October 21.
    7. The Computational Tradeoffs workshop. Abstracts are due October 17.
    8. The Model Selection workshop. Abstracts are due September 24.
  5. October 16-17 is the Singularity Summit in New York. This is for the AIists and only peripherally about ML.
  6. October 16-21 is a Predictive Analytics World in New York. As machine learning goes industrial, we see industrial-style conferences rapidly developing.
  7. October 21, there is the New York ML Symposium. In addition to what’s there, Chris Wiggins is looking into setting up a session for startups and those interested in them to get to know each other, as last year.
  8. Decembr 16-17 NIPS workshops in Granada, Spain.
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The Large Scale Learning Survey Tutorial

Ron Bekkerman initiated an effort to create an edited book on parallel machine learning that Misha and I have been helping with. The breadth of efforts to parallelize machine learning surprised me: I was only aware of a small fraction initially.

This put us in a unique position, with knowledge of a wide array of different efforts, so it is natural to put together a survey tutorial on the subject of parallel learning for KDD, tomorrow. This tutorial is not limited to the book itself however, as several interesting new algorithms have come out since we started inviting chapters.

This tutorial should interest anyone trying to use machine learning on significant quantities of data, anyone interested in developing algorithms for such, and of course who has bragging rights to the fastest learning algorithm on planet earth 🙂

(Also note the Modeling with Hadoop tutorial just before ours which deals with one way of trying to speed up learning algorithms. We have almost no overlap.)

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Vowpal Wabbit 6.0

I just released Vowpal Wabbit 6.0. Since the last version:

  1. VW is now 2-3 orders of magnitude faster at linear learning, primarily thanks to Alekh. Given the baseline, this is loads of fun, allowing us to easily deal with terafeature datasets, and dwarfing the scale of any other open source projects. The core improvement here comes from effective parallelization over kilonode clusters (either Hadoop or not). This code is highly scalable, so it even helps with clusters of size 2 (and doesn’t hurt for clusters of size 1). The core allreduce technique appears widely and easily reused—we’ve already used it to parallelize Conjugate Gradient, LBFGS, and two variants of online learning. We’ll be documenting how to do this more thoroughly, but for now “README_cluster” and associated scripts should provide a good starting point.
  2. The new LBFGS code from Miro seems to commonly dominate the existing conjugate gradient code in time/quality tradeoffs.
  3. The new matrix factorization code from Jake adds a core algorithm.
  4. We finally have basic persistent daemon support, again with Jake’s help.
  5. Adaptive gradient calculations can now be made dimensionally correct, following up on Paul’s post, yielding a better algorithm. And Nikos sped it up further with SSE native inverse square root.
  6. The LDA core is perhaps twice as fast after Paul educated us about SSE and representational gymnastics.

All of the above was done without adding significant new dependencies, so the code should compile easily.

The VW mailing list has been slowly growing, and is a good place to ask questions.

Enjoy.

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ICML 2011 and the future

Unfortunately, I ended up sick for much of this ICML. I did manage to catch one interesting paper:

Richard Socher, Cliff Lin, Andrew Y. Ng, and Christopher D. Manning Parsing Natural Scenes and Natural Language with Recursive Neural Networks.

I invited Richard to share his list of interesting papers, so hopefully we’ll hear from him soon. In the meantime, Paul and Hal have posted some lists.

the future

Joelle and I are program chairs for ICML 2012 in Edinburgh, which I previously enjoyed visiting in 2005. This is a huge responsibility, that we hope to accomplish well. A part of this (perhaps the most fun part), is imagining how we can make ICML better. A key and critical constraint is choosing things that can be accomplished. So far we have:

  1. Colocation. The first thing we looked into was potential colocations. We quickly discovered that many other conferences precomitted their location. For the future, getting a colocation with ACL or SIGIR, seems to require more advanced planning. If that can be done, I believe there is substantial interest—I understand there was substantial interest in the joint symposium this year. What we did manage was achieving a colocation with COLT and there is an outside chance that a machine learning summer school will precede the main conference. The colocation with COLT is in both time and space, with COLT organized as (essentially) a separate track in a nearby building. We look forward to organizing a joint invited session or two with the COLT program chairs.
  2. Tutorials. We don’t have anything imaginative here, except for pushing for quality tutorials, probably through a mixture of invitations and a call. There is a small chance we’ll be able to organize a machine learning summer school as a prequel, which would be quite cool, but several things have to break right for this to occur.
  3. Conference. We are considering a few tinkerings with the conference format.
    1. Shifting a conference banquet to be during the workshops, more tightly integrating the workshops.
    2. Having 3 nights of posters (1 per day) rather than 2 nights. This provides more time/poster, and avoids halving talks and posters appear on different days.
    3. Having impromptu sessions in the evening. Two possibilities here are impromptu talks and perhaps a joint open problems session with COLT. I’ve made sure we have rooms available so others can organize other things.
    4. We may go for short presentations (+ a poster) for some papers, depending on how things work out schedulewise. My opinions on this are complex. ICML is traditionally multitrack with all papers having a 25 minute-ish presentation. As a mechanism for research, I believe this is superior to a single track conference of a similar size because:
      1. Typically some talk of potential interest can always be found by participants avoiding the boredom problem which comes up at a single track conference
      2. My experience is that program organizers have a limited ability to foresee which talks are of most interest, commonly creating a misallocation of attention.

      On the other hand, there are clearly limits to the number of tracks that are reasonable, and I feel like ICML (especially with COLT cotimed) is near the upper limit. There are also some papers which have a limited scope of interest, for which a shorter presentation is reasonable.

  4. Workshops. A big change here—we want to experiment with 2 days of workshops rather than 1. There seems to be demand for it, as the number of workshops historically is about 10, enough that it’s easy to imagine people commonly interested in 2 workshops. It’s also the case that NIPS has had to start rejecting a substantial fraction of workshop submissions for space reasons. I am personally a big believer in workshops as a mechanism for further research, so I hope this works out well.
  5. Journal integration. I tend to believe that we should be shifting to a journal format for ICML papers, as per many past discussions. After thinking about this the easiest way seems to be simply piggybacking on existing journals such as JMLR and MLJ by essentially declaring that people could submit there first, and if accepted, and not otherwise presented at a conference, present at ICML. This was considered too large a change, so it is not happening. Nevertheless, it is a possible tweak that I believe should be considered for the future. My best guess is that this would never displace the baseline conference review process, but it would help some papers that don’t naturally fit into a conference format while keeping quality high.
  6. Reviewing. Drawing on plentiful experience with what goes wrong, I think we can create the best reviewing system for conferences. We are still debating exact details here while working through what is possible in different conference systems. Nevertheless, some basic goals are:
    1. Double Blind [routine now] Two identical papers with different authors should have the same chance of success. In terms of reviewing quality, I think double blind makes little difference in the short term, but the public commitment to fair reviewing makes a real difference in the long term.
    2. Author Feedback [routine now] Author feedback makes a difference in only a small minority of decisions, but I believe its effect is larger as (a) reviewer quality improves and (b) reviewer understanding improves. Both of these are silent improvers of quality. Somewhat less routine, we are seeking a mechanism for authors to be able to provide feedback if additional reviews are requested, as I’ve become cautious of the late-breaking highly negative review.
    3. Paper Editing. Geoff Gordon tweaked AIStats this year to allow authors to revise papers during feedback. I think this is helpful, because it encourages authors to fix clarity issues immediately, rather than waiting longer. This helps with some things, but it is not a panacea—authors still have to convince reviewers their paper is worthwhile, and given the way people are first impressions are lasting impressions.
    4. Multisource reviewing. We want all of the initial reviews to be assigned by good yet different mechanisms. In the past, I’ve observed that the source of reviewer assignments can greatly bias the decision outcome, all the way from “accept with minor revisions” to “reject” in the case of a JMLR submission that I had. Our plan at the moment is that one review will be assigned by bidding, one by a primary area chair, and one by a secondary area chair.
    5. No single points of failure. When Bob Williamson and I were PC members for learning theory at NIPS, we each came to a decisions given reviews and then reconciled differences. This made a difference on about 5-10% of decisions, and (I believe) improved overall quality a bit. More generally, I’ve seen instances where an area chair has an unjustifiable dislike for a paper and kills it off, which this mechanism avoids.
    6. Speed. In general, I believe speed and good decision making are antagonistic. Nevertheless, we believe it is important to try to do the reviewing both quickly and well. Doing things quickly implies that we can push the submission deadline back later, providing authors more time to make quality papers. Key elements of doing things well fast are: good organization (that’s all on us), light loads for everyone involved (i.e. not too many papers), crowd sourcing (i.e. most decisions made by area chairs), and some amount of asynchrony. Altogether, we believe at the moment that two weeks can be shaved from our reviewing process.
  7. Website. Traditionally at ICML, every new local organizer was responsible for creating a website. This doesn’t make sense anymore, because substantial work is required there, which can and should be amortized across the years so that the website can evolve to do more for the community. We plant to create a permanent website, based around some combination of icml.cc and machinelearning.org. I think this just makes sense.
  8. Publishing. We are thinking about strongly encouraging authors to use arxiv for final submissions. This provides a lasting backing store for ICML papers, as well as a mechanism for revisions. The reality here is that some mistakes get into even final drafts, so a way to revise for the long term is helpful. We are also planning to videotape and make available all talks, although a decision between videolectures and Weyond has not yet been made.

Implementing all the changes above is ambitious, but I believe feasible and that each is individually beneficial and to some extent individually evaluatable. I’d like to hear any thoughts you have on this. It’s also not too late if you have further suggestions of your own.