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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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NIPS 2010

I enjoyed attending NIPS this year, with several things interesting me. For the conference itself:

  1. Peter Welinder, Steve Branson, Serge Belongie, and Pietro Perona, The Multidimensional Wisdom of Crowds. This paper is about using mechanical turk to get label information, with results superior to a majority vote approach.
  2. David McAllester, Tamir Hazan, and Joseph Keshet Direct Loss Minimization for Structured Prediction. This is about another technique for directly optimizing the loss in structured prediction, with an application to speech recognition.
  3. Mohammad Saberian and Nuno Vasconcelos Boosting Classifier Cascades. This is about an algorithm for simultaneously optimizing loss and computation in a classifier cascade construction. There were several other papers on cascades which are worth looking at if interested.
  4. Alan Fern and Prasad Tadepalli, A Computational Decision Theory for Interactive Assistants. This paper carves out some forms of natural not-MDP problems and shows their RL-style solution is tractable. It’s good to see people moving beyond MDPs, which at this point are both well understood and limited.
  5. Oliver Williams and Frank McSherry Probabilistic Inference and Differential Privacy. This paper is about a natural and relatively unexplored, and potentially dominating approach for achieving differential privacy and learning.

I also attended two workshops—Coarse-To-Fine and LCCC which were a fine combination. The first was about more efficient (and sometimes more effective) methods for learning which start with coarse information and refine, while the second was about parallelization and distribution of learning algorithms. Together, they were about how to learn fast and effective solutions.

The CtF workshop could have been named “Integrating breadth first search and learning”. I was somewhat (I hope not too) pesky, discussing Searn repeatedly during questions, since it seems quite plausible that a good application of Searn would compete with and plausibly improve on results from several of the talks. Eventually, I hope the conventional wisdom shifts to a belief that search and learning must be integrated for efficiency and robustness reasons. The talks in this workshop were uniformly strong in making that case. I was particularly interested in Drew‘s talk on a plausible improvement on Searn.

The level of agreement in approaches at the LCCC workshop was much lower, with people discussing many radically different approaches.

  1. Should data be organized by feature partition or example partition? Fernando points out that features often scale sublinearly in the number of examples, implying that an example partition addresses scale better. However, basic learning theory tells us that if the number of parameters scales sublinearly in the number of examples, then the value of additional samples asymptotes, implying a mismatched solution design. My experience is that a ‘not enough features’ problem can be dealt with by throwing all the missing features you couldn’t properly previously use, for example personalization.
  2. How can we best leverage existing robust distributed filesystem/MapReduce frameworks? There was near unanimity on the belief that MapReduce itself is of limited value for machine learning, but the step forward is unclear. I liked what Markus said: that no one wants to abandon the ideas of robustly storing data and moving small amounts of code to large amounts of data. The best way to leverage this capability to build great algorithms remains unclear to me.
  3. Every speaker was in agreement that their approach was faster, but there was great disagreement about what “fast” meant in an absolute sense. This forced me to think about an absolute measure of (input complexity)/(time) where we see results between 100 features/s and 10*106 features/s being considered “fast” depending on who is speaking. This scale disparity is remarkably extreme. A related detail is that the strength of baseline algorithms varies greatly.

I hope we’ll discover convincing answers to these questions in the near future.

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New York Area Machine Learning Events

On Sept 21, there is another machine learning meetup where I’ll be speaking. Although the topic is contextual bandits, I think of it as “the future of machine learning”. In particular, it’s all about how to learn in an interactive environment, such as for ad display, trading, news recommendation, etc…

On Sept 24, abstracts for the New York Machine Learning Symposium are due. This is the largest Machine Learning event in the area, so it’s a great way to have a conversation with other people.

On Oct 22, the NY ML Symposium actually happens. This year, we are expanding the spotlights, and trying to have more time for posters. In addition, we have a strong set of invited speakers: David Blei, Sanjoy Dasgupta, Tommi Jaakkola, and Yann LeCun. After the meeting, a late hackNY related event is planned where students and startups can meet.

I’d also like to point out the related CS/Econ symposium as I have interests there as well.

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ICML & COLT 2010

The papers which interested me most at ICML and COLT 2010 were:

  1. Thomas Walsh, Kaushik Subramanian, Michael Littman and Carlos Diuk Generalizing Apprenticeship Learning across Hypothesis Classes. This paper formalizes and provides algorithms with guarantees for mixed-mode apprenticeship and traditional reinforcement learning algorithms, allowing RL algorithms that perform better than for either setting alone.
  2. István Szita and Csaba Szepesvári Model-based reinforcement learning with nearly tight exploration complexity bounds. This paper and anotherrepresent the frontier of best-known algorithm for Reinforcement Learning in a Markov Decision Process.
  3. James Martens Deep learning via Hessian-free optimization. About a new not-quite-online second order gradient algorithm for learning deep functional structures. Potentially this is very powerful because while people have often talked about end-to-end learning, it has rarely worked in practice.
  4. Chrisoph Sawade, Niels Landwehr, Steffen Bickel. and Tobias Scheffer Active Risk Estimation. When a test set is not known in advance, the model can be used to safely aid test set evaluation using importance weighting techniques. Relative to the paper, placing a lower bound on p(y|x) is probably important in practice.
  5. H. Brendan McMahan and Matthew Streeter Adaptive Bound Optimization for Online Convex Optimization and the almost-same paper John Duchi, Elad Hazan, and Yoram Singer, Adaptive Subgradient Methods for Online Learning and Stochastic Optimization. These papers provide tractable online algorithms with regret guarantees over a family of metrics rather than just euclidean metrics. They look pretty useful in practice.
  6. Nicolò Cesa-Bianchi, Claudio Gentile, Fabio Vitale, Giovanni Zappella, Active Learning on Trees and Graphs Various subsets of these authors have other papers about actively learning graph-obeying functions which in total provide a good basis for understanding what’s possible and how to learn.

The program chairs for ICML did a wide-ranging survey over participants. The results seem to suggest that participants generally agree with the current ICML process. I expect there is some amount of anchoring effect going on where participants have an apparent preference for the known status quo, although it’s difficult to judge the degree of that. Some survey results which aren’t of that sort are:

  1. 7.7% of reviewers say author feedback changed their mind. It would be interesting to know for which fraction of accepted papers reviewers had their mind changed, but that isn’t there.
  2. 85.4% of authors don’t know if the reviewers read their response, believe they read and ignored it, or believe they didn’t read it. Authors clearly don’t feel like they are communicating with reviewers.
  3. 58.6% support growing the conference with the largest fraction suggesting poster-only papers.
  4. Other conferences attended by the ICML community in order are NIPS, ECML/PKDD, AAAI, IJCAI, AIStats, UAI, KDD, ICDM, COLT, SIGIR, ECAI, EMNLP, CoNLL. This is pretty different from the standard colocation list for ICML. Many possibilities are precluded by scheduling, but AAAI, IJCAI, UAI, KDD, COLT, SIGIR are all serious possibilities some of which haven’t been used much in the past.

My experience with Mark‘s new paper discussion site is generally positive—having comments emailed to interested parties really helps the discussion. There are a few comments that authors haven’t responded to, so if you are an author you might want to sign up to receive comments.

In addition, I was the workshop chair for ICML&COLT this year. My overall impression was that things went reasonably well, with the exception of internet connectivity at Dan Panorama which was a minidisaster courtesy of a broken per-machine authentication system. One of the things I’m particularly happy about was the Learning to Rank Challenge workshop. I think it would be great if ICML can continue to attract new challenge workshops in the future. If anyone else has comments about the workshops, I’d love to hear them.