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The Ideal Large Scale Learning Class

At NIPS, Andrew Ng asked me what should be in a large scale learning class. After some discussion with him and Nando and mulling it over a bit, these are the topics that I think should be covered.

There are many different kinds of scaling.

  1. Scaling in examples This is the most basic kind of scaling.
    1. Online Gradient Descent This is an old algorithm—I’m not sure if anyone can be credited with it in particular. Perhaps the Perceptron is a good precursor, but substantial improvements come from the notion of a loss function of which squared loss, logistic loss, Hinge Loss, and Quantile Loss are all worth covering. It’s important to cover the semantics of these loss functions as well. Vowpal Wabbit is a reasonably fast codebase implementing these.
    2. Second Order Gradient Descent methods For some problems, methods taking into account second derivative information can be more effective. I’ve seen preconditioned conjugate gradient work well, for which Jonathan Shewchuck‘s writeup is reasonable. Nando likes L-BFGS which I don’t have much experience with.
    3. Map-Reduce I have a love-hate relationship with the Map-Reduce framework. In my experience, it’s an excellent filesystem, but it’s quite frustrating to do machine learning with, since it encourages the parallelization of slow learning algorithms. I liked what Markus said at the LCCC workshop: nobody wants to give up on the idea of distributed fault tolerant storage and moving small amounts of code to large amounts of data rather than vice-versa. The best way to use this for Machine Learning isn’t yet clear to me. Hadoop is probably the most commonly used open source implementation of Map-Reduce.
  2. Feature Scaling—what do you do when you have very many features?
    1. Hashing approaches are surprisingly effective in my experience. It’s a good idea to also present Bloom Filters, as they help with the intuition of where this works substantially.
    2. Online l1 regularization is via truncated gradient. See Bob Carpenter’s discussion. John Duchi’s composite mirror descent generalization is also a useful general treatment.
    3. Boosting based approaches can also be effective, although training time can become problematic. This is partially mitigated by parallelization algorithms as discussed at the LCCC workshop See Jerry Ye’s talk and Krysta’s talk.. A really good public implementation of this is so far missing, as far as I know.
  3. Test-time Evaluation Ultrafast and efficient test-time evaluation seems to be a goal independent of training.
    1. Indicies One way to speed things up is with inverted indicies. Aside from the basic datastructure, I’d cover WAND and predictive indexing.
    2. GPU The use of GPU’s to make evaluation both more efficient and fast seems to make sense in many applications.
  4. Labels
    1. Sourcing Just acquiring sufficient label information can be problematic.
      1. Mechanical Turk can be an efficient approach. The basic approach can be improved with some work.
      2. When you are paying directly for labels, active learning approaches can substantially cut your costs. Burr Settles active learning survey is pretty comprehensive, although if I was to cover one algorithm, it would be this one which enjoys a compelling combination of strong theoretical guarantees, computational tractability, empirical performance, and generality.
      3. The other common approach is user-feedback information where bias and exploration effects becomes a critical concern. The tutorial Alina and I did on learning and exploration is critical here.
    2. Many Labels It’s common to need to make a complex prediction.
      1. Label Tree based approaches are a good starting point. I’d discuss the inconsistency of the naive approach and the Filter Tree, discussed here. Online tree building and conditional probability trees are also potentially extremely useful. Building smarter trees can help, such as with a confusion matrix or in an iterative fashion.
      2. Label Tree approaches breakdown when the number of labels becomes so large that filtering eliminates too many examples. Here Structured Prediction techniques become particularly important. I’d cover Searn as well as some of Drew Bagnell‘s work such as this one. Many other people are still interested in CRFs or Max-Margin Markov Networks which I find somewhat less compelling for computational reasons.
      3. Cascade Learning is also a compelling approach. The canonical paper on this is the Viola-Jones Face Detector. I’m sure there’s much other vision-related work on cascades that I’m unfamiliar. A more recent instance is the structured prediction cascade.

What else is essential and missing?

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User preferences for search engines

I want to comment on the “Bing copies Google” discussion here, here, and here, because there are data-related issues which the general public may not understand, and some of the framing seems substantially misleading to me.

As a not-distant-outsider, let me mention the sources of bias I may have. I work at Yahoo!, which has started using Bing. This might predispose me towards Bing, but on the other hand I’m still at Yahoo!, and have been using Linux exclusively as an OS for many years, including even a couple minor kernel patches. And, on the gripping hand, I’ve spent quite a bit of time thinking about the basic principles of incorporating user feedback in machine learning. Also note, this post is not related to official Yahoo! policy, it’s just my personal view.

The issue Google engineers inserted synthetic responses to synthetic queries on google.com, then executed the synthetic searches on google.com using Internet Explorer with the Bing toolbar and later noticed some synthetic responses from Bing with the synthetic queries.

There are two kinds of disagreement which people might have with this.

One is the privacy disagreement “Big Brother Microsoft is looking at what I search and using it”. I’m sympathetic on this count, but also sympathetic to the counter argument, that the data collected has value and can enhance the results for all users. In the end, I think companies should simply do their best to accept a user’s wishes, so those who want privacy can have it, and those who want to contribute their data towards improving a search engine can do so. The precise manner for achieving this by opt-in, opt-out, differential privacy, anonymization or other techniques is not entirely clear to me.

Let’s assume the privacy issue is dealt with. This is at least partly and possibly grossly untrue, but I want to focus on the other issue, and this assumption simplifies it’s discussion because a user and their internet browser are synonymous when the privacy issue is dealt with, as the agent’s actions are a true reflection of the user’s preferences.

The other issue is an originality disagreement, which much of the discussion focuses on. What I believe happened was a user feedback process, where users queried Google, clicked on a result, informed Microsoft/Bing of the query and clicked result, and their preference was used to promote the search result within Bing. Now, there is a slippery-slope of questions. Should a user be allowed to:

  1. Reveal to their chosen search engine their preferred result?
  2. Reveal to a competitor’s search engine their preferred result?

If you answer ‘no’ to the first, you are deeply against user freedom in a manner I can’t sympathize with. If you answer ‘yes’ to the first, and ‘no’ to the second, then you are still somewhat against user freedom. This isn’t too crazy a stance, as various people sell information and require of their users that it not be retransmitted. One of the more famous examples of this is the Bloomberg Terminal. However, in all instances I’m aware of, users knowingly agree to a contract providing access to the information with limitations. Google never entered into such a contract with it’s users, and I don’t know a sound basis for even an implicit contract. So, my answer are “yes, and yes” here.

But this doesn’t entirely deal with the issue of originality. You could argue that it’s ok for Microsoft to take advantage of revealed user interaction, but it’s still a matter of following rather than leading. This argument is simplistic and wrong, as I expect all informed parties already understand. A basic truth seen in many ways, is that the proper incorporation of new sources of information always improves results. This is true in machine learning where sample complexity results and cotraining formalize mechanisms and values of incorporating additional information, and it was heavily used by all competitive teams in the Netflix Competition. More generally, it’s true in basic knowledge engineering, where people fuse sources of information to create a better system, and I’m virtually certain it’s true of the ranking algorithms behind Google and Bing, which are surely complex beasts taking into account many sources of information. I know no details about the algorithm which Microsoft is using, but it’s quite plausible that they incorporated this information well enough to improve the quality of their results, perhaps in some instances so they are better than Google’s or the earlier version of Bing’s. If that’s the case, Google will either follow Microsoft’s lead taking into account user feedback as Microsoft does, or risk becoming obsolete.

We can also think about things in terms of the future. A basic truth, is that building a successful search engine is extraordinarily difficult. This is revealed by search market share, but also by simply thinking about the logistics involved. You need to crawl the web, have server farms all over the world (because the speed of light just isn’t fast enough), and incorporate many sources of information in just the right way in order to succeed, all while adversaries try to corrupt your results. If we prefer a future where there is a healthy competition amongst search engines, then it’s important to lower these barriers to entry so new people with new ideas can more easily test them out. One way to lower the barrier to entry is to accept that users can share their interaction, even with a competitor’s search engine.

Perhaps it’s inevitable that Amit Singhal has a viewpoint driving towards a monopoly on internet search. However, Google has generally been relatively good about supporting a rich ecosystem of innovation for information technology development, so I am still somewhat surprised. I would be more sympathetic to a position for allowing users of Internet Explorer a built-in means to choose to share their search behavior with Google or other search engines on an equal footing.

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Herman Goldstine 2011

Vikas points out the Herman Goldstine Fellowship at IBM. I was a Herman Goldstine Fellow, and benefited from the experience a great deal—that’s where work on learning reductions started. If you can do research independently, it’s recommended. Applications are due January 6.

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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.