Machine Learning (Theory)

9/28/2011

Somebody’s Eating Your Lunch

Tags: CS,Online,Teaching jl@ 1:36 pm

Since we last discussed the other online learning, Stanford has very visibly started pushing mass teaching in AI, Machine Learning, and Databases. In retrospect, it’s not too surprising that the next step up in serious online teaching experiments are occurring at the computer science department of a university embedded in the land of startups. Numbers on the order of 100000 are quite significant—similar in scale to the number of computer science undergraduate students/year in the US. Although these populations surely differ, the fact that they could overlap is worth considering for the future.

It’s too soon to say how successful these classes will be and there are many easy criticisms to make:

  1. Registration != Learning … but if only 1/10th complete these classes, the scale of teaching still surpasses the scale of any traditional process.
  2. 1st year excitement != nth year routine … but if only 1/10th take future classes, the scale of teaching still surpasses the scale of any traditional process.
  3. Hello, cheating … but teaching is much harder than testing in general, and we already have recognized systems for mass testing.
  4. Online misses out … sure, but for students not enrolled in a high quality university program, this is simply not a relevant comparison. There are also benefits to being online as well, as your time might be better focused. Anecdotally, at Caltech, they let us take two classes at the same time, which I did a few times. Typically, I had a better grade in the class that I skipped as the instructor had to go through things rather slowly.
  5. Where’s the beef? The hard nosed will want to know how to make money, which is always a concern. But, a decent expectation is that if you first figure out how to create value, you’ll find some way to make money. And, if you first wait until it’s clear how to make money, you won’t make any.

My belief is that this project will pan out, with allowances for the expected inevitable adjustments that you learn to make from experience. I think the critics miss an understanding of what’s possible and what motivates people.

The prospect of teaching 1 student means you might review some notes. The prospect of teaching ~10 students means you prepare some slides. The prospect of teaching ~100 students means you polish your slides well, trying to anticipate questions, and hopefully drawing on experience from previous presentations. I’ve never directly taught ~1000 students, but at that scale you must try very hard to make the presentation perfect, including serious testing with dry runs. 105 students must make getting out of bed in the morning quite easy.

Stanford has a significant first-mover advantage amongst top research universities, but it’s easy to imagine a few other (but not many) universities operating at a similar scale. Those that have the foresight to start a serious online teaching program soon will have a chance of being among the few. For other research universities, we can expect boutique traditional classes to continue for some time. These boutique classes may have some significant social value, because it’s easy to imagine that the few megaclasses miss important things in developing research areas. And for everyone working at teaching universities, someone is eating your lunch.

(Cross posted at CACM.)

9/7/2011

KDD and MUCMD 2011

At KDD I enjoyed Stephen Boyd‘s invited talk about optimization quite a bit. However, the most interesting talk for me was David Haussler‘s. His talk started out with a formidable load of biological complexity. About half-way through you start wondering, “can this be used to help with cancer?” And at the end he connects it directly to use with a call to arms for the audience: cure cancer. The core thesis here is that cancer is a complex set of diseases which can be distentangled via genetic assays, allowing attacking the specific signature of individual cancers. However, the data quantity and complex dependencies within the data require systematic and relatively automatic prediction and analysis algorithms of the kind that we are best familiar with.

Some of the papers which interested me are:

  1. Kai-Wei Chang and Dan Roth, Selective Block Minimization for Faster Convergence of Limited Memory Large-Scale Linear Models, which is about effectively using a hard-example cache to speedup learning.
  2. Leland Wilkinson, Anushka Anand, and Dang Nhon Tuan, CHIRP: A New Classifier Based on Composite Hypercubes on Iterated Random Projections. The bar on creating new classifiers is pretty high. The approach here uses a combination of random projection and partition which appears to be compelling for some nonlinear and relatively high computation settings. They do a more thorough empirical evaluation than most papers.
  3. Zhuang Wang, Nemanja Djuric, Koby Crammer, and Slobodan Vucetic Trading Representability for Scalability: Adaptive Multi-Hyperplane Machine for Nonlinear Classification. The paper explores an interesting idea: having lots of weight vectors (effectively infinity) associated with a particular label, showing that algorithms on this representation can deal with lots of data as per linear predictors, but with superior-to-linear performance. The authors don’t use the hashing trick, but their representation is begging for it.
  4. Michael Bruckner and Tobias Scheffer, Stackelberg Games for Adversarial Prediction Problem. This is about email spam filtering, where the authors use a theory of adversarial equilibria to construct a more robust filter, at least in some cases. Demonstrating this on noninteractive data is inherently difficult.

There were also three papers that were about creating (or perhaps composing) learning systems to do something cool.

  1. Gideon Dror, Yehuda Koren, Yoelle Maarek, and Idan Szpektor, I Want to Answer, Who Has a Question? Yahoo! Answers Recommender System. This is about how to learn to route a question to the appropriate answerer automatically.
  2. Yehuda Koren, Edo Liberty, Yoelle Maarek, and Roman Sandler, Automatically Tagging Email by Leveraging Other Users’ Folders. This is about helping people organize their email with machine learning.
  3. D. Sculley, Matthew Eric Otey, Michael Pohl, Bridget Spitznagel, John Hainsworth, Yunkai Zhou, Detecting Adversarial Advertisements in the Wild. The title is an excellent abstract here, and there are quite a few details about the implementation.

I also attended MUCMD, a workshop on the Meaningful Use of Complex Medical Data shortly afterwards. This workshop is about the emergent area of using data to improve medicine. The combination of electronic health records, the economic importance of getting medicine right, and the relatively weak use of existing data implies there is much good work to do.

This finally gave us a chance to discuss radically superior medical trial designs based on work in exploration and learning :)

Jeff Hammerbacher‘s talk was a hilarilously blunt and well stated monologue about the need and how to gather data in a usable way.

Amongst the talks on using medical data, Suchi Saria‘s seemed the most mature. They’ve constructed a noninvasive test for problem infants which is radically superior to the existing Apgar score according to leave-one-out cross validation.

From the doctor’s side, there was discussion of the deep balkanization of data systems within hospitals, efforts to overcome that, and the (un)trustworthiness of data. Many issues clearly remain here, but it also looks like serious progress is being made.

Overall, the workshop went well, with the broad cross-section of talks providing quite a bit of extra context you don’t normally see. It left me believing that a community centered on MUCMD is rising now, with attendant workshops, conferences, etc… to be expected.

9/3/2011

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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