Category: Machine Learning

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.