Machine Learning (Theory)


Machined Learnings

Paul Mineiro has started Machined Learnings where he’s seriously attempting to do ML research in public. I personally need to read through in greater detail, as much of it is learning reduction related, trying to deal with the sorts of complex source problems that come up in practice.


Regretting the dead

Nikos pointed out this new york times article about poor clinical design killing people. For those of us who study learning from exploration information this is a reminder that low regret algorithms are particularly important, as regret in clinical trials is measured by patient deaths.

Two obvious improvements on the experimental design are:

  1. With reasonable record keeping of existing outcomes for the standard treatments, there is no need to explicitly assign people to a control group with the standard treatment, as that approach is effectively explored with great certainty. Asserting otherwise would imply that the nature of effective treatments for cancer has changed between now and a year ago, which denies the value of any clinical trial.
  2. An optimal experimental design will smoothly phase between exploration and exploitation as evidence for a new treatment shows that it can be effective. This is old tech, for example in the EXP3.P algorithm (page 12 aka 59) although I prefer the generalized and somewhat clearer analysis of EXP4.P.

Done the right way, the clinical trial for a successful treatment would start with some initial small pool (equivalent to “phase 1” in the article) and then simply expanded the pool of participants over time as it proved superior to the existing treatment, until the pool is everyone. And as a bonus, you can even compete with policies on treatments rather than raw treatments (i.e. personalized medicine).

Getting from here to there seems difficult. It’s been 15 years since EXP3.P was first published, and the progress in clinical trial design seems glacial to us outsiders. Partly, I think this is a communication and education failure, but partly, it’s also a failure of imagination within our own field. When we design algorithms, we often don’t think about all the applications, where a little massaging of the design in obvious-to-us ways so as to suit these applications would go a long ways. Getting this right here has a substantial moral aspect, potentially saving millions of lives over time through more precise and fast deployments of new treatments.


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.



Tags: Announcements,Conferences jl@ 5:35 pm

Geoff Gordon points out AIStats 2011 in Ft. Lauderdale, Florida. The call for papers is now out, due Nov. 1. The plan is to experiment with the review process to encourage quality in several ways. I expect to submit a paper and would encourage others with good research to do likewise.

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