[AI Seminar] AI Lunch -- Shiva Kaul -- April 12, 2016

[Ellen Vitercik]

Dear faculty and students,

We look forward to seeing you this Tuesday, April 12th, at noon in NSH 3305
for AI lunch. To learn more about the seminar and lunch, or to volunteer to
give a talk, please visit the AI Lunch webpage
<http://www.cs.cmu.edu/~aiseminar/>. *We are looking for someone to give a
talk on May 10th*.

On Tuesday, Shiva Kaul will give a talk titled "Agnostic linear
classification."

*Abstract:* We introduce a simple new algorithm for improper, agnostic
learning of halfspaces, a problem closely related to learning sparse
parities with noise. It uses exponentially less data than previous
algorithms, particularly ones based on fitting polynomials. It is provably
correct in a wide range of settings, but not necessarily fast. The
algorithm is very practical and achieves good experimental performance for
both natural and artificial problems. The lynchpin of this algorithm is a
new hypothesis class called smooth lists of halfspaces. They are more
flexible than halfspaces, but do not require more data to train in the
worst case. These new classifiers enable an iterative approach which is
fundamentally different than update rules such as perceptron and
multiplicative weights. Our analysis involves a dual interpretation of the
algorithm as a dynamical system. Joint work with Geoff Gordon.

Best,
Ellen and Ariel
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