[AI Seminar] AI Seminar sponsored by Apple -- Yichong Xu -- April 17

[Adams Wei Yu]

Dear faculty and students,

We look forward to seeing you next Tuesday, April 17, at noon in *NSH 1507* for
AI Seminar sponsored by Apple. To learn more about the seminar series,
please visit the AI Seminar webpage <http://www.cs.cmu.edu/~aiseminar/>.

On Tuesday,  Yichong Xu <http://xycking.wixsite.com/yichongxu> will give
the following talk:

Title:  Interactive learning using Comparison Queries

Abstract:

In supervised learning, we leverage a labeled dataset to design methods for
function estimation. In many practical situations, we are able to obtain
alternative feedback, possibly at a low cost. A broad goal is to understand
the usefulness of, and to design algorithms to exploit, this alternative
feedback. We consider a interactive learning setting where we obtain
additional ordinal (or comparison) information for potentially unlabeled
samples. In this talk we show the usefulness of such ordinal feedback for
two tasks: Binary classification and nonparametric regression. For binary
classification, we show that comparison queries can help in improving the
label and total query complexity by reducing the learning problem to that
of learning a threshold function. We present an algorithm that achieves
near-optimal label and total query complexity. For nonparametric
regression, we show that it is possible to accurately estimate an
underlying function with a very small labeled set, effectively escaping the
curse of dimensionality. We develop an algorithm called
Ranking-Regression(R^2) and analyze its accuracy as a function of size of
the labeled and unlabeled datasets and various noise parameters. We also
derive lower bounds to show that R^2 is optimal in a variety of settings.
Experiments show that our algorithms outperforms label-only algorithms when
comparison information is available.

Based on joint works with Sivaraman Balakrishnan, Artur Dubrawski, Kyle
Miller, Hariank Muthakana, Aarti Singh and Hongyang Zhang.
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