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

[Adams Wei Yu]

A gentle reminder that the talk will be tomorrow (Tuesday) noon in *NSH
1507.*

On Sat, Apr 14, 2018 at 3:17 AM, Adams Wei Yu <weiyu at cs.cmu.edu> wrote:

> 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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