[AI Seminar] ai-seminar-announce Digest, Vol 78, Issue 3

[Adams Wei Yu]

A gentle reminder that the talk will happen tomorrow (Tuesday) noon at NSH
3305.

On Sun, Nov 12, 2017 at 9:00 AM, <ai-seminar-announce-request at cs.cmu.edu>
wrote:

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> Today's Topics:
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>    1.  AI Seminar sponsored by Apple -- Nika Haghtalab -- Nov   14
>       (Adams Wei Yu)
>
>
> ----------------------------------------------------------------------
>
> Message: 1
> Date: Sat, 11 Nov 2017 23:03:08 +0000
> From: Adams Wei Yu <weiyu at cs.cmu.edu>
> To: ai-seminar-announce at cs.cmu.edu
> Subject: [AI Seminar] AI Seminar sponsored by Apple -- Nika Haghtalab
>         -- Nov  14
> Message-ID:
>         <CABzq7er_Fy6MBoBHA4s31OnxwOD2CQKva8vmFU
> 0wpA8CQxDOYg at mail.gmail.com>
> Content-Type: text/plain; charset="utf-8"
>
> Dear faculty and students,
>
> We look forward to seeing you next Tuesday, Nov 14, at noon in NSH 3305 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, Nika Haghtalab <https://www.cs.cmu.edu/~nhaghtal/> will give
> the following talk:
>
> Title: Algorithms for Generalized Topic Modeling
>
> Abstract:
>
>  Topic modeling is an area with significant recent work in the intersection
> of algorithms and machine learning. In standard topic models, a topic (such
> as sports, business, or politics) is viewed as a probability distribution
> \vec a_i over words, and a document is generated by first selecting a
> mixture \vec w over topics, and then generating words iid from the
> associated mixture \vec w^T A. Given a large collection of such documents,
> the goal is to recover the topic vectors and then to correctly classify new
> documents according to their topic mixture.
>
> In this work we consider a broad generalization of this framework in which
> words are no longer assumed to be drawn iid and instead a topic is a
> complex distribution over sequences of paragraphs. Since one could not hope
> to even represent such a distribution in general (even if paragraphs are
> given using some natural feature representation), we aim instead to
> directly learn a document classifier. That is, we aim to learn a predictor
> that given a new document, accurately predicts its topic mixture, without
> learning the distributions explicitly. We present several natural
> conditions under which one can do this efficiently and discuss issues such
> as noise tolerance and sample complexity in this model. More generally, our
> model can be viewed as a generalization of the multi-view or co-training
> setting in machine learning.
>
> This talk is based on joint work with Avrim Blum. To appear in AAAI 2018.
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> End of ai-seminar-announce Digest, Vol 78, Issue 3
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