[AI Seminar] ai-seminar-announce Digest, Vol 70, Issue 5

[Adams Wei Yu]

A gentle reminder that the talk will be tomorrow noon.

On Sat, Jan 28, 2017 at 12:00 PM, <ai-seminar-announce-request at cs.cmu.edu>
wrote:

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>    1.  AI Lunch -- Po-Wei Wang -- January 31 (Adams Wei Yu)
>
>
> ----------------------------------------------------------------------
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> Message: 1
> Date: Fri, 27 Jan 2017 13:43:20 -0500
> From: Adams Wei Yu <weiyu at cs.cmu.edu>
> To: ai-seminar-announce at cs.cmu.edu
> Subject: [AI Seminar] AI Lunch -- Po-Wei Wang -- January 31
> Message-ID:
>         <CABzq7erCO_7iHYp0zr5EaNqt2sf7t+UkWPH6UEqWpOmaSO4RiQ at mail.
> gmail.com>
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> Dear faculty and students,
>
> We look forward to seeing you Next Tuesday, January 31, at noon in NSH 3305
> for AI lunch. To learn more about the seminar and lunch, please visit
> the AI Lunch webpage <http://www.cs.cmu.edu/~aiseminar/>.
>
> On Tuesday, Po-Wei Wang <http://www.powei.tw/> will give a talk titled
> ?Polynomial optimization methods for matrix factorization?.
>
> *Abstract:* Matrix factorization is a core technique in many machine
> learning problems, yet also presents a nonconvex and often
> difficult-to-optimize problem. In this paper we present an approach based
> upon polynomial optimization techniques that both improves the convergence
> time of matrix factorization algorithms and helps them escape from local
> optima. Our method is based on the realization that given a joint search
> direction in a matrix factorization task, we can solve the ``subspace
> search'' problem (the task of jointly finding the steps to take in each
> direction) by solving a bivariate quartic polynomial optimization problem.
> We derive two methods for solving this problem based upon sum of squares
> moment relaxations and the Durand-Kerner method, then apply these
> techniques on matrix factorization to derive a direct coordinate descent
> approach and a method for speeding up existing approaches. On three
> benchmark datasets we show the method substantially improves convergence
> speed over state-of-the-art approaches, while also attaining lower
> objective value.
>
> This is a joint work with Chun-Liang Li and J. Zico Kolter. Forthcoming in
> AAAI 2017.
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> End of ai-seminar-announce Digest, Vol 70, Issue 5
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