[AI Seminar] Online AI Seminar on April 07 (Zoom) -- Cynthia Rudin -- Do Simpler Models Exist and How Can We Find Them?. AI seminar is sponsored by Fortive.

[Aayush Bansal]

reminder.. this is tomorrow noon.

On Tue, Mar 31, 2020 at 1:28 PM Aayush Bansal <aayushb at cs.cmu.edu> wrote:

> Cynthia Rudin (Duke University) will be giving an online seminar on "Do
> Simpler Models Exist and How Can We Find Them?" from *12:00 - 01:00 PM* on
> April 07.
>
> Zoom Link: *https://cmu.zoom.us/j/262225154
> <https://cmu.zoom.us/j/262225154>*
>
> CMU AI Seminar is sponsored by Fortive.
>
> Following are the details of the talk:
>
> *Title: *Do Simpler Models Exist and How Can We Find Them?
>
> *Abstract: *While the trend in machine learning has tended towards more
> complex hypothesis spaces, it is not clear that this extra complexity is
> always necessary or helpful for many domains. In particular, models and
> their predictions are often made easier to understand by adding
> interpretability constraints. These constraints shrink the hypothesis
> space; that is, they make the model simpler. Statistical learning theory
> suggests that generalization may be improved as a result as well. However,
> adding extra constraints can make optimization (exponentially) harder. For
> instance, it is much easier in practice to create an accurate neural
> network than an accurate and sparse decision tree. We address the following
> question: Can we show that a simple-but-accurate machine learning model
> might exist for our problem, before actually finding it? If the answer is
> promising, it would then be worthwhile to solve the harder constrained
> optimization problem to find such a model. In this talk, I present an easy
> calculation to check for the possibility of a simpler model. This
> calculation indicates that simpler-but-accurate models do exist in practice
> more often than you might think. Time-permitting, I will then briefly
> overview our progress towards the challenging problem of finding optimal
> sparse decision trees.
>
> *Bio*: Cynthia Rudin is a professor of computer science, electrical and
> computer engineering, and statistical science at Duke University.
> Previously, Prof. Rudin held positions at MIT, Columbia, and NYU. Her
> degrees are from the University at Buffalo and Princeton University. She is
> a three-time winner of the INFORMS Innovative Applications in Analytics
> Award, was named as one of the "Top 40 Under 40" by Poets and Quants in
> 2015, and was named by Businessinsider.com as one of the 12 most impressive
> professors at MIT in 2015. She has served on committees for INFORMS, the
> National Academies, the American Statistical Association, DARPA, the NIJ,
> and AAAI. She is a fellow of both the American Statistical Association and
> Institute of Mathematical Statistics. She is a Thomas Langford Lecturer at
> Duke University for 2019-2020.
>
> *Cynthia is available for one-on-one (virtual) meetings on April 07.
> Please send an email to me if you would like to schedule a meeting with
> her.*
>
>
> To learn more about the seminar series, please visit the website.
>
> --
> Aayush Bansal
> http://www.cs.cmu.edu/~aayushb/
>
>

-- 
Aayush Bansal
http://www.cs.cmu.edu/~aayushb/
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