[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]

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/
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