[AI Seminar] AI Seminar sponsored by Fortive on Feb 18 (NSH 3305) -- Ben Lengerich -- Interaction Effects: Helpful or Hurtful?

[Aayush Bansal]

Ben Lengerich will be giving a seminar on "Interaction Effects: Helpful or
Hurtful?" from *12:00 - 01:00 PM* on Feb 18 in Newell Simon Hall (NSH) 3305.

CMU AI Seminar is sponsored by Fortive. Lunch will be served.

Following are the details of the talk:

*Title: *Interaction Effects: Helpful or Hurtful?

*Abstract: *The large representational capacity of deep learning models is
often viewed as a positive attribute which allows us to learn interactions
of many input variables. However, large model classes can also present
challenges for estimation. In this talk, we take special interest in
learning interaction effects. First, we define interaction effects through
the statistical framework of the functional ANOVA. By giving care to this
definition, we encounter several surprising findings about the nature of
interaction effects (e.g. all interaction effects look like XOR). Next, we
find that traditional machine learning models (such as tree-based models)
gain almost all of their predictive power from low-order interaction
effects. Turning to deep models, we find that fully-connected networks tend
to estimate a large amount of spurious interaction effects. Finally, we
present a view of Dropout as a regularizer against interaction effects.

*Bio*: Ben Lengerich is a Ph.D. student in the CS Department at Carnegie
Mellon University, advised by Prof. Eric Xing.

To learn more about the seminar series, please visit the website
<http://www.cs.cmu.edu/~aiseminar/>.


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