[AI Seminar] AI Seminar -- Aditya Grover, Mitigating Bias in Generative Modeling

[Han Zhao]

Dear faculty and students:

We look forward to seeing you next Tuesday, Oct. 15th, at noon in *NSH
3305 *for our first AI Seminar this semester, sponsored by Apple. To learn
more about the seminar series, please visit the website
<http://www.cs.cmu.edu/~aiseminar/>.
On Tuesday, Aditya Grover will give the following talk:
*Title: *Mitigating Bias in Generative Modeling

*Abstract:* In the last few years, there has been remarkable progress in
deep generative modeling. However, the learned models are noticeably
inaccurate w.r.t. to the underlying data distribution, as evident from
downstream metrics that compare statistics of interest across the true and
generated data samples. This bias in downstream evaluation can be
attributed to imperfections in learning (“model bias”) or be propagated due
to the bias in the training dataset itself (“dataset bias”). In this talk,
I will present an importance weighting approach for mitigating both these
kinds of biases of generative models.  Our approach assumes only
‘black-box’ sample access to a generative model and is broadly applicable
to both likelihood-based and likelihood-free generative models.
Empirically, we find that our technique consistently improves standard
goodness-of-fit metrics for evaluating the sample quality of
state-of-the-art deep generative models, suggesting reduced bias. We
demonstrate its utility on representative applications in a) data
augmentation, b) model-based policy evaluation using off-policy data, and
c) permutation-invariant generative modeling of graphs. Finally, I will
present some recent work extending these ideas to fair data generation in
the presence of biased training datasets.

*Bio:* Aditya Grover is a 5th-year Ph.D. candidate in Computer Science at
Stanford University advised by Stefano Ermon. His research focuses on
probabilistic machine learning, including topics in generative modeling,
approximate inference, and deep learning as well as applications in
sustainability. His research has been cited widely in academia, deployed
into production at major technology companies, and recognized with a best
paper award (StarAI), a Lieberman Fellowship, a Data Science Institute
Scholarship, and a Microsoft Research Ph.D. Fellowship. He is also a
Teaching Fellow at Stanford since 2018, where he co-designed and teaches a
new class on Deep Generative Models. Previously, Aditya obtained his
bachelors in Computer Science and Engineering from IIT Delhi in 2015, where
he received a best undergraduate thesis award.
-- 

*Han ZhaoMachine Learning Department*


*School of Computer ScienceCarnegie Mellon UniversityMobile: +1-*
*412-652-4404*
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