[Colloquium] Today at 3 -- DSI Talk: Aarti Singh, Carnegie Mellon University

[Rob Mitchum]

*Data Science Institute Distinguished Speaker Series*

*Aarti Singh <http://www.cs.cmu.edu/~aarti/>*
*Associate Professor in Machine Learning*
*Carnegie Mellon University*

*Monday, October 25th*
*3:00 p.m. - 4:00 p.m.*
*Live Stream Only (Zoom
<https://uchicagogroup.zoom.us/j/97600845733?pwd=NzI4VmRWaHUyRTFlTXZJSjdyOGd6QT09>
or YouTube <https://youtu.be/wgHmekGrLnU>)*
*Register
<https://www.eventbrite.com/e/aarti-singh-learning-and-decision-making-with-preferential-supervision-tickets-190768211857>*


*Learning and Decision Making with Preferential Supervision*

Despite the widespread use and success of machine learning, a key
bottleneck in applying machine learning is the need for high quality
supervision that can be used to guide training of the algorithms. However,
obtaining meaningful labels and designing rewards for supervision becomes
challenging, particularly as machine learning is used to solve increasingly
complex problems. Poorly designed rewards and inaccurate labels can result
in unstable and unsafe performance. This necessitates use of alternative
forms of supervision for learning and decision making. In the setting of
human-in-the-loop, preferences in the form of pairwise comparisons or
rankings have emerged as an alternate supervision mechanism that are often
easier to elicit and more accurate than labels or rewards. This talk will
outline our efforts in understanding the fundamental limits of learning and
decision making when an algorithm is given access to preferences in
addition to labels.  We will discuss and contrast the value of preferential
supervision in several settings including classification, regression,
bandits, optimization and reinforcement learning, along with some open
problems.

*Bio:* Aarti Singh is an Associate Professor in the Machine Learning
Department within the School of Computer Science at Carnegie Mellon
University. She received her Ph.D. degree in Electrical and Computer
Engineering from the University of Wisconsin-Madison and was a Postdoctoral
Research Associate at the Program in Applied and Computational Mathematics
at Princeton University before joining CMU. Her research lies at the
intersection of machine learning, statistics and signal processing, and
focuses on developing, analyzing and applying interactive algorithms that
use the most informative data and actions to guide learning and
decision-making in both human-in-loop and human-out-of-loop settings, with
applications to enabling social and scientific discoveries. Her work is
recognized by an NSF Career Award, a United States Air Force Young
Investigator Award, A. Nico Habermann Faculty Chair Award, Harold A.
Peterson Best Dissertation Award, and four best student paper awards. Dr.
Singh has served as Program Chair for the International Conference on
Machine Learning (ICML) 2020, Program Chair for Artificial Intelligence and
Statistics (AISTATS) 2017 conference, the National Academy of Sciences
(NAS) committee on Applied and Theoretical Statistics, lead expert on
ONR/NIST and NAS studies, NASEM advisory board for NSF DMREF, and Associate
Editor for IEEE Transactions on Information Theory.


*Part of the Data Science Institute Distinguished Speaker Series:*

*Defining The Field of Data Science*
As data science evolves from buzzword to a mature and singular field, its
research questions dive deeper into the foundations of this new discipline.
The Fall 2021 Distinguished Speaker Series convenes world-class experts
actively exploring and expanding the fundamental methods and approaches
that transform large and complex datasets into knowledge and action,
fueling new applications in areas such as artificial intelligence,
healthcare, and the social sciences. Join the new UChicago Data Science
Institute for provocative talks and discussion that will illuminate the
bedrock and promise of the flourishing field of data science.
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