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Aarti SinghAssociate Professor, Machine Learning Department within the School of Computer Science-Carnegie Mellon University
Virtual Seminar via Zoom
AbstractA major factor in the wide success of machine learning comes from the ability to map complex input-output associations based on some given data. However, real-world deployments require machine learning systems that continually interact with their environment making decisions about what data to collect to continually improve their performance. In this talk, I will talk about some recent research from my group that focuses on design of such interactive machine learning algorithms for fully autonomous operation as well as for settings where human judgement is available and can be queried in an interactive fashion. Specifically, I will talk about bandit optimization algorithms that are used in recommender systems and how we can generalize them to leverage comparative feedback from humans and to adaptively leverage smoothness properties of underlying functions.
BiographyAarti Singh is an Associate Professor in the Machine Learning Department within the School of Computer Science at Carnegie Mellon University. She received her M.S. and 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, 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, advisory board for NSF AI institute AI-EDGE, and Associate Editor for IEEE Transactions on Information Theory and Journal of Machine Learning Research.