Monday, September 23, 2019 - 2:00pm to Tuesday, September 24, 2019 - 2:55pm
Event Calendar Category
LIDS Seminar Series
Carnegie Mellon University
Building and Room number
Classical supervised machine learning algorithms focus on the setting where the algorithm has access to a fixed labeled dataset obtained prior to any analysis. In most applications, however, we have control over the data collection process such as which image labels to obtain, which drug-gene interactions to record, which network routes to probe, which movies to rate, etc. Furthermore, most applications face budget limitations on the amount of labels that can be collected. Experimental design and active learning are two paradigms that involve careful selection of data points to label from a large unlabeled pool. This talk will discuss and contrast the power of experimental design and active learning, starting with some recent advances in these paradigms and then posing open questions involving their integration and application to deep models.
Aarti Singh is an Associate Professor in the Machine Learning Department at Carnegie Mellon University. Her research lies at the intersection of machine learning, statistics and signal processing, and focuses on designing statistically and computationally efficient algorithms for learning from direct, compressive and interactive queries. Her work is recognized by an NSF Career Award, the United States Air Force Young Investigator Award, A. Nico Habermann Junior Faculty Chair Award, Harold A. Peterson Best Dissertation Award, and three best student paper awards. Her service honors include serving as Program Chair for the International Conference on Machine Learning (ICML) 2020, Program Chair for Artificial Intelligence and Statistics (AISTATS) 2017 conference, member of the National Academy of Sciences (NAS) Committee on Applied and Theoretical Statistics, guest editor for Electronic Journal of Statistics, and Associate Editor of the IEEE Transactions on Information Theory and IEEE Transactions on Signal and Information Processing over Networks.