*********************************
There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
*********************************
TITLE: Machine learning by the people, for the people.
ABSTRACT:
Typical analysis of learning algorithms considers their outcome in isolation from the effects that they may have on the process that generates the data or the entity that is interested in learning. However, current technological trends mean that people and organizations increasingly interact with learning systems, making it necessary to consider these effects, which fundamentally change the nature of learning and the challenges involved. In this talk, I will explore three lines of research from my work on the theoretical aspects of machine learning and algorithmic economics that account for these interactions: learning optimal policies in game-theoretic settings, without an accurate behavioral model, by interacting with people; managing people's expertise and resources in data-collection and machine learning; and collaborative learning in a setting where multiple learners interact with each other to discover similar underlying concepts.
BIO: Nika Haghtalab is a Ph.D. candidate at the Computer Science Department of Carnegie Mellon University, co-advised by Avrim Blum and Ariel Procaccia. Her research interests include learning theory and algorithmic economics. She is a recipient of the IBM and Microsoft Research Ph.D. fellowships and the Siebel Scholarship.
