Multi-distribution learning, for robustness, fairness, and collaboration
Abstract
Social and real-world considerations such as robustness, fairness, social welfare, and multi-agent tradeoffs have given rise to multi-distribution learning paradigms. In recent years, these paradigms have been studied by several disconnected communities and under different names, including collaborative learning, distributional robustness, and fair federated learning. In this talk, I will highlight the importance of multi-distribution learning paradigms in general, introduce technical tools for addressing them, and discuss how these problems relate to classical and modern consideration in data driven processes.
Bio
Nika Haghtalab is an Assistant Professor in the Department of Electrical Engineering and Computer Sciences at UC Berkeley. She works broadly on the theoretical aspects of machine learning and algorithmic economics. Prof. Haghtalab's work builds theoretical foundations for ensuring both the performance of learning algorithms in presence of everyday economic forces and the integrity of social and economic forces that are born out of the use of machine learning systems. Previously, Prof. Haghtalab was an Assistant Professor in the CS department of Cornell University, in 2019-2020. She received her Ph.D. from the Computer Science Department of Carnegie Mellon University. She is a co-founder of Learning Theory Alliance (LeT-All). Among her honors are the CMU School of Computer Science Dissertation Award, SIGecom Dissertation Honorable Mention, and other industry research awards.
