Machine Learning Seminar Series with Yi Zhao (ECE, University of Utah)

Assisted Learning: A Learning Framework for Organizations with Limited and Imbalanced Data

We develop an assisted learning framework for assisting organization-level learners in improving their learning performance with limited and imbalanced data. In particular, learners at the organizational level usually have sufficient computation resource, but are subject to stringent collaboration policy and information privacy. Their limited imbalanced data often cause biased inference and sub-optimal decision-making. In our assisted learning framework, an organizational learner purchases assistance service from a service provider and aims to enhance its model performance within a few assistance rounds. We develop effective stochastic training algorithms for assisted deep learning and assisted reinforcement learning. Different from existing distributed algorithms that need to frequently transmit gradients or models, our framework allows the learner to only occasionally share information with the service provider, and still achieve a near-oracle model as if all the data were centralized.

About Yi Zhou
Yi Zhou is an assistant professor affiliated with the Department of ECE at the University of Utah. Before, he worked as a post-doctorate research associate in the Department of ECE at Duke University. He obtained a Ph.D. degree in ECE from The Ohio State University in 2018. His research interests include deep learning, reinforcement learning, statistical machine learning, nonconvex and distributed optimization, and statistical signal processing.