Kavli Affiliate: Yi Zhou
| First 5 Authors: Cheng Chen, Jiaying Zhou, Jie Ding, Yi Zhou,
| Summary:
We develop an assisted learning framework for assisting organization-level
learners to improve their learning performance with limited and imbalanced
data. In particular, learners at the organization 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.
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