Kavli Affiliate: Yi Zhou
| First 5 Authors: Cheng Chen, Jiaying Zhou, Jie Ding, Yi Zhou,
| Summary:
In the era of big data, many big organizations are integrating machine
learning into their work pipelines to facilitate data analysis. However, the
performance of their trained models is often restricted by limited and
imbalanced data available to them. In this work, we develop an assisted
learning framework for assisting organizations to improve their learning
performance. The organizations have sufficient computation resources but are
subject to stringent data-sharing and collaboration policies. Their limited
imbalanced data often cause biased inference and sub-optimal decision-making.
In assisted learning, an organizational learner purchases assistance service
from an external service provider and aims to enhance its model performance
within only a few assistance rounds. We develop effective stochastic training
algorithms for both 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, but still obtain a model that
achieves near-oracle performance as if all the data were centralized.
| Search Query: ArXiv Query: search_query=au:”Yi Zhou”&id_list=&start=0&max_results=3