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Title: Collaborative PAC Learning
We consider a collaborative PAC learning model, in which k players attempt to learn the same underlying concept. We ask how much more information is required to learn an accurate classifier for all players simultaneously. We refer to the ratio between the sample complexity of collaborative PAC learning and its non-collaborative (single-player) counterpart as the overhead. We design learning algorithms with O(ln(k)) and O(ln2 (k)) overhead in the personalized and centralized variants our model. This gives an exponential improvement upon the naïve algorithm that does not share information among players. We complement our upper bounds with an Ω(ln(k)) overhead lower bound, showing that our results are tight up to a logarithmic factor.  more » « less
Award ID(s):
1525971 1331175 1733556 1714140
Author(s) / Creator(s):
; ; ;
Date Published:
Journal Name:
NIPS 2017
Medium: X
Sponsoring Org:
National Science Foundation
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