Incentivized Collaboration in Active Learning
Lee Cohen Han Shao
In collaborative active learning, where multiple agents try to learn labels
from a common hypothesis, we introduce an innovative framework for incentivized
collaboration. Here, rational agents aim to obtain labels for their data sets
while keeping label complexity at a minimum. We focus on designing (strict)
individually rational (IR) collaboration protocols, ensuring that agents cannot
reduce their expected label complexity by acting individually. We first show
that given any optimal active learning algorithm, the collaboration protocol
that runs the algorithm as is over the entire data is already IR. However,
computing the optimal algorithm is NP-hard. We therefore provide collaboration
protocols that achieve (strict) IR and are comparable with the best known
tractable approximation algorithm in terms of label complexity.