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Incentivized Collaboration in Active Learning

Authors Lee Cohen, Han Shao

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Author Details

Lee Cohen
  • Stanford University, CA, USA
Han Shao
  • Toyota Technological Institute of Chicago, IL, USA

Funding

This work was supported in part by the National Science Foundation under grants 2212968 and 2216899, by the Simons Foundation under the Simons Collaboration on the Theory of Algorithmic Fairness, by the Defense Advanced Research Projects Agency under cooperative agreement HR00112020003. The views expressed in this work do not necessarily reflect the position or the policy of the Government and no official endorsement should be inferred. Approved for public release; distribution is unlimited.

Acknowledgements

We would like to thank Avrim Blum for several useful discussions.

Cite AsGet BibTex

Lee Cohen and Han Shao. Incentivized Collaboration in Active Learning. In 5th Symposium on Foundations of Responsible Computing (FORC 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 295, pp. 2:1-2:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)
https://doi.org/10.4230/LIPIcs.FORC.2024.2

Abstract

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.

Subject Classification

ACM Subject Classification
  • Social and professional topics → Computing / technology policy
Keywords
  • pool-based active learning
  • individual rationality
  • incentives
  • Bayesian
  • collaboration

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