An entropy-regularized optimal transport method learns welfare-optimal two-sided matching policies with estimated costs, supported by a non-asymptotic regret bound and calibrated simulations suggesting about one percentage point job-finding gains.
(2009): Inferring optimal peer assignment from experimental data, Journal of the American Statistical Association, 104, 486--500
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Who With Whom? Learning Optimal Matching Policies
An entropy-regularized optimal transport method learns welfare-optimal two-sided matching policies with estimated costs, supported by a non-asymptotic regret bound and calibrated simulations suggesting about one percentage point job-finding gains.