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.
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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.