GNN-DCMs apply graph neural networks to discrete choice modeling, recovering nested logit and spatially correlated logit via message passing on utilities and demonstrating better predictive performance for residential location choices in Chicago.
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Mini-batch SGD optimizes a different objective than full partial likelihood in Cox models, but the resulting mb-MPLE is still consistent with optimal rates for neural nets and asymptotic normality for linear models.
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Graph neural networks for residential location choice: connection to classical logit models
GNN-DCMs apply graph neural networks to discrete choice modeling, recovering nested logit and spatially correlated logit via message passing on utilities and demonstrating better predictive performance for residential location choices in Chicago.
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Mini-batch Estimation for Deep Cox Models: Statistical Foundations and Practical Guidance
Mini-batch SGD optimizes a different objective than full partial likelihood in Cox models, but the resulting mb-MPLE is still consistent with optimal rates for neural nets and asymptotic normality for linear models.