FT-MDN-Transformer improves transfer learning for loan recovery rate prediction under covariate, conditional, and label shifts with heterogeneous features, outperforming baselines when target data is limited.
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VaRDASS improves unsupervised domain adaptation by using stratified sampling to reduce variance in discrepancy estimation for measures like correlation alignment and MMD, with derived error bounds, an optimality proof for MMD under assumptions, and a k-means style algorithm.
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Transfer Learning for Loan Recovery Prediction under Distribution Shifts with Heterogeneous Feature Spaces
FT-MDN-Transformer improves transfer learning for loan recovery rate prediction under covariate, conditional, and label shifts with heterogeneous features, outperforming baselines when target data is limited.
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Variance Matters: Improving Domain Adaptation via Stratified Sampling
VaRDASS improves unsupervised domain adaptation by using stratified sampling to reduce variance in discrepancy estimation for measures like correlation alignment and MMD, with derived error bounds, an optimality proof for MMD under assumptions, and a k-means style algorithm.