Research map / Learning theory and algorithms / Optimal transport
Tsallis Regularized Optimal Transport and Ecological Inference
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Tune q to 2.8 and use the survey-derived cost matrix instead of Sinkhorn's q=1 configuration (ablation) | TROT for ecological inference · Florida counties | KL-divergence: 0.035 \pm 0.027 -> 0.007 \pm 0.009 | Helped |
| Tune q to 2.8 and use the survey-derived cost matrix instead of Sinkhorn's q=1 configuration (ablation) | TROT for ecological inference · Florida counties | absolute error: 0.007 \pm 0.004 -> 0.003 \pm 0.002 | Helped |
| Use q=1.0 with the RBF cost matrix (ablation) | TROT for ecological inference · Florida counties | KL-divergence: 0.054 \pm 0.036 -> 0.054 \pm 0.036 | No clear effect |
| Use q=1.0 with the RBF cost matrix (ablation) | TROT for ecological inference · Florida counties | absolute error: 0.009 \pm 0.005 -> 0.009 \pm 0.005 | No clear effect |
| Use an unregularized Simplex transport solution instead of regularized transport | Optimal transport for ecological inference · Florida counties | KL-divergence: 0.007 \pm 0.009 -> 0.136 \pm 0.098 | Hurt |
| Use an unregularized Simplex transport solution instead of regularized transport | Optimal transport for ecological inference · Florida counties | absolute error: 0.003 \pm 0.002 -> 0.013 \pm 0.009 | Hurt |
| Use the no-prior cost matrix (ablation) | TROT for ecological inference · Florida counties | KL-divergence: 0.007 \pm 0.009 -> 0.076 \pm 0.048 | Hurt |
| Use the no-prior cost matrix (ablation) | TROT for ecological inference · Florida counties | absolute error: 0.003 \pm 0.002 -> 0.011 \pm 0.005 | Hurt |
| Use the Florida-average joint distribution for each county | Ecological inference · Florida counties | KL-divergence: 0.007 \pm 0.009 -> 0.251 \pm 0.187 | Hurt |
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