Research map / Label-efficient and robust learning / Domain adaptation and generalization
A New PAC-Bayesian Perspective on Domain Adaptation
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Adding the target-disagreement + source-joint-error trade-off (DALC) instead of PBDA's bound | linear classifiers domain adaptation · Amazon 12 tasks average | error rate: 0.200 vs 0.208 (PBDA) | Helped |
| Domain adaptation (DALC) vs non-adaptive SVM | linear classifiers · Amazon 12 tasks average | error rate: 0.200 vs 0.231 | Helped |
| Adding the source-target divergence weighting with RCV-selected hyperparameters | DALC on Amazon electro→DVDs and DVDs→electro · Amazon electro→DVDs | error rate: 0.256 vs 0.214 (DASVM best) | Hurt |
| Using the kernel-free linear DALC vs its own RCV tuning with wide grid | Intertwining moons toy problem · toy rotation 50 degrees | qualitative decision boundary | Helped |
| Wilcoxon test of DALC vs PBDA | Amazon 12 tasks · Amazon | probability DALC better than PBDA: 89.5% probability | Mixed |
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