Research map / Learning theory and algorithms / Sample complexity
Mixture Proportion Estimation via Kernel Embeddings of Distributions
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use kernel mean embedding gradient-thresholding estimators (KM1 and KM2) instead of the comparison algorithms | Mixture proportion estimation · Six UCI classification datasets | absolute mixture proportion error | Mixed |
| Set the gradient threshold using the KM1 rule rather than the KM2 empirical convex-combination rule (ablation) | Kernel mean based gradient thresholder · Six UCI classification datasets | absolute mixture proportion error | No clear effect |
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