Research map / Learning theory and algorithms / Sample complexity
Quantum Entropy Scoring for Fast Robust Mean Estimation and Improved Outlier Detection
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use QUE-based filtering instead of the previous fastest approach | Robust mean estimation · Robust mean estimation | running time: \widetilde{O}(\min(nd/\epsilon^6, nd^2)) -> \widetilde{O}(nd) | Helped |
| Choose finite α rather than either extreme (α = 0 or α → ∞) (ablation) | QUE outlier scoring · Synthetic, word embeddings, and CIFAR-10 outlier detection | ROCAUC | Helped |
| Increase the number of outlier directions (ablation) | QUE and naive spectral scoring · Synthetic outlier detection | ROCAUC | Mixed |
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