Research map / Learning theory and algorithms / Sample complexity
Trimmed Density Ratio Estimation
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Trim high log-density-ratio samples using TR-DRE with ν = 90% | Density ratio estimation for sparse Markov-network changes with an injected outlier · Gaussian Markov networks | true positive rate | Helped |
| Add one outlier to the data used by standard DRE | DRE for sparse Markov-network changes · Gaussian Markov networks | true positive rate | Hurt |
| Trim samples with high density ratios (ablation) | TR-DRE for sparse Markov-network changes · Gaussian Markov networks | true positive rate | Hurt |
| Increase the number of high-ratio windows selected by tuning ν (ablation) | TR-DRE for relative novelty detection · Relative novelty detection from images | relative novelty detection | Mixed |
| Use the reference dataset Q when scoring image windows | TR-DRE · Relative novelty detection from images | relative novelty detection | Helped |
| Select the top 3% highest density-ratio windows using thresholded DRE | Thresholded-DRE · Relative novelty detection from images | relative novelty detection | Mixed |
Closest papers by meaning
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- ConjNorm: Tractable Density Estimation for Out-of-Distribution Detection (ICLR 2024 · similarity 0.84)
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