Research map / Probabilistic and scientific modeling / Molecules and proteins
Deep Direct Likelihood Knockoffs
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use DDLK instead of the baseline knockoff generators | Knockoff feature selection · COVID-19 adverse events | selected features: 10 vs 5, 4, and 4 | Helped |
| Use DDLK instead of the baseline generators | Knockoff feature selection · COVID-19 adverse events | doctor-validated selected features: 8 of which were found to be meaningful | Helped |
| Use DDLK rather than baseline generators | Knockoff feature selection · mixture synthetic benchmark | power | Helped |
| Use DDLK rather than the baselines | Knockoff feature selection · gene semi-synthetic benchmark | power | Mixed |
| Set entropy regularization to λ ≤ 0.1 (ablation) | DDLK · Gaussian synthetic benchmark across tested correlations | FDP calibration: λ ≤ 0.1 | Helped |
| Increase entropy regularization λ (ablation) | DDLK · Gaussian synthetic benchmark | FDP | Hurt |
| Increase entropy regularization λ (ablation) | DDLK · Gaussian synthetic benchmark with weaker covariate correlations | FDP increase | Mixed |
Closest papers by meaning
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- Debiasing Synthetic Data Generated by Deep Generative Models (NeurIPS 2024 · similarity 0.79)
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