Research map / Probabilistic and scientific modeling / Molecules and proteins
Selective Inference for Sparse High-Order Interaction Models
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| naive inference without selection bias correction (ablation) | MS and OMP feature selection on synthetic data · synthetic FW-FPR | family-wise false positive rate | Hurt |
| selective inference (conditioning on selection event) vs data splitting | MS and OMP on synthetic data · synthetic TPR | true positive rate | Helped |
| tree-based pruning computational trick for selection event (ablation) | MS selective inference · synthetic, d=100 to 10000, n up to 10000 | computation time (sec): e.g., d=100 n=10000 zeta=0.8: 1.98e4 s without vs 5.04 s with | Helped |
| tree-based pruning computational trick for selection event (ablation) | OMP selective inference · synthetic, d=100 to 10000, n up to 10000 | computation time (sec): e.g., n=100 d=100 zeta=0.8: 883 s without vs 0.233 s with | Helped |
| selective inference on high-order interactions with d=10000, r=5, k=10 (MS) | MS selective inference · HIV drug resistance | number of Bonferroni-significant interactions | No clear effect |
Closest papers by meaning
- Fast and More Powerful Selective Inference for Sparse High-Order Interaction Model (AAAI 2022 · similarity 0.94)
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- Bias-Variance Trade-Off in Hierarchical Probabilistic Models Using Higher-Order Feature Interactions (AAAI 2019 · similarity 0.85)
- Permutation-Free High-Order Interaction Tests (ICML 2025 · similarity 0.85)
- WHInter: A Working set algorithm for High-dimensional sparse second order Interaction models (ICML 2018 · similarity 0.85)
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