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Fast and More Powerful Selective Inference for Sparse High-Order Interaction Model
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use homotopy rather than data splitting for selective inference | Selective inference for SHIM · Synthetic data | true positive rate | Helped |
| Use homotopy rather than polytope-based selective inference | Selective inference for SHIM · Synthetic data | true positive rate | Helped |
| Use homotopy rather than polytope-based selective inference | Selective inference for SHIM · HIV-1 drug-resistance random subsamples | p-value | Helped |
| Add tree pruning to the homotopy mining search (ablation) | Homotopy mining · HIV NRTI D4T, d=7, first-order interactions | computation time (sec): $450.24 \pm 28.50$ -> $ 68.19 \pm 33.17$ | Helped |
| Add tree pruning to the homotopy mining search (ablation) | Homotopy mining · HIV NRTI D4T, d=7, second-order interactions | computation time (sec): $447.59 \pm 22.15$ -> $24.15 \pm 11.83$ | Helped |
| Add tree pruning to the homotopy mining search (ablation) | Homotopy mining · HIV NRTI D4T, d=7, third-order interactions | computation time (sec): $447.19 \pm 37.69$ -> $25.19 \pm 20.50$ | Helped |
| Add tree pruning to the homotopy mining search (ablation) | Homotopy mining · HIV NRTI D4T, d=8, first-order interactions | computation time (sec): > 1 day -> $ 110.25 \pm 55.55$ | Helped |
| Add tree pruning to the homotopy mining search (ablation) | Homotopy mining · HIV NRTI D4T, d=8, second-order interactions | computation time (sec): > 1 day -> $37.70 \pm 19.39$ | Helped |
| Add tree pruning to the homotopy mining search (ablation) | Homotopy mining · HIV NRTI D4T, d=8, third-order interactions | computation time (sec): > 1 day -> $39.45 \pm 33.37$ | Helped |
| Use homotopy rather than polytope-based selective inference | Selective inference for SHIM · Synthetic data | false positive rate | No clear effect |
Closest papers by meaning
- Selective Inference for Sparse High-Order Interaction Models (ICML 2017 · similarity 0.94)
- Permutation-Free High-Order Interaction Tests (ICML 2025 · similarity 0.87)
- Proxy-Based Approximation of Shapley and Banzhaf Interactions (NeurIPS 2026 · similarity 0.83)
- Strong and Weak Identifiability of Optimization-based Causal Discovery in Non-linear Additive Noise Models (ICML 2025 · similarity 0.83)
- Robust group and simultaneous inferences for high-dimensional single index model (NeurIPS 2024 · similarity 0.83)
- Beyond TreeSHAP: Efficient Computation of Any-Order Shapley Interactions for Tree Ensembles (AAAI 2024 · similarity 0.82)
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