Research map / Learning theory and algorithms / Prediction and decision losses
Stepdown SLOPE for Controlled Feature Selection
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Replace the BH-based SLOPE penalty sequence with stepdown-based sequences for k-FWER or FDP control | SLOPE on orthogonal-design simulations · Orthogonal design, t=50 | Prob(FDP>γ): 0.450 -> 0.001; 0.450 -> 0.003 | Helped |
| Use stepdown-based k-SLOPE or F-SLOPE instead of SLOPE | SLOPE on orthogonal-design simulations · Orthogonal design, t=50 | Power: 1.000; 1.000; 1.000 | No clear effect |
| Use k-SLOPE rather than the classical stepdown k-FWER procedure | Multiple testing under Gaussian design · Gaussian design, weak signals | Power | Helped |
Closest papers by meaning
- Error-Based Knockoffs Inference for Controlled Feature Selection (AAAI 2022 · similarity 0.85)
- The Strong Screening Rule for SLOPE (NeurIPS 2020 · similarity 0.84)
- Best Subset Selection: Optimal Pursuit for Feature Selection and Elimination (ICML 2025 · similarity 0.81)
- Knockoffs Inference for Partially Linear Models with Automatic Structure Discovery (AAAI 2025 · similarity 0.81)
- Model-Agnostic FDR Control via Group Gaussian Mirror and Permutation SHAP (NeurIPS 2026 · similarity 0.81)
- Statistical Test for Feature Selection Pipelines by Selective Inference (ICML 2025 · similarity 0.81)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.