Research map / Learning theory and algorithms / Prediction and decision losses
On Benefits of Selection Diversity via Bilevel Exclusive Sparsity
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use bilevel exclusive sparsity instead of L0 logistic regression | Logistic regression · ISOLET | testing accuracy: 80.63%±0.1013% -> 81.14%±0.0729 % | Helped |
| Use bilevel exclusive sparsity instead of L0 logistic regression | Logistic regression · MNIST | testing accuracy: 97.13%±0.1011% -> 97.29%±0.1138 % | Helped |
| Use bilevel exclusive sparsity instead of L0 logistic regression | Logistic regression · ALLAML | testing accuracy: 85.28%±0.1785% -> 85.28%±0.0561 % | No clear effect |
| Use bilevel exclusive sparsity instead of L0 logistic regression | Logistic regression · Colorectal | testing accuracy: 92.14%±0.2203% -> 93.75%±0.0060 % | Helped |
| Use bilevel exclusive sparsity instead of L0 logistic regression | Logistic regression · GISETTE | testing accuracy: 93.78%±0.1528% -> 94.17%±0.1360% | Helped |
| Use bilevel exclusive sparsity instead of L0 logistic regression | Logistic regression · Prostate-GE | testing accuracy: 88.24%±0.1720% -> 88.63%±0.0118 % | Helped |
| Use bilevel exclusive sparsity instead of L0 logistic regression | Logistic regression · LEU | testing accuracy: 93.61%±0.3812% -> 95.56%±0.0673 % | Helped |
| Use bilevel exclusive sparsity instead of L0 logistic regression | Logistic regression · TwitterHealthData | testing accuracy: 83.39%±0.0601% -> 83.50%±0.0241% | Helped |
Closest papers by meaning
- A General Framework for Sparsity Regularized Feature Selection via Iteratively Reweighted Least Square Minimization (AAAI 2017 · similarity 0.85)
- Best Subset Selection: Optimal Pursuit for Feature Selection and Elimination (ICML 2025 · similarity 0.85)
- Differentially Private High-dimensional Variable Selection via Integer Programming (NeurIPS 2025 · similarity 0.83)
- Measures of diversity and space-filling designs for categorical data (ICML 2024 · similarity 0.82)
- GIST: Greedy Independent Set Thresholding for Max-Min Diversification with Submodular Utility (NeurIPS 2025 · similarity 0.82)
- Most Influential Subset Selection: Challenges, Promises, and Beyond (NeurIPS 2024 · similarity 0.82)
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