Research map / Learning theory and algorithms / Submodular and game algorithms
Weakly Submodular Maximization Beyond Cardinality Constraints: Does Randomization Help Greedy?
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use Residual Random Greedy for weakly submodular maximization under a general matroid constraint | Non-negative monotone γ-weakly submodular objectives · General matroid constraint | approximation ratio: (1 + 1/γ)^{-2} | Helped |
| Use Residual Random Greedy instead of random feasible-element selection | Weakly submodular optimization · Synthetic linear regression with graphic and partition matroids | normalized log-likelihood | Helped |
| Use greedy selection instead of random feasible-element selection | Weakly submodular optimization · Synthetic linear regression with graphic and partition matroids | normalized log-likelihood | Helped |
| Use Residual Random Greedy instead of random feasible-element selection | DPP determinant objective · Video summarization | determinant | Helped |
| Use Residual Random Greedy instead of random feasible-element selection | Logistic-regression feature selection · MEMset splice donor and acceptor sites | normalized log-likelihood | Helped |
| Use Residual Random Greedy instead of greedy selection | LIME superpixel selection · Black-box interpretation (Bernese mountain dog) | label likelihood | Helped |
| Use Residual Random Greedy instead of random feasible-element selection | LIME superpixel selection · Black-box interpretation (Bernese mountain dog) | label likelihood | Helped |
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