Research map / Learning theory and algorithms / Submodular and game algorithms
Deletion Robust Submodular Maximization over Matroids
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use a threshold-bucket summary and randomized selection for deletion robustness in the centralized setting | Monotone submodular maximization over general matroids · Centralized deletion-robust | approximation factor: 3.582+O(ε) | Helped |
| Use a streaming summary with threshold buckets and matroid-feasible swaps | Monotone submodular maximization over general matroids · Streaming deletion-robust | approximation factor: 5.582+O(ε) | Helped |
| Increase ε from 0.3 to 0.99 (ablation) | Proposed algorithms · Additional experiments across datasets | objective value: ≈ 1300 to ≈ 1200 | Hurt |
| Use the streaming deletion-robust algorithm instead of its omniscient counterpart | Streaming algorithm · Reported experiments | objective value: up to 20%; up to 2% | Mixed |
| Use the centralized deletion-robust algorithm instead of the omniscient benchmark | Centralized algorithm · Reported experiments | objective value relative to omniscient benchmark: 90-95% | Hurt |
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