Research map / Learning theory and algorithms / Submodular and game algorithms
Randomized Algorithms for Submodular Function Maximization with a $k$-System Constraint
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| randomized RANDOMMULTIGREEDY with ℓ=2 and p=2/(1+√k) instead of prior deterministic algorithms | non-adaptive k-system submodular maximization · theory | approximation ratio: (1+√k)^2 vs k+2√k+2+3 for prior best | Helped |
| accelerate RANDOMMULTIGREEDY via lazy evaluation | RANDOMMULTIGREEDY · movie recommendation, image summarization, social advertising | number of oracle queries | Helped |
| randomized adaptive policy ADAPTRANDOMGREEDY for non-monotone adaptive objectives | adaptive k-system submodular maximization · social advertising with multiple products | utility | Helped |
| randomized sampling of RAMG with p in (0.9,1) (RAMG+) (ablation) | RANDOMMULTIGREEDY · social advertising with multiple products | utility | Helped |
| RAMG vs FSGS on utility | RAMG · image summarization | utility | No clear effect |
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