Research map / Learning theory and algorithms / Submodular and game algorithms
Beyond Adaptive Submodularity: Approximation Guarantees of Greedy Policy with Adaptive Submodularity Ratio
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use adaptive greedy instead of non-adaptive greedy | Adaptive influence maximization · Synthetic bipartite graph, linear threshold | objective value | Helped |
| use adaptive greedy instead of non-adaptive greedy | Adaptive influence maximization · Synthetic bipartite graph, extended linear threshold | objective value | Helped |
| use adaptive greedy instead of Degree or Random | Adaptive influence maximization · Yahoo! Search Marketing graph, linear threshold | objective value | Helped |
| use adaptive greedy instead of Degree or Random | Adaptive influence maximization · Yahoo! Search Marketing graph, extended linear threshold | objective value | Helped |
| use adaptive greedy instead of non-adaptive greedy and noise-oblivious greedy | Adaptive feature selection · Synthetic features, uniform noise [-σ, σ], σ = 0.1 | objective value | Helped |
| use adaptive greedy instead of non-adaptive greedy and noise-oblivious greedy | Adaptive feature selection · Synthetic features, uniform noise [-σ, σ], σ = 0.2 | objective value | Helped |
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