Research map / Learning theory and algorithms / Submodular and game algorithms
Optimization of Chance-Constrained Submodular Functions
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use Chernoff bounds instead of Chebyshev's inequality for the GA surrogate | GA with uniform expected weights · Digg influence maximization, most tested δ values | influence spread | Helped |
| Use Chernoff bounds instead of Chebyshev's inequality for the GA surrogate | GA with uniform expected weights · Digg, high budgets and α=0.001 or 0.0001 | number of selected elements | Helped |
| Use Chebyshev's inequality instead of Chernoff bounds for the GA surrogate | GA with uniform expected weights · Digg, α=0.1 or 0.01 | influence spread | Helped |
| Switch between Chebyshev's inequality and Chernoff bounds | GGA with degree-dependent expected weights · Digg, B=100 and B=500 | influence spread | No clear effect |
| Increase uncertainty δ | GA and GGA · Digg influence maximization | influence spread | Hurt |
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