Research map / Learning theory and algorithms / Submodular and game algorithms
Pareto Optimization for Subset Selection with Dynamic Cost Constraints
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use POMC (tau=10000) instead of generalized greedy | POMC · Dynamic routing influence maximization, changes 76-100 | influence spread: 127.3422 -> 162.7353 | Helped |
| use POMC with 10000-iteration warm-up phase (tau=10000) instead of generalized greedy (ablation) | POMC · Dynamic routing influence maximization, changes 1-25 | influence spread: 85.0349 -> 85.8794 | Helped |
| use adaptive generalized greedy instead of restarting generalized greedy | Generalized greedy · Dynamic routing influence maximization, changes 76-100 | influence spread: 127.3422 -> 115.2978 | Hurt |
| use adaptive generalized greedy instead of restarting generalized greedy | Generalized greedy · Dynamic cardinality influence maximization, changes 76-100 | influence spread: 167.9512 -> 167.3365 | No clear effect |
| use POMC with tau=1000 instead of generalized greedy | POMC · Dynamic routing influence maximization, changes 1-25 | influence spread: 85.0349 -> 66.3992 | Hurt |
| add warm-up phase to POMC (tau=1000) (ablation) | POMC · Dynamic routing influence maximization, changes 1-25 | influence spread: 66.3992 -> 86.3846 | Helped |
| add warm-up phase to POMC (tau=10000) (ablation) | POMC · Dynamic routing influence maximization, changes 76-100 | influence spread: 162.7353 -> 163.1958 | No clear effect |
| increase generations between changes tau from 1000 to 10000 (ablation) | POMC · Dynamic cardinality influence maximization, changes 76-100 | influence spread: 171.5600 -> 189.6107 | Helped |
Closest papers by meaning
- Pareto Optimization for Subset Selection with Dynamic Partition Matroid Constraints (AAAI 2021 · similarity 0.89)
- On Multiset Selection With Size Constraints (AAAI 2018 · similarity 0.88)
- Beyond Adaptive Submodularity: Approximation Guarantees of Greedy Policy with Adaptive Submodularity Ratio (ICML 2019 · similarity 0.88)
- Improved Theoretically-Grounded Evolutionary Algorithms for Subset Selection with a Linear Cost Constraint (ICML 2025 · similarity 0.87)
- Fast Adaptive Non-Monotone Submodular Maximization Subject to a Knapsack Constraint (NeurIPS 2020 · similarity 0.87)
- An Asymptotically Optimal Approximation Algorithm for Multiobjective Submodular Maximization at Scale (ICML 2025 · similarity 0.86)
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