Research map / Learning theory and algorithms / Submodular and game algorithms
Streaming k-Submodular Maximization under Noise subject to Size Constraint
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add the denoise step (eta=2) to RSTREAM instead of eta=1 (ablation) | RSTREAM · Influence Maximization (Facebook) | solution quality (estimated f(s)) | Helped |
| Use streaming DSTREAM/RSTREAM instead of non-streaming Greedy | Influence Maximization with k topics · Facebook, B=50 | number of queries | Helped |
| Increase gamma from 0.5 to 1.5 in DSTREAM (ablation) | DSTREAM · Influence Maximization, M=4, B=20 | solution quality and number of queries: quality drops ~13%, queries drop ~40% | Mixed |
| Increase M (3 to 5) (ablation) | DSTREAM and RSTREAM · Influence Maximization, gamma=1.0, B=50 | solution quality | No clear effect |
| Set epsilon=0 so denoise step is unnecessary (eta=1) | RSTREAM · Sensor Placement (Intel Lab) | solution quality | No clear effect |
Closest papers by meaning
- Streaming Submodular Maximization under a k-Set System Constraint (ICML 2020 · similarity 0.93)
- Online and Streaming Algorithms for Constrained k-Submodular Maximization (AAAI 2025 · similarity 0.92)
- Consistent Submodular Maximization (ICML 2024 · similarity 0.90)
- Deletion-Robust Submodular Maximization with Knapsack Constraints (AAAI 2024 · similarity 0.90)
- A Unified Approach to Submodular Maximization Under Noise (NeurIPS 2025 · similarity 0.89)
- Streaming Stochastic Submodular Maximization with On-Demand User Requests (NeurIPS 2025 · similarity 0.89)
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