Research map / Learning theory and algorithms / Submodular and game algorithms
Do Less, Get More: Streaming Submodular Maximization with Subsampling
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Subsample arriving elements before processing them | Streaming submodular maximization under a p-matchoid constraint · p-matchoid, monotone | queries per element: O(km) -> O(km/p) | Helped |
| Subsample arriving elements and use the randomized streaming algorithm | Streaming submodular maximization under a p-matchoid constraint · p-matchoid, non-monotone | approximation ratio: \frac{5p + 2 + 1/p}{{1-\varepsilon}} -> 4p + 2 - o(1) | Helped |
| Subsample arriving elements before processing them | Streaming submodular maximization under a p-matchoid constraint · p-matchoid, non-monotone | queries per element: O(\frac{k^2m}{\varepsilon^2} \log \frac{k}{\varepsilon}) -> O(km/p) | Helped |
| Use Sample-Streaming instead of Local-Search | Video summarization · YouTube | running time speedup: 40 times faster | Helped |
| Use Sample-Streaming instead of Local-Search | Video summarization · OVP | running time speedup: 50 times faster | Helped |
| Subsample elements while retaining the streaming algorithm's output quality | Video summarization · YouTube and OVP | summary quality | No clear effect |
Closest papers by meaning
- Streaming Submodular Maximization under a k-Set System Constraint (ICML 2020 · similarity 0.94)
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- Streaming Stochastic Submodular Maximization with On-Demand User Requests (NeurIPS 2025 · similarity 0.90)
- Consistent Submodular Maximization (ICML 2024 · similarity 0.90)
- Deletion-Robust Submodular Maximization with Knapsack Constraints (AAAI 2024 · similarity 0.90)
- Practical $0.385$-Approximation for Submodular Maximization Subject to a Cardinality Constraint (NeurIPS 2024 · similarity 0.89)
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