Research map / Learning theory and algorithms / Submodular and game algorithms
Deletion-Robust Submodular Maximization: Data Summarization with “the Right to be Forgotten”
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use r instances of streaming algorithm (ROBUST-STREAMING) instead of single SIEVE-STREAMING to support deletions | SIEVE-STREAMING on image collection · 100 images, k=5, r=3 | normalized objective value vs. greedy knowing deletions | Helped |
| Increase memory from 1 to r instances with ROBUST-STREAMING under random deletions (ablation) | Yahoo! Webscope click logs · Yahoo! Webscope, 99% deleted | normalized AUC improvement | Helped |
| Naively keep a larger solution (k+m) in a streaming algorithm instead of robust chaining | Streaming submodular maximization with deletions · Coverage bad example | objective value after deleting B1 | Hurt |
| Use stochastic/random greedy deletions (RG/SG) versus deleting representative elements | STREAM-GREEDY on geolocation data · geolocation, deletions during stream | normalized objective value | Mixed |
Closest papers by meaning
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- Streaming Robust Submodular Maximization: A Partitioned Thresholding Approach (NeurIPS 2017 · similarity 0.90)
- Streaming Non-Monotone Submodular Maximization: Personalized Video Summarization on the Fly (AAAI 2018 · similarity 0.90)
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- Online and Streaming Algorithms for Constrained k-Submodular Maximization (AAAI 2025 · similarity 0.86)
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