One Size Does Not Fit All: A Bandit-Based Sampler Combination Framework with Theoretical Guarantees
Summary: SamComb, a bandit-based sampler-assembly framework, blends uniform, stratified, and measure-biased samplers under a budget to estimate population parameters. It casts sampler selection as a multi-armed bandit with exploration–exploitation guarantees, delivering accuracy gains on synthetic and real data without assuming distribution. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Jinglin Peng
- 2. Bolin Ding
- 3. Jiannan Wang
- 4. Kai Zeng
- 5. Jingren Zhou
Incoming Citations (Sorted by Pagerank)
Showing 3 of 3 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,491 | FAAQP: Fast and Accurate Approximate Query Processing based on Bitmap-augmented Sum-Product Network | 2025 | SIGMOD | 4.1905499e-05 |
| 10,543 | AdaNDV: Adaptive Number of Distinct Value Estimation via Learning to Select and Fuse Estimators | 2025 | VLDB | 4.1905499e-05 |
| 10,984 | Enabling Adaptive Sampling for Intra-Window Join: Simultaneously Optimizing Quantity and Quality | 2024 | SIGMOD | 4.1905499e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 22 of 22 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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