Minimizing Average Regret Ratio in Database
Summary: Proposes ARR to select k representative points, basing satisfaction on utility distribution rather than max regret. Proves ARR is supermodular and enables approximation, yielding fixed-size, distribution-aware samples without user input, unlike k-regret. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
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Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 6,816 | RRR: Rank-Regret Representative | 2019 | SIGMOD | 4.9173197e-05 |
| 6,834 | A Unified Optimization Algorithm For Solving "Regret-Minimizing Representative" Problems | 2020 | VLDB | 4.9117328e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 1 of 1 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,072 | Regret-Minimizing Representative Databases | 2010 | VLDB | 0.00014270817 |
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