A Unified Optimization Algorithm For Solving "Regret-Minimizing Representative" Problems
Summary: A unified optimization framework for RRMS variants replaces bespoke solvers with a single algorithm plus variant-specific oracles. It borrows k-medoids ideas and uses LP, sampling, and volume estimation to tailor oracles, with theory and experiments on real and synthetic data. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Suraj Shetiya
- 2. Abolfazl Asudeh
- 3. Sadia Ahmed
- 4. Gautam Das
Incoming Citations (Sorted by Pagerank)
Showing 3 of 3 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 9,246 | Happiness Maximizing Sets under Group Fairness Constraints | 2023 | VLDB | 4.3690661e-05 |
| 9,772 | Minimum Coresets for Maxima Representation of Multidimensional Data | 2021 | PODS | 4.2856106e-05 |
| 11,195 | rkHit: Representative Query with Uncertain Preference | 2023 | SIGMOD | 4.1945683e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 12 of 12 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 7 | Optimal Aggregation Algorithms for Middleware [Extended Abstract] | 2001 | PODS | 0.0015496097 |
| 1,072 | Regret-Minimizing Representative Databases | 2010 | VLDB | 0.00014270817 |
| 1,597 | Designing Fair Ranking Schemes | 2019 | SIGMOD | 0.00011209846 |
| 2,478 | Computing k-Regret Minimizing Sets | 2014 | VLDB | 8.6927744e-05 |
| 2,615 | Interactive Regret Minimization | 2012 | SIGMOD | 8.4473503e-05 |
| 2,933 | Answering Top-k Queries Using Views | 2006 | VLDB | 7.8679669e-05 |
| 5,116 | Efficient Computation of Regret-ratio Minimizing Set: A Compact Maxima Representative | 2017 | SIGMOD | 5.6830089e-05 |
| 5,255 | Efficient k-Regret Query Algorithm with Restriction-free Bound for any Dimensionality | 2018 | SIGMOD | 5.6013035e-05 |
| 5,555 | On Obtaining Stable Rankings | 2019 | VLDB | 5.4386174e-05 |
| 6,843 | Minimizing Average Regret Ratio in Database | 2016 | SIGMOD | 4.909799e-05 |
| 8,129 | Discovering the Skyline of Web Databases | 2016 | VLDB | 4.5784968e-05 |
| 11,883 | Query Reranking As A Service | 2016 | VLDB | 4.1945683e-05 |
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Semantically Similar Papers
| Overall Rank | Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 2,615 | Interactive Regret Minimization | 2012 | SIGMOD | 8.4473503e-05 |
| 10,927 | Computing A Well-Representative Summary of Conjunctive Query Results | 2024 | PODS | 4.1945683e-05 |
| 9,772 | Minimum Coresets for Maxima Representation of Multidimensional Data | 2021 | PODS | 4.2856106e-05 |
| 5,904 | k-Regret Queries with Nonlinear Utilities | 2015 | VLDB | 5.2790141e-05 |
| 5,255 | Efficient k-Regret Query Algorithm with Restriction-free Bound for any Dimensionality | 2018 | SIGMOD | 5.6013035e-05 |
| 6,843 | Minimizing Average Regret Ratio in Database | 2016 | SIGMOD | 4.909799e-05 |
| 6,816 | RRR: Rank-Regret Representative | 2019 | SIGMOD | 4.9173197e-05 |
| 1,072 | Regret-Minimizing Representative Databases | 2010 | VLDB | 0.00014270817 |
| 2,478 | Computing k-Regret Minimizing Sets | 2014 | VLDB | 8.6927744e-05 |
| 5,116 | Efficient Computation of Regret-ratio Minimizing Set: A Compact Maxima Representative | 2017 | SIGMOD | 5.6830089e-05 |