A Unified Optimization Algorithm For Solving “Regret-Minimizing Representative” Problems
Summary: Introduces a unified framework for regret-minimizing representative selection across worst-case and aggregate variants, isolating differences in variant-specific oracles. Combines K-MEDOIDS-style optimization with LP, graph edge sampling, and convex-polytope volume estimation. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Suraj Shetiya (University of Texas)
- 2. Abolfazl Asudeh (University of Illinois Chicago)
- 3. Sadia Ahmed (University of Texas)
- 4. Gautam Das (University of Texas)
BibTeX Citation
@article{shetiya_vldb20,
title = {{A Unified Optimization Algorithm For Solving “Regret-Minimizing Representative” Problems}},
author = {Shetiya, Suraj and Asudeh, Abolfazl and Ahmed, Sadia and Das, Gautam},
journal = {PVLDB},
series = {{VLDB} '20},
volume = {13},
number = {3},
pages = {239--251},
doi = {10.14778/3368289.3368291},
url = {https://doi.org/10.14778/3368289.3368291},
year = {2020}
}Incoming Citations (Sorted by Pagerank)
Showing 3 of 3 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 9,609 | Happiness Maximizing Sets under Group Fairness Constraints | 2023 | VLDB | 5.1376962e-05 |
| 10,124 | Minimum Coresets for Maxima Representation of Multidimensional Data | 2021 | PODS | 5.0638787e-05 |
| 11,764 | rkHit: Representative Query with Uncertain Preference | 2023 | SIGMOD | 4.9605343e-05 |
Previous
Page 1 / 1
Next
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 |
|---|---|---|---|---|
| 5 | Optimal Aggregation Algorithms for Middleware [Extended Abstract] | 2001 | PODS | 0.0010688884 |
| 1,092 | Regret-Minimizing Representative Databases | 2010 | VLDB | 0.00012052099 |
| 1,385 | Designing Fair Ranking Schemes | 2019 | SIGMOD | 0.00010823535 |
| 2,119 | Computing k-Regret Minimizing Sets | 2014 | VLDB | 8.9989036e-05 |
| 2,551 | Interactive Regret Minimization | 2012 | SIGMOD | 8.2864226e-05 |
| 2,566 | Answering Top-k Queries Using Views | 2006 | VLDB | 8.2679821e-05 |
| 4,856 | Efficient Computation of Regret-ratio Minimizing Set: A Compact Maxima Representative | 2017 | SIGMOD | 6.3655902e-05 |
| 5,092 | Efficient k-Regret Query Algorithm with Restriction-free Bound for any Dimensionality | 2018 | SIGMOD | 6.2656058e-05 |
| 5,624 | On Obtaining Stable Rankings | 2019 | VLDB | 6.0456849e-05 |
| 7,053 | Minimizing Average Regret Ratio in Database | 2016 | SIGMOD | 5.5958722e-05 |
| 8,557 | Discovering the Skyline of Web Databases | 2016 | VLDB | 5.3017168e-05 |
| 12,425 | Query Reranking As A Service | 2016 | VLDB | 4.9605343e-05 |
Previous
Page 1 / 1
Next
Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 2,551 | Interactive Regret Minimization | 2012 | SIGMOD |
| 2 | 7,537 | Computing A Well-Representative Summary of Conjunctive Query Results | 2024 | PODS |
| 3 | 10,124 | Minimum Coresets for Maxima Representation of Multidimensional Data | 2021 | PODS |
| 4 | 5,802 | k-Regret Queries with Nonlinear Utilities | 2015 | VLDB |
| 5 | 5,092 | Efficient k-Regret Query Algorithm with Restriction-free Bound for any Dimensionality | 2018 | SIGMOD |
| 6 | 7,053 | Minimizing Average Regret Ratio in Database | 2016 | SIGMOD |
| 7 | 6,736 | RRR: Rank-Regret Representative | 2019 | SIGMOD |
| 8 | 1,092 | Regret-Minimizing Representative Databases | 2010 | VLDB |
| 9 | 2,119 | Computing k-Regret Minimizing Sets | 2014 | VLDB |
| 10 | 4,856 | Efficient Computation of Regret-ratio Minimizing Set: A Compact Maxima Representative | 2017 | SIGMOD |