How to Design Robust Algorithms using Noisy Comparison Oracle
Summary: Robust max/nearest/farthest search under a noisy comparison oracle; two noise models: adversarial and probabilistic. Derives robust k-center and agglomerative clustering with approximation guarantees; analyzes query complexity, validated on real data. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Raghavendra Addanki (University of Massachusetts Amherst)
- 2. Sainyam Galhotra (University of Massachusetts Amherst)
- 3. Barna Saha (University of California Berkeley)
BibTeX Citation
@article{addanki_vldb21,
title = {{How to Design Robust Algorithms using Noisy Comparison Oracle}},
author = {Addanki, Raghavendra and Galhotra, Sainyam and Saha, Barna},
journal = {PVLDB},
series = {{VLDB} '21},
volume = {14},
number = {10},
pages = {1703--1716},
doi = {10.14778/3467861.3467862},
url = {https://doi.org/10.14778/3467861.3467862},
year = {2021}
}Incoming Citations (Sorted by Pagerank)
Showing 4 of 4 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,047 | Hierarchical Entity Resolution using an Oracle | 2022 | SIGMOD | 5.0786422e-05 |
| 10,651 | LLM-Powered Interactive Graph Search: A Scalable and Practical Approach | 2026 | SIGMOD | 4.9605343e-05 |
| 11,549 | k-Clustering with Comparison and Distance Oracles | 2024 | PODS | 4.9605343e-05 |
| 11,998 | Approximation Algorithms for Large Scale Data Analysis | 2021 | PODS | 4.9605343e-05 |
Previous
Page 1 / 1
Next
Outgoing Citations (Sorted by Pagerank)
Showing 10 of 10 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 199 | CrowdER: Crowdsourcing Entity Resolution | 2012 | VLDB | 0.0002551523 |
| 434 | Corleone: Hands-Off Crowdsourcing for Entity Matching | 2014 | SIGMOD | 0.00018323777 |
| 650 | HD-Index: Pushing the Scalability-Accuracy Boundary for Approximate kNN Search in High-Dimensional Spaces | 2018 | VLDB | 0.00015142453 |
| 754 | So Who Won? Dynamic Max Discovery with the Crowd | 2012 | SIGMOD | 0.00014226032 |
| 1,473 | Crowdsourcing Algorithms for Entity Resolution | 2014 | VLDB | 0.0001052886 |
| 2,120 | Comparative Analysis of Approximate Blocking Techniques for Entity Resolution | 2016 | VLDB | 8.9985078e-05 |
| 3,672 | Online Entity Resolution Using an Oracle | 2016 | VLDB | 7.0926043e-05 |
| 4,503 | Top-k Sorting Under Partial Order Information | 2018 | SIGMOD | 6.5565742e-05 |
| 4,941 | Crowdsourced Top-k Queries by Confidence-Aware Pairwise Judgments | 2017 | SIGMOD | 6.3314694e-05 |
| 7,601 | Robust Entity Resolution using Random Graphs | 2018 | SIGMOD | 5.4714372e-05 |
Previous
Page 1 / 1
Next
Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 5,264 | Top-k Nearest Neighbor Search In Uncertain Data Series | 2015 | VLDB |
| 2 | 4,749 | Finding Near Neighbors Through Cluster Pruning | 2007 | PODS |
| 3 | 8,813 | A Generalized Approach for Reducing Expensive Distance Calls for A Broad Class of Proximity Problems | 2021 | SIGMOD |
| 4 | 10,446 | Coresets for Robust Query Optimization | 2026 | PODS |
| 5 | 2,319 | Approximation Algorithms for Clustering Uncertain Data | 2008 | PODS |
| 6 | 11,550 | Improved Approximation Algorithms for Relational Clustering | 2024 | PODS |
| 7 | 1,641 | Efficient Search for the Top-k Probable Nearest Neighbors in Uncertain Databases | 2008 | VLDB |
| 8 | 10,302 | On Saving Outliers for Better Clustering over Noisy Data | 2021 | SIGMOD |
| 9 | 10,471 | Clustering with Set Outliers and Applications in Relational Clustering | 2026 | PODS |
| 10 | 11,549 | k-Clustering with Comparison and Distance Oracles | 2024 | PODS |