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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)

Paper ID
12356
Venue
VLDB
Year
2021
Pagerank
4.3047774e-05
Overall Rank
9,684 | 32.64%
DOI
10.14778/3467861.3467862

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Incoming Citations (Sorted by Pagerank)

Showing 4 of 4 citing papers.

Rank Citing Paper Year Venue Pagerank
9,683 Hierarchical Entity Resolution using an Oracle 2022 SIGMOD 4.3047774e-05
10,091 LLM-Powered Interactive Graph Search: A Scalable and Practical Approach 2026 SIGMOD 4.1945683e-05
10,923 k-Clustering with Comparison and Distance Oracles 2024 PODS 4.1945683e-05
11,443 Approximation Algorithms for Large Scale Data Analysis 2021 PODS 4.1945683e-05
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

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