Summary of Query-efficient Correlation Clustering with Noisy Oracle, by Yuko Kuroki et al.
Query-Efficient Correlation Clustering with Noisy Oracle
by Yuko Kuroki, Atsushi Miyauchi, Francesco Bonchi, Wei Chen
First submitted to arxiv on: 2 Feb 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Data Structures and Algorithms (cs.DS); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research proposes two novel formulations of online learning problems rooted in Pure Exploration in Combinatorial Multi-Armed Bandits (PE-CMAB). The goal is to cluster n elements using a minimum number of queries to an oracle that returns noisy similarity data. The setting encompasses various application domains where the similarity function is costly and noisy. The paper introduces algorithms combining sampling strategies with classic approximation methods for correlation clustering, providing theoretical guarantees. These polynomial-time algorithms work in cases where the underlying offline optimization problem is NP-hard. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study helps us cluster things more efficiently when we can only get noisy clues about how similar they are. Imagine you have a lot of items to group together based on their characteristics, but it’s hard and slow to figure out which ones go together. We want to find the best way to do this with as few guesses as possible. The researchers developed new methods that use sampling and other techniques to achieve this goal. Their work has important implications for fields where data is noisy or expensive to collect. |
Keywords
* Artificial intelligence * Clustering * Online learning * Optimization
