Summary of Learning Smooth Distance Functions Via Queries, by Akash Kumar et al.
Learning Smooth Distance Functions via Queries
by Akash Kumar, Sanjoy Dasgupta
First submitted to arxiv on: 2 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR); Machine Learning (stat.ML)
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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 The paper explores learning distance functions within the query-based learning framework, where a learner can pose triplet queries about distances between data points. The researchers establish formal guarantees on the number of queries required to learn smooth distance functions, considering two types of approximation: additive and multiplicative. They propose two methods for achieving these approximations: a global approach for additive approximation and a combination of global and local approaches for multiplicative approximation. These methods have quadratic query complexities that scale with the size of the sample space and its ambient dimension. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about learning how to measure distances between things in a special way. It’s like asking if one thing is closer to another or not. The researchers figure out how many times you need to ask this question to learn how to do it correctly. They come up with two ways to do this: one that looks at the whole picture and one that uses multiple measurements. This helps us understand how to measure distances in a better way. |
