Summary of No Dimensional Sampling Coresets For Classification, by Meysam Alishahi and Jeff M. Phillips
No Dimensional Sampling Coresets for Classification
by Meysam Alishahi, Jeff M. Phillips
First submitted to arxiv on: 7 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computational Geometry (cs.CG)
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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 paper refines and generalizes our understanding of coreset construction for classification problems using the sensitivity sampling framework. The goal is to identify the smallest possible subset of input data that allows us to optimize a loss function with approximation guarantees for the original dataset. The authors’ analysis provides the first no-dimensional coresets, meaning the size of the coreset does not depend on the dimensionality of the data. The results are general and applicable to distributional inputs, allowing for sample complexity bounds and working with various loss functions. A key contribution is a Radamacher complexity version of the main sensitivity sampling approach, which has independent interest. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how to build smaller groups of data that still represent larger datasets accurately. It’s like finding the most important parts of a puzzle and using those to make an approximation. The researchers developed new methods to create these “coresets” without relying on the dimensionality of the data, making it more flexible and useful for different types of problems. |
Keywords
* Artificial intelligence * Classification * Loss function
