Summary of Sample Compression Scheme Reductions, by Idan Attias and Steve Hanneke and Arvind Ramaswami
Sample Compression Scheme Reductions
by Idan Attias, Steve Hanneke, Arvind Ramaswami
First submitted to arxiv on: 16 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 presents novel reductions from sample compression schemes in multiclass classification, regression, and adversarially robust learning settings to binary sample compression schemes. The authors show that assuming a compression scheme for binary classes of size f(d_VC), where d_VC is the VC dimension, they can obtain multiclass compression schemes with sizes O(f(d_G)) and O(f(d_P)), respectively. These results have significant implications if the sample compression conjecture is resolved, extending the proof to other settings. The paper also establishes similar results for adversarially robust learning and provides an example of a concept class that is robustly learnable but has no bounded-size compression scheme. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research explores how to compress data in machine learning models. They show that if they can compress binary data (yes or no answers), they can also compress more complex data like pictures and words. This is important because it could help improve the efficiency of machine learning algorithms. The authors also provide an example of a situation where they were able to compress data, but only for certain types of problems. |
Keywords
» Artificial intelligence » Classification » Machine learning » Regression
