Summary of Revisit Non-parametric Two-sample Testing As a Semi-supervised Learning Problem, by Xunye Tian et al.
Revisit Non-parametric Two-sample Testing as a Semi-supervised Learning Problem
by Xunye Tian, Liuhua Peng, Zhijian Zhou, Mingming Gong, Feng Liu
First submitted to arxiv on: 30 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 proposed paper introduces a novel perspective on non-parametric two-sample testing as a semi-supervised learning (SSL) problem, which is crucial for answering if two samples X and Y are from the same distribution. The authors suggest that traditional methods have limitations in addressing this problem, including reducing data points available for the testing phase or missing discriminative cues. To address these issues, they propose a two-step approach: first, learning inherent representations (IRs) using all data, and then fine-tuning IRs with labeled data to learn discriminative representations (DRs). The authors claim that this approach can effectively leverage unlabeled data, which is essential for achieving strong test power. They also demonstrate the superiority of their proposed method, SSL-C2ST, over traditional methods in extensive experiments and theoretical analysis. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to solve a problem called non-parametric two-sample testing. This means figuring out if two groups of data come from the same place or not. The authors want to make this process better by using special kinds of learning algorithms that can look at both labeled (with answers) and unlabeled data. They think their approach will be more powerful than what’s currently used because it can use all the available data, rather than just some of it. This could lead to better results in many cases. |
Keywords
» Artificial intelligence » Fine tuning » Semi supervised
