Summary of What Is Different Between These Datasets?, by Varun Babbar et al.
What is different between these datasets?
by Varun Babbar, Zhicheng Guo, Cynthia Rudin
First submitted to arxiv on: 8 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
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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 toolbox offers a comprehensive solution for comparing datasets with differing distributions, leveraging various case studies across diverse modalities including tabular data, text data, images, time series signals in both low and high-dimensional settings. The approach aims to provide actionable and interpretable insights for better understanding and addressing distribution shifts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to train a machine learning model using real-world data. Sometimes, the data might be different from what your model is used to. This can happen when you collect new data or try to use an existing model on a new task. The problem is that it’s hard to understand why the data is different and how to fix it. To solve this issue, researchers have developed a set of tools that help explain these differences in a way that humans can understand. These tools work with many types of data, including text, images, and numbers. By using these tools, you can gain insights into what’s going on and make better decisions. |
Keywords
* Artificial intelligence * Machine learning * Time series
