Summary of Beyond Discrepancy: a Closer Look at the Theory Of Distribution Shift, by Robi Bhattacharjee et al.
Beyond Discrepancy: A Closer Look at the Theory of Distribution Shift
by Robi Bhattacharjee, Nick Rittler, Kamalika Chaudhuri
First submitted to arxiv on: 29 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 the theory of distribution shift for machine learning classifiers, investigating how well they perform on a target distribution that differs significantly from their training distribution. While many models appear to handle this shift with ease, theoretical guarantees are rare and often fail to ensure high accuracy. The authors propose an Invariant-Risk-Minimization (IRM)-like assumption connecting the source and target distributions, and provide conditions for accurate classification using only source data or unlabeled target data. Theoretical guarantees are offered in the large sample regime. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how well machine learning models work when their training data is different from what they’re tested on. Many models seem to do great job adapting to new situations, but it’s hard to know for sure because there aren’t many rules that can guarantee high accuracy. The researchers took a closer look at the problem and came up with an idea called Invariant-Risk-Minimization (IRM). They figured out when you only need data from the old situation or the new one, and when you need both. This helps us understand how to use models in different situations. |
Keywords
» Artificial intelligence » Classification » Machine learning
