Summary of Taking a Moment For Distributional Robustness, by Jabari Hastings et al.
Taking a Moment for Distributional Robustness
by Jabari Hastings, Christopher Jung, Charlotte Peale, Vasilis Syrgkanis
First submitted to arxiv on: 8 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 introduces a new objective for distributionally robust learning that focuses on minimizing the worst-case distance to the true conditional expectation of labels given each covariate. This approach is shown to be equivalent to minimizing the worst-case _2-distance to the true conditional expectation using an adversarial moment violation-based min-max objective. The paper also shows that in the case of square loss, minimizing the worst-case regret is equivalent to minimizing the worst-case _2-distance to the true conditional expectation. The proposed approach provides large empirical savings in computational cost while maintaining the noise-oblivious worst-distribution guarantee. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores a new way to learn from data that might be noisy or different from what we expect. Instead of trying to perform well on many possible datasets, it tries to get as close as possible to the true underlying relationship between the variables. This is important because real-world data can be messy and unreliable. The paper shows that this approach can work just as well as previous methods but is much faster and more efficient. |
