Summary of Data-driven Error Estimation: Upper Bounding Multiple Errors Without Class Complexity As Input, by Sanath Kumar Krishnamurthy et al.
Data-driven Error Estimation: Upper Bounding Multiple Errors without Class Complexity as Input
by Sanath Kumar Krishnamurthy, Anna Lyubarskaja, Emma Brunskill, Susan Athey
First submitted to arxiv on: 7 May 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 A novel approach is proposed for constructing simultaneous confidence intervals across a class of estimates, applicable to tasks such as multiple mean estimation, bounding generalization error, and adaptive experimental design. The “error estimation problem” is framed as determining a high-probability upper bound on the maximum error for a class of estimates. A data-driven method is presented that derives such bounds for both finite and infinite class settings, adapting to potential correlation structures of random errors without requiring class complexity as an input. This overcomes limitations of existing approaches like union bounding and Talagrand’s inequality-based bounds. The approach is demonstrated through applications including constructing multiple simultaneously valid confidence intervals and optimizing exploration in contextual bandit algorithms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way is found to create confidence intervals that work together for many different estimates, which is important for tasks like guessing multiple means, understanding how well machine learning models generalize, and designing experiments that can adapt. This is called the “error estimation problem” because we want to figure out a high chance upper limit on the biggest error that could happen for these many estimates. A new method is developed that works just by looking at data and doesn’t need to know anything about how complex these groups are. This helps solve problems with other methods that were used before, like combining all the groups together or using special math tricks. The approach is shown to work well through examples including creating multiple confidence intervals that work together and improving decision-making in situations where there’s a lot of uncertainty. |
Keywords
» Artificial intelligence » Generalization » Machine learning » Probability
