Summary of A Fundamental Accuracy–robustness Trade-off in Regression and Classification, by Sohail Bahmani
A Fundamental Accuracy–Robustness Trade-off in Regression and Classification
by Sohail Bahmani
First submitted to arxiv on: 6 Nov 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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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 derives a fundamental trade-off between standard and adversarial risk in a general situation, formalizing the intuition that “If no (nearly) optimal predictor is smooth, adversarial robustness comes at the cost of accuracy.” Specifically, it explores this trade-off in regression with polynomial ridge functions under mild regularity conditions. The authors evaluate this derived trade-off using benchmarks and metrics relevant to the task. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how to make predictions better when there’s noise or cheating involved. It shows that if you want your predictions to be robust against these kinds of attacks, it will come at a cost – your accuracy will suffer. To test this idea, the researchers looked at polynomial ridge functions in regression and found that this trade-off holds true under certain conditions. |
Keywords
* Artificial intelligence * Regression
