Summary of Adaptive and Oblivious Statistical Adversaries Are Equivalent, by Guy Blanc et al.
Adaptive and oblivious statistical adversaries are equivalent
by Guy Blanc, Gregory Valiant
First submitted to arxiv on: 17 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computational Complexity (cs.CC); Data Structures and Algorithms (cs.DS)
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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 This paper addresses a fundamental problem in machine learning, exploring how an adversary can corrupt a statistical sample and impact learning outcomes. The authors focus on adversaries with varying levels of knowledge about the sample, distinguishing between those that are adaptive (knowing the contents when choosing corruption) and oblivious (ignoring the sample’s contents). They prove that these two types of adversaries are equivalent up to polynomial factors in the sample size, resolving a long-standing open question. The results have implications for understanding the resilience of machine learning models against various forms of corruption. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how an adversary can make a statistical sample bad and affect how well a machine learning model learns. The authors looked at different types of adversaries that might corrupt the data, some knowing what’s in the sample and others not caring. They found out that these two kinds of adversaries are pretty much the same when it comes to affecting the model’s performance. This means we can use the same methods to protect against both kinds of attacks. |
Keywords
* Artificial intelligence * Machine learning
