Summary of Strategic Littlestone Dimension: Improved Bounds on Online Strategic Classification, by Saba Ahmadi et al.
Strategic Littlestone Dimension: Improved Bounds on Online Strategic Classification
by Saba Ahmadi, Kunhe Yang, Hanrui Zhang
First submitted to arxiv on: 16 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Science and Game Theory (cs.GT)
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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 investigates online binary classification when strategic agents can modify their features to receive a positive classification. The authors introduce the Strategic Littlestone Dimension, a measure that captures the joint complexity of the hypothesis class and manipulation graph. They demonstrate that this dimension characterizes instance-optimal mistake bounds for deterministic learning algorithms in the realizable setting. Additionally, they achieve improved regret in the agnostic setting by accounting for the challenge of not observing agents’ original features. The authors also relax the assumption that the learner knows the manipulation graph, instead assuming their knowledge is captured by a family of graphs. They derive regret bounds for both realizable and agnostic settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how people can change what they look like to get a certain answer when trying to classify something online. The researchers created a new way to measure the difficulty of this problem, called the Strategic Littlestone Dimension. They showed that this dimension helps us understand how well we can do in certain situations. They also found ways to improve how well we can do by taking into account that we don’t always get to see what people started with. |
Keywords
* Artificial intelligence * Classification
