Summary of Mistake, Manipulation and Margin Guarantees in Online Strategic Classification, by Lingqing Shen et al.
Mistake, Manipulation and Margin Guarantees in Online Strategic Classification
by Lingqing Shen, Nam Ho-Nguyen, Khanh-Hung Giang-Tran, Fatma Kılınç-Karzan
First submitted to arxiv on: 27 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Science and Game Theory (cs.GT); Optimization and Control (math.OC)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 investigates an online strategic classification problem where agents can manipulate their features to obtain a desired label while incurring a cost. The learner aims to predict the agent’s true label given manipulated features, with the true label revealed after prediction. Existing algorithms guarantee finitely many mistakes under a margin assumption but may not encourage truthfulness. To promote truthfulness and maximize the margin classifier, we propose two new algorithms that converge, ensure finite mistakes and manipulations, and perform well across various agent cost structures. We also extend the strategic perceptron with mistake guarantees for different costs. Our experiments on real and synthetic data show that these new algorithms outperform previous ones in terms of margin, manipulation, and mistakes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to guess what someone wants based on what they say, but they can lie or hide the truth to get a certain answer. The paper explores how to make good predictions when people might be dishonest. It proposes new ways to learn from this situation, which helps ensure that people are more truthful and makes better predictions possible. The new methods work well in various situations and even outperform previous approaches in some cases. |
Keywords
* Artificial intelligence * Classification * Synthetic data
