Summary of Adversaries with Incentives: a Strategic Alternative to Adversarial Robustness, by Maayan Ehrenberg et al.
Adversaries With Incentives: A Strategic Alternative to Adversarial Robustness
by Maayan Ehrenberg, Roy Ganz, Nir Rosenfeld
First submitted to arxiv on: 17 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Science and Game Theory (cs.GT)
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Summary difficulty | Written by | Summary |
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High | Paper authors | High Difficulty Summary Read the original abstract here |
Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Medium Difficulty summary: Adversarial training is typically used to defend against malicious opponents trying to harm predictive performance. However, this approach often results in unnecessarily conservative models. Instead, a new strategy called “strategic training” models opponents as pursuing their own goals rather than working directly against the classifier. This method uses knowledge or beliefs about the opponent’s possible incentives as inductive bias for learning. The approach is designed to defend against opponents within an “incentive uncertainty set”, which can be useful even with mild knowledge of the adversary’s incentives. Experimental results show that this strategy can offer potential gains depending on how incentives relate to the structure of the learning task. |
Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This paper talks about a new way to make machines learn better by understanding what other “adversaries” (people or computers trying to trick them) might want. Right now, we try to stop these adversaries from harming our predictions, but that makes our models too cautious. Instead, the authors suggest thinking of these opponents as just trying to achieve their own goals. This new approach uses information about what these opponents might want to help machines learn better. The results show that even a little bit of knowledge about an adversary’s goals can be helpful, and how much it helps depends on the task. |