Summary of Towards Calibrated Losses For Adversarial Robust Reject Option Classification, by Vrund Shah et al.
Towards Calibrated Losses for Adversarial Robust Reject Option Classification
by Vrund Shah, Tejas Chaudhari, Naresh Manwani
First submitted to arxiv on: 14 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 robust classification in scenarios where misclassification is costly, such as autonomous driving and medical diagnosis. The authors propose an Adversarial Robust Reject Option (ARRO) setting, which enables classifiers to abstain from predictions when uncertain. They introduce an adversarial robust reject option loss function ({d}^{}) and analyze its properties for linear classifiers ({}). The paper also provides a characterization result for surrogates calibrated in the ARRO setting, demonstrating that certain convex and quasi-concave conditional risk cases do not satisfy calibration conditions. Empirical results on a synthetically generated dataset show that Shifted Double Ramp Loss (DRL) and Shifted Double Sigmoid Loss (DSL) are robust against adversarial perturbations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper is about making computer models more reliable in real-world situations where mistakes can have serious consequences. For example, if a self-driving car makes an incorrect decision, it could cause accidents or injuries. The authors explore ways to improve the performance of these models by allowing them to say “I’m not sure” when they’re uncertain. They develop new methods and test them on artificial data to show that their approach works well. |
Keywords
» Artificial intelligence » Classification » Loss function » Sigmoid
