Research map / Adversarial robustness and security / Certified and empirical robustness
Provable Robustness of Adversarial Training for Learning Halfspaces with Noise
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Replace a typical convex surrogate loss with a nonconvex sigmoidal loss (ablation) | Adversarial training for halfspaces · ℓ2 robust halfspace learning with agnostic label noise | robust classification error: \tilde O(\sqrt{\mathsf{OPT}_{2,r}}) -> O(\mathsf{OPT}_{2,r}) | Helped |
| Replace a typical convex surrogate loss with a nonconvex sigmoidal loss (ablation) | Adversarial training for halfspaces · ℓ∞ robust halfspace learning with agnostic label noise | robust classification error: \tilde O(d^{1/4} \sqrt{\mathsf{OPT}_{\infty,r}} + d^{1/2} \mathsf{OPT}_{\infty,r}) -> O(d^{1/4} \mathsf{OPT}_{\infty, r} | Helped |
Closest papers by meaning
- Efficiently Learning Adversarially Robust Halfspaces with Noise (ICML 2020 · similarity 0.96)
- The Complexity of Adversarially Robust Proper Learning of Halfspaces with Agnostic Noise (NeurIPS 2020 · similarity 0.92)
- Efficient Testable Learning of Halfspaces with Adversarial Label Noise (NeurIPS 2023 · similarity 0.91)
- Robust learning of halfspaces under log-concave marginals (NeurIPS 2025 · similarity 0.90)
- An Efficient Tester-Learner for Halfspaces (ICLR 2024 · similarity 0.89)
- Consistent Adversarially Robust Linear Classification: Non-Parametric Setting (ICML 2024 · similarity 0.87)
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