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Provable robustness against all adversarial $l_p$-perturbations for $p1$.

, and . ICLR, OpenReview.net, (2020)

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Provably robust boosted decision stumps and trees against adversarial attacks., and . NeurIPS, page 12997-13008. (2019)On the Adversarial Robustness of Multi-Modal Foundation Models., and . ICCV (Workshops), page 3679-3687. IEEE, (2023)Identifying Systematic Errors in Object Detectors with the SCROD Pipeline., , and . ICCV (Workshops), page 4092-4101. IEEE, (2023)Generalized Matrix Means for Semi-Supervised Learning with Multilayer Graphs., , and . NeurIPS, page 14848-14857. (2019)Provably Adversarially Robust Detection of Out-of-Distribution Data (Almost) for Free., , and . NeurIPS, (2022)Meta-Learning the Search Distribution of Black-Box Random Search Based Adversarial Attacks., , and . NeurIPS, page 30181-30195. (2021)Adversarial Robustness on In- and Out-Distribution Improves Explainability., , and . ECCV (26), volume 12371 of Lecture Notes in Computer Science, page 228-245. Springer, (2020)The loss surface and expressivity of deep convolutional neural networks., and . ICLR (Workshop), OpenReview.net, (2018)Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks., and . ICML, volume 119 of Proceedings of Machine Learning Research, page 2206-2216. PMLR, (2020)Confidence-Calibrated Adversarial Training: Generalizing to Unseen Attacks., , and . ICML, volume 119 of Proceedings of Machine Learning Research, page 9155-9166. PMLR, (2020)