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With False Friends Like These, Who Can Notice Mistakes?, , , and . AAAI, page 8458-8466. AAAI Press, (2022)Improving Model Robustness by Adaptively Correcting Perturbation Levels with Active Queries., , , and . AAAI, page 9161-9169. AAAI Press, (2021)Open-Sampling: Exploring Out-of-Distribution data for Re-balancing Long-tailed datasets., , , , and . ICML, volume 162 of Proceedings of Machine Learning Research, page 23615-23630. PMLR, (2022)With False Friends Like These, Who Can Have Self-Knowledge?, and . CoRR, (2020)On the Vulnerability of Adversarially Trained Models Against Two-faced Attacks., , , , , and . ICLR, OpenReview.net, (2024)Can Adversarial Training Be Manipulated By Non-Robust Features?, , , , , and . NeurIPS, (2022)Better Safe Than Sorry: Preventing Delusive Adversaries with Adversarial Training., , , , and . NeurIPS, page 16209-16225. (2021)Multiple-Instance Learning from Similar and Dissimilar Bags., , , , , , , and . KDD, page 374-382. ACM, (2021)Deciphering Raw Data in Neuro-Symbolic Learning with Provable Guarantees., , , and . AAAI, page 15310-15318. AAAI Press, (2024)Open-set Label Noise Can Improve Robustness Against Inherent Label Noise., , , and . NeurIPS, page 7978-7992. (2021)