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Tackling Instance-Dependent Label Noise via a Universal Probabilistic Model.

, , , , , and . AAAI, page 10183-10191. AAAI Press, (2021)

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A method for the hub location problem of scale-distance tradeoffs., , , and . SOLI, page 434-437. IEEE, (2014)On the Minimal Supervision for Training Any Binary Classifier from Only Unlabeled Data., , , and . ICLR (Poster), OpenReview.net, (2019)Active Feature Acquisition with Supervised Matrix Completion., , , , , and . KDD, page 1571-1579. ACM, (2018)Multi-Class Classification from Noisy-Similarity-Labeled Data., , , , , , , and . CoRR, (2020)Towards Effective Visual Representations for Partial-Label Learning., , , , and . CVPR, page 15589-15598. IEEE, (2023)Probabilistic Margins for Instance Reweighting in Adversarial Training., , , , , , , and . NeurIPS, page 23258-23269. (2021)Estimating Instance-dependent Bayes-label Transition Matrix using a Deep Neural Network., , , , , , and . ICML, volume 162 of Proceedings of Machine Learning Research, page 25302-25312. PMLR, (2022)Progressive Identification of True Labels for Partial-Label Learning., , , , , and . ICML, volume 119 of Proceedings of Machine Learning Research, page 6500-6510. PMLR, (2020)Provably End-to-end Label-noise Learning without Anchor Points., , , , and . ICML, volume 139 of Proceedings of Machine Learning Research, page 6403-6413. PMLR, (2021)Maximum Mean Discrepancy Test is Aware of Adversarial Attacks., , , , , , and . ICML, volume 139 of Proceedings of Machine Learning Research, page 3564-3575. PMLR, (2021)