随笔分类 - Robust Learning
摘要：Guo S., Zou L., Liu Y., Ye W., Cheng S., Wang S., Chen H., Yin D. and Chang Y. Enhanced doubly robust learning for debiasing post-click conversion rat
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摘要：Tian C., Xie Y., Li Y., Yang N. and Zhao W. Learning to denoise unreliable interactions for graph collaborative filtering. In ACM SIGIR Conference on
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摘要：Wang X., Zhang R., Sun Y. and Qi J. Doubly robust joint learning for recommendation on data missing not at random. In International Conference on Mach
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摘要：Schnabel T., Swaminathan A., Singh A., Chandak N., Joachims T. Recommendations as treatments: debiasing learning and evaluation. In International Conf
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摘要：Ge Y., Tan J., Zhu Y., Xia Y., Luo J., Liu S., Fu Z., Geng S., Li Z. and Zhang Y. Explainable fairness in recommendation. In International ACM SIGIR C
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摘要：Celis L. E., Straszak D. and Vishnoi N. K. Ranking with fairness constraints. arXiv preprint arXiv:1704.06840, 2017. 概 本文讨论在一种'强硬'的 Fairness 约束下, 如何 (
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摘要：Suresh Harini. A framework for understanding sources of harm throughout the machine learning life cycle. arXiv preprint arXiv:1901.10002, 2019. 概 本文介绍
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摘要：Zhu Z., Kim J. and Nguyen T. Fairness among new items in cold start recommender systems. In International ACM SIGIR Conference on Research and Develop
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摘要：Zhang S., Yin H., Chen T., Huang Z., Cui L. and Zhang X. Graph embedding for recommendation against attribute inference attacks. In International Worl
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摘要：Wu C., Wu F., Qi T. and Huang Y. FairRec: fairness-aware news recommendation with decomposed adversarial learning. In AAAI Conference on Artificial In
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摘要：Naghiaei M., Rahmani H. A. and Deldjoo Y. CPFair: personalized consumer and producer fairness re-ranking for recommender systems. In International ACM
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摘要：Samuel D. and Chechik G. Distributional robustness loss for long-tail learning. In International Conference on Computer Vision (ICCV), 2021. 概 本文利用 Di
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摘要：Zhang Y., Tan Y., Zhang M., Liu Y., Chua T. and Ma S. Catch the black sheep: unified framework for shilling attack detection based on fraudulent actio
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摘要：Lin C., Chen S., Li H., Xiao Y., Li L. and Yang Q. Attacking recommender systems with augmented user profiles. In ACM International Conference on Info
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摘要：Zhang H., Li Y., Ding B. and Gao J. Practical data poisoning attack against next-item recommendation. International World Wide Web Conferences (WWW),
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摘要：He X., He Z., Du X. and Chua T. Adversarial personalized ranking for recommendation. In International ACM SIGIR Conference on Research and Development
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摘要：Li B., Wang Y., Singh A. and Vorobeychik Y. Data poisoning attacks on factorization-based collaborative filtering. In Advances in Neural Information P
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摘要：Sagawa S., Koh P. W., Hashimoto T. B. and Liang P. Distributionally robust neural networks for group shifts: on the importance of regularization for w
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摘要：Lee S., Lee H. and Yoon S. Adversarial vertex mixup: toward better adversarially robust generalization. In IEEE Conference on Computer Vsion and Patte
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摘要：Flooding-X: improving bert’s resistance to adversarial attacks via loss-restricted fine-tuning. 概 作者认为通过 flooding 能够使得 loss landscape 平滑, 这有利于抵抗对抗攻击.
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