no code implementations • 22 Jan 2023 • Lilin Zhang, Ning Yang, Yanchao Sun, Philip S. Yu
Second, the existing AT methods often achieve adversarial robustness at the expense of standard generalizability (i. e., the accuracy on natural examples) because they make a tradeoff between them.
1 code implementation • 4 Feb 2023 • Zhihui Zhou, Lilin Zhang, Ning Yang
To address this issue, we propose a novel model called Contrastive Collaborative Filtering for Cold-start item Recommendation (CCFCRec), which capitalizes on the co-occurrence collaborative signals in warm training data to alleviate the issue of blurry collaborative embeddings for cold-start item recommendation.
2 code implementations • 11 Apr 2024 • Xinyu Zhu, Lilin Zhang, Ning Yang
The existing works often treat a fairness requirement, represented as a collection of sensitive attributes, as a hyper-parameter, and pursue extreme fairness by completely removing information of sensitive attributes from the learned fair embedding, which suffer from two challenges: huge training cost incurred by the explosion of attribute combinations, and the suboptimal trade-off between fairness and accuracy.