17 citations · 42 across the 7 of their papers we have counts for
7 papers
Automated Data Denoising for Recommendation
Yingqiang Ge, Mostafa Rahmani, Athirai Irissappane +3
In real-world scenarios, most platforms collect both large-scale, naturally noisy implicit feedback and small-scale yet highly relevant explicit feedback. Due to the issue of data…
Uncertainty-Aware Learning Against Label Noise on Imbalanced Datasets
Yingsong Huang, Bing Bai, Shengwei Zhao +2
Learning against label noise is a vital topic to guarantee a reliable performance for deep neural networks. Recent research usually refers to dynamic noise modeling with model outp…
Incentive Compatible Pareto Alignment for Multi-Source Large Graphs
Jian Liang, Fangrui Lv, Di Liu +5
In this paper, we focus on learning effective entity matching models over multi-source large-scale data. For real applications, we relax typical assumptions that data distributions…
A Federated Multi-View Deep Learning Framework for Privacy-Preserving Recommendations
Mingkai Huang, Hao Li, Bing Bai +3
Privacy-preserving recommendations are recently gaining momentum, since the decentralized user data is increasingly harder to collect, by recommendation service providers, due to t…
Why Attentions May Not Be Interpretable?
Bing Bai, Jian Liang, Guanhua Zhang +3
Attention-based methods have played important roles in model interpretations, where the calculated attention weights are expected to highlight the critical parts of inputs~(e.g., k…
Adversarial Infidelity Learning for Model Interpretation
Jian Liang, Bing Bai, Yuren Cao +2
Model interpretation is essential in data mining and knowledge discovery. It can help understand the intrinsic model working mechanism and check if the model has undesired characte…