2 citations · 3 across the 6 of their papers we have counts for
6 papers
Supervised Algorithmic Fairness in Distribution Shifts: A Survey
Minglai Shao, Dong Li, Chen Zhao +3
Supervised fairness-aware machine learning under distribution shifts is an emerging field that addresses the challenge of maintaining equitable and unbiased predictions when faced…
Privacy-Preserving Sequential Recommendation with Collaborative Confusion
Wei Wang, Yujie Lin, Pengjie Ren +7
Sequential recommendation has attracted a lot of attention from both academia and industry, however the privacy risks associated to gathering and transferring users' personal inter…
TencentLLMEval: A Hierarchical Evaluation of Real-World Capabilities for Human-Aligned LLMs
Shuyi Xie, Wenlin Yao, Yong Dai +11
Large language models (LLMs) have shown impressive capabilities across various natural language tasks. However, evaluating their alignment with human preferences remains a challeng…
Adaptation Speed Analysis for Fairness-aware Causal Models
Yujie Lin, Chen Zhao, Minglai Shao +2
For example, in machine translation tasks, to achieve bidirectional translation between two languages, the source corpus is often used as the target corpus, which involves the trai…
A Self-Correcting Sequential Recommender
Yujie Lin, Chenyang Wang, Zhumin Chen +6
Sequential recommendations aim to capture users' preferences from their historical interactions so as to predict the next item that they will interact with. Sequential recommendati…
Modeling Sequential Recommendation as Missing Information Imputation
Yujie Lin, Zhumin Chen, Zhaochun Ren +5
Side information is being used extensively to improve the effectiveness of sequential recommendation models. It is said to help capture the transition patterns among items. Most pr…