3 citations · 16 across the 9 of their papers we have counts for
9 papers
Decoupled Entity Representation Learning for Pinterest Ads Ranking
Jie Liu, Yinrui Li, Jiankai Sun +12
In this paper, we introduce a novel framework following an upstream-downstream paradigm to construct user and item (Pin) embeddings from diverse data sources, which are essential f…
Privacy Preserving Conversion Modeling in Data Clean Room
Kungang Li, Xiangyi Chen, Ling Leng +3
In the realm of online advertising, accurately predicting the conversion rate (CVR) is crucial for enhancing advertising efficiency and user satisfaction. This paper addresses the…
Graph-Based Model-Agnostic Data Subsampling for Recommendation Systems
Xiaohui Chen, Jiankai Sun, Taiqing Wang +3
Data subsampling is widely used to speed up the training of large-scale recommendation systems. Most subsampling methods are model-based and often require a pre-trained pilot model…
Netflix and Forget: Efficient and Exact Machine Unlearning from Bi-linear Recommendations
Mimee Xu, Jiankai Sun, Xin Yang +2
People break up, miscarry, and lose loved ones. Their online streaming and shopping recommendations, however, do not necessarily update, and may serve as unhappy reminders of their…
Label Inference Attack against Split Learning under Regression Setting
Shangyu Xie, Xin Yang, Yuanshun Yao +3
As a crucial building block in vertical Federated Learning (vFL), Split Learning (SL) has demonstrated its practice in the two-party model training collaboration, where one party h…
Weak Proxies are Sufficient and Preferable for Fairness with Missing Sensitive Attributes
Zhaowei Zhu, Yuanshun Yao, Jiankai Sun +2
Evaluating fairness can be challenging in practice because the sensitive attributes of data are often inaccessible due to privacy constraints. The go-to approach that the industry…
DPAUC: Differentially Private AUC Computation in Federated Learning
Jiankai Sun, Xin Yang, Yuanshun Yao +3
Federated learning (FL) has gained significant attention recently as a privacy-enhancing tool to jointly train a machine learning model by multiple participants. The prior work on…
Differentially Private Multi-Party Data Release for Linear Regression
Ruihan Wu, Xin Yang, Yuanshun Yao +4
Differentially Private (DP) data release is a promising technique to disseminate data without compromising the privacy of data subjects. However the majority of prior work has focu…
Deep Retrieval: Learning A Retrievable Structure for Large-Scale Recommendations
Weihao Gao, Xiangjun Fan, Chong Wang +7
One of the core problems in large-scale recommendations is to retrieve top relevant candidates accurately and efficiently, preferably in sub-linear time. Previous approaches are mo…