17 citations · 40 across the 8 of their papers we have counts for
8 papers
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…
NVDiff: Graph Generation through the Diffusion of Node Vectors
Xiaohui Chen, Yukun Li, Aonan Zhang +1
Learning to generate graphs is challenging as a graph is a set of pairwise connected, unordered nodes encoding complex combinatorial structures. Recently, several works have propos…
Collaborative Anomaly Detection
Ke Bai, Aonan Zhang, Zhizhong Li +3
In recommendation systems, items are likely to be exposed to various users and we would like to learn about the familiarity of a new user with an existing item. This can be formula…
Nonuniform Negative Sampling and Log Odds Correction with Rare Events Data
HaiYing Wang, Aonan Zhang, Chong Wang
We investigate the issue of parameter estimation with nonuniform negative sampling for imbalanced data. We first prove that, with imbalanced data, the available information about u…
Vertical Federated Learning without Revealing Intersection Membership
Jiankai Sun, Xin Yang, Yuanshun Yao +4
Vertical Federated Learning (vFL) allows multiple parties that own different attributes (e.g. features and labels) of the same data entity (e.g. a person) to jointly train a model.…
Random Function Priors for Correlation Modeling
Aonan Zhang, John Paisley
The likelihood model of high dimensional data can often be expressed as , where is a collection of hidden features shared…
Fully Supervised Speaker Diarization
Aonan Zhang, Quan Wang, Zhenyao Zhu +2
In this paper, we propose a fully supervised speaker diarization approach, named unbounded interleaved-state recurrent neural networks (UIS-RNN). Given extracted speaker-discrimina…
Stochastic Annealing for Variational Inference
San Gultekin, Aonan Zhang, John Paisley
We empirically evaluate a stochastic annealing strategy for Bayesian posterior optimization with variational inference. Variational inference is a deterministic approach to approxi…