activity
20172020
most citedFairness and Accuracy in Federated Learning

36 citations · 69 across the 8 of their papers we have counts for

collaborators

13 papers

cs.LG20202 cited

Adaptive Graph-based Generalized Regression Model for Unsupervised Feature Selection

Yanyong Huang, Zongxin Shen, Fuxu Cai +2

Unsupervised feature selection is an important method to reduce dimensions of high dimensional data without labels, which is benefit to avoid ``curse of dimensionality'' and improv…

cs.LG202036 cited

Fairness and Accuracy in Federated Learning

Wei Huang, Tianrui Li, Dexian Wang +2

In the federated learning setting, multiple clients jointly train a model under the coordination of the central server, while the training data is kept on the client to ensure priv…

cs.CL20204 cited

GRACE: Gradient Harmonized and Cascaded Labeling for Aspect-based Sentiment Analysis

Huaishao Luo, Lei Ji, Tianrui Li +2

In this paper, we focus on the imbalance issue, which is rarely studied in aspect term extraction and aspect sentiment classification when regarding them as sequence labeling tasks…

cs.SI20201 cited

ReAD: A Regional Anomaly Detection Framework Based on Dynamic Partition

Huaishao Luo, Chuishi Meng, Bowen Wu +3

The detection of the abnormal area from urban data is a significant research problem. However, to the best of our knowledge, previous methods designed on spatio-temporal anomalies…

cs.CL20201 cited

Distributional Discrepancy: A Metric for Unconditional Text Generation

Ping Cai, Xingyuan Chen, Peng Jin +2

The purpose of unconditional text generation is to train a model with real sentences, then generate novel sentences of the same quality and diversity as the training data. However,…

cs.DB20209 cited

Efficient Suspected Infected Crowds Detection Based on Spatio-Temporal Trajectories

Huajun He, Ruiyuan Li, Rubin Wang +3

Virus transmission from person to person is an emergency event facing the global public. Early detection and isolation of potentially susceptible crowds can effectively control the…