activity
20192021
most citedFederated -Differential Privacy

9 citations · 16 across the 3 of their papers we have counts for

collaborators

5 papers

stat.ME20215 cited

Minimax Rates and Adaptivity in Combining Experimental and Observational Data

Shuxiao Chen, Bo Zhang, Ting Ye

Randomized controlled trials (RCTs) are the gold standard for evaluating the causal effect of a treatment; however, they often have limited sample sizes and sometimes poor generali…

stat.ME2021

Estimating and Improving Dynamic Treatment Regimes With a Time-Varying Instrumental Variable

Shuxiao Chen, Bo Zhang

Estimating dynamic treatment regimes (DTRs) from retrospective observational data is challenging as some degree of unmeasured confounding is often expected. In this work, we develo…

stat.ML20219 cited

Federated -Differential Privacy

Qinqing Zheng, Shuxiao Chen, Qi Long +1

Federated learning (FL) is a training paradigm where the clients collaboratively learn models by repeatedly sharing information without compromising much on the privacy of their lo…

cs.LG20202 cited

Label-Aware Neural Tangent Kernel: Toward Better Generalization and Local Elasticity

Shuxiao Chen, Hangfeng He, Weijie J. Su

As a popular approach to modeling the dynamics of training overparametrized neural networks (NNs), the neural tangent kernels (NTK) are known to fall behind real-world NNs in gener…

stat.ML2019

A Group-Theoretic Framework for Data Augmentation

Shuxiao Chen, Edgar Dobriban, Jane H Lee

Data augmentation is a widely used trick when training deep neural networks: in addition to the original data, properly transformed data are also added to the training set. However…