activity
20052023
most citedPartition Information and its Transmission over Boolean Multi-Access Channels

19 citations · 85 across the 30 of their papers we have counts for

collaborators

33 papers

cs.IT2023

The First and Second-Order Asymptotics of Covert Communication over AWGN Channels

Xinchun Yu, Shuangqing Wei, Shao-Lun Huang +1

This paper investigates the asymptotics of the maximal throughput of communication over AWGN channels by channel uses under a covert constraint in terms of an upper bound o…

cs.CR2022★ 1 cited

The Economics of Privacy and Utility: Investment Strategies

Chandra Sharma, George Amariucai, Shuangqing Wei

The inevitable leakage of privacy as a result of unrestrained disclosure of personal information has motivated extensive research on robust privacy-preserving mechanisms. However,…

cs.LG2022★ 6 cited

Uncertainty-Autoencoder-Based Privacy and Utility Preserving Data Type Conscious Transformation

Bishwas Mandal, George Amariucai, Shuangqing Wei

We propose an adversarial learning framework that deals with the privacy-utility tradeoff problem under two types of conditions: data-type ignorant, and data-type aware. Under data…

cs.LG2021

Information-Bottleneck-Based Behavior Representation Learning for Multi-agent Reinforcement learning

Yue Jin, Shuangqing Wei, Jian Yuan +1

In multi-agent deep reinforcement learning, extracting sufficient and compact information of other agents is critical to attain efficient convergence and scalability of an algorith…

cs.IT2020★ 1 cited

Total Variation Distance Based Performance Analysis of Covert Communication over AWGN Channels in Non-asymptotic Regime

Xinchun Yu, Shuangqin Wei, Yuan Luo

This paper investigates covert communication over an additive white Gaussian noise (AWGN) channel in finite block length regime on the assumption of Gaussian codebooks. We first re…

stat.ML2020★ 3 cited

VAE-KRnet and its applications to variational Bayes

Xiaoliang Wan, Shuangqing Wei

In this work, we have proposed a generative model, called VAE-KRnet, for density estimation or approximation, which combines the canonical variational autoencoder (VAE) with our re…