activity
20232026
collaborators

7 papers

cs.LG2026

Embedding Hybrid Systems into Continuous Latent Vector Fields

Sangli Teng, Hang Liu, Koushil Sreenath

This work proves that an -dimensional hybrid system can be embedded into an -dimensional Euclidean space equipped with a continuous vector field on its embedded image wheneve…

eess.SP2026

Enabling Safety-Critical Wireless Communications via Safe Reinforcement Learning

Haoran Peng, Tong Wu, Hang Liu +3

Ensuring strict safety guarantees is the paramount challenge for emerging 5G/6G wireless systems, particularly as they increasingly govern mission-critical applications ranging fro…

cs.CR2025

Differential Privacy of Network Parameters from a System Identification Perspective

Andrew Campbell, Anna Scaglione, Hang Liu +3

This paper addresses the problem of protecting network information from privacy system identification (SI) attacks when sharing cyber-physical system simulations. We model analyst…

cs.IT2025

Differentially Private Distribution Release of Gaussian Mixture Models via KL-Divergence Minimization

Hang Liu, Anna Scaglione, Sean Peisert

Gaussian Mixture Models (GMMs) are widely used statistical models for representing multi-modal data distributions, with numerous applications in data mining, pattern recognition, d…

math.ST2024

Shuffled Linear Regression via Spectral Matching

Hang Liu, Anna Scaglione

Shuffled linear regression (SLR) seeks to estimate latent features through a linear transformation, complicated by unknown permutations in the measurement dimensions. This problem…

cs.LG2023

Compressed and Sparse Models for Non-Convex Decentralized Learning

Andrew Campbell, Hang Liu, Leah Woldemariam +1

Recent research highlights frequent model communication as a significant bottleneck to the efficiency of decentralized machine learning (ML), especially for large-scale and over-pa…

cs.IT2023

Low-Complexity Vector Source Coding for Discrete Long Sequences with Unknown Distributions

Leah Woldemariam, Hang Liu, Anna Scaglione

In this paper, we propose a source coding scheme that represents data from unknown distributions through frequency and support information. Existing encoding schemes often compress…