Wenyuan Zhao

ROOM 326

WEB Building

College Station, TX 77840

Hi, I am Wenyuan(Warren), a third year Ph.D. student in the Electrical and Computer Engineering Department at Texas A&M University, advised by Professor Chao Tian. Currently, I am working on the general area of Machine Learning and Information Theory where new algorithms are proposed dealing with diverse settings.

Before joining TAMU, I obtained my M.S. degree in Communication Theory & Systems at UC San Diego, where I worked on Dynamic mmWave Networking with Professor Xinyu Zhang. Before that, I obtained my B.Eng. degree in Information Engineering at Southeast University in 2021.

My research interests lie in machine learning, information theory, and communication. Particularly, I am working on Bayesian deep learning, uncertainty quantification and generative models.

News

Nov 14, 2025	ECE Poster Event Winner @ Texas A&M University!
Oct 11, 2025	NeurIPS 2025 Scholar Award!
Sep 18, 2025	“Partial Information Decomposition via Normalizing Flows in Latent Gaussian Distributions” is accepted by NeurIPS 2025!
Jun 18, 2025	“Weakly heterogeneous PIR” is accepted by IEEE Transactions on Information Theory!
Apr 28, 2025	ISIT 2025 Travel Grant Award!
Jan 21, 2025	“From deep additive kernel learning to last-layer Bayesian neural networks via induced prior approximation” is accepted by AISTATS 2025!
Aug 15, 2023	Pursuing Ph.D. degree at Texas A&M University with Prof. Chao Tian!
Nov 10, 2019	First-prize winner at 2019 Mathematical Contest in Modeling!

Selected Publications

NeurIPS

Partial Information Decomposition via Normalizing Flows in Latent Gaussian Distributions

Wenyuan Zhao, Adithya Balachandran, Chao Tian, and Paul Pu Liang

Advances in Neural Information Processing Systems, 2025

Bib PDF Code

@article{zhao2025partial,
  title = {Partial Information Decomposition via Normalizing Flows in Latent Gaussian Distributions},
  author = {Zhao, Wenyuan and Balachandran, Adithya and Tian, Chao and Liang, Paul Pu},
  journal = {Advances in Neural Information Processing Systems},
  year = {2025},
}

AISTATS

From Deep Additive Kernel Learning to Last-layer Bayesian Neural Networks via Induced Prior Approximation

Wenyuan Zhao, Haoyuan Chen, Tie Liu, Tuo Rui, and Chao Tian

In The 28th International Conference on Artificial Intelligence and Statistics, 2025

Bib PDF Code

@inproceedings{zhao2025deep,
  title = {From Deep Additive Kernel Learning to Last-layer Bayesian Neural Networks via Induced Prior Approximation},
  author = {Zhao, Wenyuan and Chen, Haoyuan and Liu, Tie and Rui, Tuo and Tian, Chao},
  booktitle = {The 28th International Conference on Artificial Intelligence and Statistics},
  year = {2025},
  organization = {PMLR},
}

IEEE TIT

Weakly private information retrieval from heterogeneously trusted servers

Wenyuan Zhao, Yu-Shin Huang, Ruida Zhou, and Chao Tian

IEEE Transactions on Information Theory, 2025

Bib PDF

@article{zhao2025weakly,
  title = {Weakly private information retrieval from heterogeneously trusted servers},
  author = {Zhao, Wenyuan and Huang, Yu-Shin and Zhou, Ruida and Tian, Chao},
  journal = {IEEE Transactions on Information Theory},
  year = {2025},
  publisher = {IEEE},
}