Sitemap

A list of all the posts and pages found on the site. For you robots out there, there is an XML version available for digesting as well.

Pages

Posts

Future Blog Post

less than 1 minute read

Published:

This post will show up by default. To disable scheduling of future posts, edit config.yml and set future: false.

Blog Post number 4

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 3

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 2

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 1

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

portfolio

publications

Information-Theoretic Generalization Analysis for Topology-aware Heterogeneous Federated Edge Learning over Noisy Channels

Published in IEEE Signal Processing Letters, 2024

This work provides a generalization analysis for topology-aware heterogeneous federated edge learning.

Recommended citation: Zheshun Wu, Zenglin Xu, Hongfang Yu, and Jie Liu. (2024). Information-Theoretic Generalization Analysis for Topology-aware Heterogeneous Federated Edge Learning over Noisy Channels. IEEE Signal Processing Letters.

Topology Learning for Heterogeneous Decentralized Federated Learning Over Unreliable D2D Networks

Published in IEEE Transactions on Vehicular Technology, 2024

This work studies topology learning for decentralized federated learning over unreliable D2D networks.

Recommended citation: Zheshun Wu, Zenglin Xu, Dun Zeng, Junfan Li, and Jie Liu. (2024). Topology Learning for Heterogeneous Decentralized Federated Learning Over Unreliable D2D Networks. IEEE Transactions on Vehicular Technology.

Advocating for the Silent: Enhancing Federated Generalization for Non-Participating Clients

Published in IEEE Transactions on Neural Networks and Learning Systems, 2024

This work investigates federated generalization for non-participating clients.

Recommended citation: Zheshun Wu, Zenglin Xu, Dun Zeng, Qifan Wang, and Jie Liu. (2024). Advocating for the Silent: Enhancing Federated Generalization for Non-Participating Clients. IEEE Transactions on Neural Networks and Learning Systems.

On the Necessity of Collaboration for Online Model Selection with Decentralized Data

Published in Advances in Neural Information Processing Systems (NeurIPS 2024), 2024

This work studies the necessity of collaboration for online model selection with decentralized data.

Recommended citation: Junfan Li, Zheshun Wu, Zenglin Xu, and Irwin King. (2024). On the Necessity of Collaboration for Online Model Selection with Decentralized Data. In NeurIPS.

Online Optimization for Learning to Communicate over Time-correlated Channels

Published in IEEE Transactions on Cognitive Communications and Networking, 2025

This work studies online optimization for communication over time-correlated channels.

Recommended citation: Zheshun Wu, Junfan Li, Zenglin Xu, Sumei Sun, and Jie Liu. (2025). Online Optimization for Learning to Communicate over Time-correlated Channels. IEEE Transactions on Cognitive Communications and Networking.

Bandit Learning in Matching Markets Robust to Adversarial Corruptions

Published in International Conference on Learning Representations (ICLR 2026), 2026

This work studies robust bandit learning in two-sided matching markets under adversarial corruptions.

Recommended citation: Zheshun Wu, Jinhang Zuo, Zenglin Xu, and Fang Kong. (2026). Bandit Learning in Matching Markets Robust to Adversarial Corruptions. In ICLR.

Enhancing Progressive Ensemble Learning via Normalized Extra-Gradient Initialization

Published in Neural Networks, 2026

This work develops Normalized Extra-Gradient Initialization to accelerate progressive training of sparse MoE models.

Recommended citation: Zheshun Wu, Yu Pan, Dun Zeng, Zenglin Xu, Qifan Wang, and Jie Liu. (2026). Enhancing Progressive Ensemble Learning via Normalized Extra-Gradient Initialization. Neural Networks.

Federated Combinatorial Causal Bandits with Heterogeneous Causal Influences

Published in Conference on Uncertainty in Artificial Intelligence (UAI 2026), 2026

This work studies federated combinatorial causal bandits under heterogeneous causal influences.

Recommended citation: Zheshun Wu, Wei Chen, Zenglin Xu, and Fang Kong. (2026). Federated Combinatorial Causal Bandits with Heterogeneous Causal Influences. In UAI.

talks

teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

This is a description of a teaching experience. You can use markdown like any other post.

Teaching experience 2

Workshop, University 1, Department, 2015

This is a description of a teaching experience. You can use markdown like any other post.