Selected Publications

Books

• H. Wang, S. Lin, and J. Zhang, “Continual and Reinforcement Learning for Edge AI: Framework, Foundation, and Algorithm Design”, Springer, 2025.
• S. Lin, Z. Zhou, Z. Zhang, X. Chen, and J. Zhang, “Edge Intelligence in the Making: Optimization, Deep Learning, and Applications”, Morgan & Claypool Publishers, 2020.

Surveys

• S. Mu, and S. Lin, “A Comprehensive Survey of Mixture-of-Experts: Algorithms, Theory, and Applications”, arxiv, 2025.

Bilevel Optimization

• Z. Guan, D. Sow, S. Lin, and Y. Liang, “AdaProx: A Novel Method for Bilevel Optimization under Pessimistic Framework”, CPAL, 2025.
• D. Sow, S. Lin, Z. Wang, and Y. Liang, “Doubly Robust Instance-Reweighted Adversarial Training”, ICLR, 2024.
• S. Lin, D. Sow, K. Ji, Y. Liang, and N. Shroff, “Non-Convex Bilevel Optimization with Time-Varying Objective Functions”, NeurIPS, 2023.

Continual Learning

• H. Li, S. Lin, L. Duan, Y. Liang, and N. Shroff, “Theory on Mixture-of-Experts in Continual Learning”, ICLR, 2025. (Spotlight)
• L. Yang, S. Lin, J. Zhang, and D. Fan, “Efficient Self-supervised Continual Learning with Progressive Task-Correlated Layer Freezing”, ISQED, 2025.
• S. Lin^*, P. Ju^*, Y. Liang, and N. Shroff, “Theory on Forgetting and Generalization of Continual Learning”, ICML, 2023. (^* co-primary authors)
• S. Lin, L. Yang, D. Fan, and J. Zhang, “Beyond Not-Forgetting: Continual Learning with Backward Knowledge Transfer”, NeurIPS, 2022.
• S. Lin, L. Yang, D. Fan, and J. Zhang, “TRGP: Trust Region Gradient Projection for Continual Learning”, ICLR, 2022. (Spotlight)
• L. Yang, S. Lin, J. Zhang, and D. Fan, “CL-LSG: Continual Learning via Learnable Sparse Growth”, NeurIPS Memory in Artificial and Real Intelligence workshop, 2022.

Machine Unlearning

• M. Kazemi, A. Hussain, MRI. Rabin, MA. Alipour, S. Lin, “Unlearning Trojans in Large Language Models: A comparison Between Natural Language and Source Code”, arxiv, 2024.

Reinforcement Learning

• S. Yue, X. Hua, J. Ren, S. Lin, J. Zhang, and Y. Zhang, “OLLIE: Imitation Learning from Offline Pretraining to Online Finetuning”, ICML, 2024.
• S. Yue, J. Liu, X. Hua, J. Ren, S. Lin, J. Zhang, and Y. Zhang, “How to Leverage Diverse Demonstrations in Offline Imitation Learning”, ICML, 2024.
• I. Adham, H. Wang, S. Lin, and J. Zhang, “L-MBOP-E: Latent-Model Based Offline Planning with Extrinsic Policy Guided Exploration”, MOST, 2024.
• H. Wang, S. Lin, and J. Zhang, “Warm-Start Actor-Critic: From Approximation Error to Sub-optimality Gap”, ICML, 2023. (Oral)
• S. Yue, G. Wang, W. Shao, Z. Zhang, S. Lin, J. Ren, and J. Zhang, “CLARE: Conservative Model-Based Reward Learning for Offline Inverse Reinforcement Learning”, ICLR, 2023.
• S. Lin, J. Wan, T. Xu, Y. Liang, and J. Zhang, “Model-Based Offline Meta-Reinforcement Learning with Regularization”, ICLR, 2022.
• H. Wang, S. Lin, and J. Zhang, “Adaptive Ensemble Q-learning: Minimizing Estimation Bias via Error Feedback”, NeurIPS, 2021.
• H. Wang, S. Lin, H. Jafarkhani, and J. Zhang, “Distributed Q-Learning with State Tracking for Multi-agent Networked Control”, AAMAS, 2021. (extended abstract)
• S. Lin, H. Wang, and J. Zhang, “System Identification via Meta-Learning in Linear Time-Varying Environments”, arXiv preprint arXiv:2010.14664, 2020.

Distributed Edge Learning

• M. Dedeoglu, S. Lin, Z. Zhang, and J. Zhang, “Continual Learning of Generative Models with Limited Data: From Wasserstein-1 Barycenter to Adaptive Coalescence”, IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2023.
• S. Lin^*, Ming Shi^*, and All NSF AI-EDGE Faculty Member, “Leveraging Synergies between AI and Networking to Build Next Generation Edge Networks”, CIC, 2022. (invited paper, ^*co-primary authors)
• S. Lin, L. Yang, Z. He, D. Fan, and J. Zhang, “MetaGater: Fast Learning of Conditional Channel Gated Networks via Federated Meta-Learning”, MASS, 2021. (invited paper)
• S. Yue, J. Ren, J. Xin, S. Lin, and J. Zhang, “Inexact-ADMM based Federated Meta-Learning for Fast and Continual Edge Learning”, MobiHoc, 2021.
• S. Lin, M. Dedeoglu, and J. Zhang, “Accelerating Distributed Online Meta-Learning via Multi-Agent Collaboration under Limited Communication”, MobiHoc, 2021.
• S. Lin, G. Yang, and J. Zhang, “A Collaborative Learning Framework via Federated Meta-Learning”, ICDCS, 2020.
• Z. Zhang, S. Lin, M. Dedeoglu, K. Ding, and J. Zhang, “Data-driven Distributionally Robust Optimization for Edge Intelligence”, INFOCOM, 2020.

Applications in Wireless Networks

• J. Wan, S. Lin, Z. Zhang, J. Zhang, and T. Zhang, “Scheduling Real-time Wireless Traffic: A Network-aided Offline Reinforcement Learning Approach”, IEEE Internet of Things Journal, 2023.
• M. Dedeoglu, S. Lin, Z. Zhang, and J. Zhang, “Federated Learning Based Demand Reshaping for Electric Vehicle Charging”, Globecom, 2022.
• S. Lin, J. Zhang, and L. Ying, “Crowdsensing for Spectrum Discovery: A Waze-Inspired Design via Smartphone Sensing”, IEEE/ACM Transactions on Networking, 2020.
• S. Lin, J. Zhang, and L. Ying, “Waze-Inspired Spectrum Discovery via Smartphone Sensing Data Fusion”, WiOPT, 2018. (Best Student Paper Award)