Selected Publications

Book Chapters

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.

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.

L. Yang, S. Lin, J. Zhang, and D. Fan, “Efficient Self-supervised Continual Learning with Progressive Task-Correlated Layer Freezing”, arxiv, 2023.
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.

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.

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.

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)