Welcome to the website of the Learning and INference Systems (LINs) Lab at Westlake University!

Our research interests lie in the intersection of optimization and generalization for deep learning:

  • leveraging theoretical/empirical understanding (e.g., loss landscape, and training dynamics)
  • to design efficient & robust methods (both learning and inference)
  • for deep learning (centralized) and collaborative deep learning (distributed and/or decentralized),
  • under imperfect environments (e.g., noisy, heterogeneous, and hardware-constrained).

Lab activities:


Jul 2, 2024 Congratulations to Yuxuan on the acceptance of his paper on Multimodal benchmark in pathology at ECCV 2024.
May 2, 2024 Our paper on distribution-shift-robust Federated Learning was accepted at this year’s ICML 2024 conference. Congratulations to Yongxin.
Feb 27, 2024 Our paper on efficient dataset distillation was accepted at this year’s CVPR 2024 conference. Congratulations to Peng (he also received a CVPR 2024 travel grant).
Feb 4, 2024 We are looking for self-motivated research internship students! Please email us with your CV.
Jan 16, 2024 Two papers of our group were accepted at this year’s ICLR 2024 conference, on model selection for robust multi-modal model reasoning and parameter-efficient fine-tuning. Congratulations to Xiangyan, Rongxue, Haobo and Hao. In addition, we present workshop papers on collaborative knowledge editing for LLMs, flash tree-attention for efficient LLM inference, federated unlearning, and any-scale dataset distillation.

Selected publications

  1. CVPR 2024
    On the Diversity and Realism of Distilled Dataset: An Efficient Dataset Distillation Paradigm
    Sun, PengShi, Bei, Yu, Daiwei, and Lin, Tao
    In Computer Vision and Pattern Recognition Conference (CVPR) 2024
  2. ICLR 2023
    Test-Time Robust Personalization for Federated Learning
    In International Conference on Learning Representations (ICLR) 2023
  3. ICML 2021
    Quasi-global Momentum: Accelerating Decentralized Deep Learning on Heterogeneous Data
    In International Conference on Machine Learning (ICML) 2021
  4. ICLR 2020
    Decentralized Deep Learning with Arbitrary Communication Compression
    In International Conference on Learning Representations (ICLR) 2020
  5. ICLR 2020
    Don’t Use Large Mini-batches, Use Local SGD
    In International Conference on Learning Representations (ICLR) 2020