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:


News

Sep 22, 2023 One paper was accepted to NeurIPS 2023. Congratulations to Lin.
Jul 14, 2023 One paper was accepted to ICCV 2023. Congratulations to Zexi.
Jun 1, 2023 We have several open positions: 1-3 Ph.D. students (Fall 2024), and multiple research assistants/interns.
Apr 25, 2023 Three papers were accepted to ICML 2023. Congratulations to Yongxin, Hao, Yuejiang, and Zexi.

Selected publications

  1. ICML 2023
    On Pitfalls of Test-time Adaptation
    Zhao, Hao*, Liu, Yuejiang*, Alahi, Alexandre, and Lin, Tao†
    In International Conference on Machine Learning (ICML), abridged in ICLR Workshop on Trustworthy ML 2023 and ICLR Workshop on DG (spotlight) 2023
    PDF Code
  2. ICLR 2023
    Test-Time Robust Personalization for Federated Learning
    Jiang, Liangze*, and Lin, Tao*†
    In International Conference on Learning Representations (ICLR) 2023
    PDF Code
  3. ICML 2021
    Quasi-global Momentum: Accelerating Decentralized Deep Learning on Heterogeneous Data
    Lin, Tao, Karimireddy, Sai Praneeth, Stich, Sebastian U., and Jaggi, Martin
    In International Conference on Machine Learning (ICML) 2021
    PDF Code
  4. NeurIPS 2020
    Ensemble Distillation for Robust Model Fusion in Federated Learning
    Lin, Tao*†, Kong, Lingjing*, Stich, Sebastian U, and Jaggi, Martin
    In Advances in Neural Information Processing Systems (NeurIPS) 2020
    PDF Code
  5. ICLR 2020
    Decentralized Deep Learning with Arbitrary Communication Compression
    Koloskova, Anastasia*, Lin, Tao*, Stich, Sebastian U, and Jaggi, Martin
    In International Conference on Learning Representations (ICLR) 2020
    PDF Code
  6. ICLR 2020
    Don’t Use Large Mini-batches, Use Local SGD
    Lin, Tao, Stich, Sebastian U, Patel, Kumar Kshitij, and Jaggi, Martin
    In International Conference on Learning Representations (ICLR) 2020
    PDF Code