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:
- We are running a research seminar on Deep Learning and Optimization.
News
| Jul 2, 2024 | Congratulations to Yuxuan on the acceptance of his paper on Multimodal benchmark in pathology at ECCV 2024. |
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| 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. |