About me
I’m a second-year PhD student in Computer Science at Stanford University, affiliated with Stanford AI Lab and Stanford NLP group. I am fortunate to be advised by Tengyu Ma. My current research interests broadly lie in large language models, especially their optimization and adaptation.
Before Stanford, I was an undergraduate at Tsinghua University working with Mingsheng Long.
News
Check out Sophia, a new light-weight second-order optimizer which is 2x faster than Adam in language model pre-training!
Language Models and Self-supervised Learning
Sophia: A Scalable Stochastic Second-order Optimizer for Language Model Pre-training
Hong Liu, Zhiyuan Li, David Hall, Percy Liang, Tengyu Ma.
Arxiv 2305.14342. [Code] [Twitter]
Same Pre-training Loss, Better Downstream: Implicit Bias Matters for Language Models
Hong Liu, Sang Michael Xie, Zhiyuan Li, Tengyu Ma.
International Conference on Machine Learning (ICML), 2023, Oral. [Code] [Twitter]
Self-supervised Learning is More Robust to Dataset Imbalance
Hong Liu, Jeff Z. HaoChen, Adrien Gaidon, Tengyu Ma.
International Conference on Learning Representations (ICLR), 2022, Spotlight. [Code] [Twitter]
Domain Adaptation and Transfer Learning
Cycle Self-Training for Domain Adaptation
Hong Liu, Jianmin Wang, Mingsheng Long.
Neural Information Processing Systems (NeurIPS), 2021. [Code]
Learning to Adapt to Evolving Domains
Hong Liu, Mingsheng Long, Jianmin Wang, Yu Wang.
Neural Information Processing Systems (NeurIPS), 2020.
Meta-learning Transferable Representations with a Single Target Domain
Hong Liu, Jeff Z. HaoChen, Colin Wei, Tengyu Ma
Arxiv 2011.01418.
Towards Understanding the Transferability of Deep Representations
Hong Liu, Mingsheng Long, Jianmin Wang, Michael Jordan.
Arxiv 1909.12031.
Transferable adversarial training: A general approach to adapting deep classifiers
Hong Liu, Mingsheng Long, Jianmin Wang, Michael Jordan.
International Conference on Machine Learning (ICML), 2019, Long Talk. [Code]
Separate to Adapt: Open Set Domain Adaptation via Progressive Separation
Hong Liu, Zhangjie Cao, Mingsheng Long, Jianmin Wang, Qiang Yang.
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019. [Code]