(Romy) Mi LUO
University of Texas at Austin
romyluo7 (at) gmail.com
I am a PhD student at UT Austin, advised by Prof. Kristen Grauman and Prof. Alex Dimakis. My research lies in Machine Learning and Computer Vision, specifically in the following topics:
- First-person "egocentric" computer vision.
- Designing new architectures for real-world machine learning system.
- Learning generalized and transferrable representations (Self-supervised Learning & Life-long Learning).
- Learning with heterogenous/imbalanced data.
- Model inversion and gradient leakage.
- MetaFormer Baselines for Vision
Weihao Yu, Chenyang Si, Pan Zhou, Mi Luo, Yichen Zhou, Jiashi Feng, Shuicheng Yan & Xinchao Wang.
Technical Report, 2022.
- MetaFormer is Actually What You Need for Vision
Weihao Yu, Mi Luo, Pan Zhou, Chenyang Si, Yichen Zhou, Xinchao Wang, Jiashi Feng & Shuicheng Yan.
In IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022. (Oral)
- Architecture Personalization in Resource-constrained Federated Learning
Mi Luo, Fei Chen, Zhenguo Li & Jiashi Feng.
In NFFL Workshop, NeurIPS 2021. (Selected as outstanding paper, acceptance rate: 9%)
- No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Data
Mi Luo, Fei Chen, Dapeng Hu, Yifan Zhang, Jian Liang & Jiashi Feng.
In Advances in Neural Information Processing Systems, NeurIPS 2021.
- MetaSelector: Meta-Learning for Recommendation with User-Level Adaptive Model Selection
Mi Luo, Fei Chen, Pengxiang Cheng, Zhenhua Dong, Xiuqiang He, Jiashi Feng & Zhenguo Li.
In Proceedings of The Web Conference, WWW 2020.
[PDF] [Oral Presentation]
- Federated Meta-Learning with Fast Convergence and Efficient Communication
Fei Chen, Mi Luo, Zhenhua Dong, Zhenguo Li & Xiuqiang He.
Technical Report, 2019.
- Conference Reviewer: AISTATS, ECCV, CVPR.
- Teaching Assistant: EE2211 (Introduction to Machine Learning), CG3207 (Computer Architecture).