I'm currently a first-year PhD student at CMU ECE. I'm interested in robustness and AI security.
Previously I'm a master student at Institute of Automation, Chinese Academy of Sciences , working with Prof. Liang Wang and Prof. Yan Huang .
Before this, I recieved my bachelor's degree from University of Chinese Academy of Sciences (UCAS) . I also had a good time at the EECS, University of California, Berkeley .
If you are also fancinated in robustness / privacy attack / defense and are open to collaboration, just email me at wyu3@andrew.cmu.edu!
Bag of Tricks for Training Data Extraction from Language Models.
Weichen Yu , Tianyu Pang, Qian Liu, Chao Du, Bingyi Kang, Yan Huang, Min Lin, Shuicheng Yan.
International Conference on Machine Learning (ICML), 2023.
[paper ]
[project ]
Generalized Inter-class Loss for Gait Recognition.
Weichen Yu , Hongyuan Yu, Yan Huang, Liang Wang.
CNTN: Cyclic Noise-Tolerant Network.
Weichen Yu , Hongyuan Yu, Yan Huang, Chunshui Cao, Liang Wang.
Regularized Graph Structure Learning with Semantic Knowledge for Multi-variates Time-Series Forecasting.
Hongyuan Yu*, Ting Li *, Weichen Yu* , Jianguo Li, Yan Huang, Liang Wang, Alex Liu.
International Joint Conference on Artificial Intelligence (IJCAI), 2022. (Oral)
[paper ]
[project ]
Deconfounded Noisy Labels Learning.
Weichen Yu , Hongyuan Yu, Yan Huang, Jianghao Zhang, Qiang Liu, Liang Wang.
(under review)
1st Learning and Mining with Noisy Labels Challenge, IJCAI-ECAI 2022.
runner-up of task 1-1 and
2nd runner-up of task 1-2.
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details ]
Weichen Yu , Hongyuan Yu, Yan Huang, Dong An,
Keji He, Zhipeng Zhang, Xiuchuan Li, Liang Wang
REVERIE Challenge 2022.
Dong An, Yifeng Su, Shuanglin Sima, Hongyuan Yu, Weichen Yu , Yan Huang
Training Data Extraction Challenge
Weichen Yu , Tianyu Pang, Qian Liu, Chao Du, Bingyi Kang, Yan Huang, Min Lin, Shuicheng Yan.
Undergraduate Research
2016-2020
SiC MOSFET in electromobile controlling
Genetic Algorithm Based SiC MOSFET On-state Resistance Modeling Method.
Han C, Weichen Y , Xiaoguang C, Puqi N, Xuhui W.
Journal of Power Supply, 2020, 18(04): 38-44. DOI:10.13234/j.issn.2095-2805.2020.4.38
[
paper ]
Power supply noise analysis using autocorrelation
- developed better algorithms which combined autocorrelation with averaging on Simulink to analyze power supply noise and achieve high resolution (1 mV) at high frequency (30GHz).
- Significantly reduced the cost from 500$ to 20$ and improved the performance and resolution by accumulating small steps of advancement such as the step of float-point to fixed-point optimization.
2020/09 - 2023/07 : Master student in CS, Institute of Automation, Chinese Academy of Sciences.
2016/09 - 2020/07 : Bachelor in EE, University of Chinese Academy of Sciences.
2019/01 - 2019/08 : Visiting student in EECS, University of California, Berkeley.