Preprints
- Global Optimization with Parametric Function Approximation.
Chong Liu and Yu-Xiang Wang.
Manuscript. [ArXiv]
Appeared in ICML Workshop on Adaptive Experimental Design and Active Learning in the Real World (ReALML-2022), Baltimore, MD, 2022.
Conference Publications
- Human-in-the-Loop Video Semantic Segmentation Auto-Annotation.
Nan Qiao, Yuyin Sun, Chong Liu, Lu Xia, Jiajia Luo, Ke Zhang, and Cheng-Hao Kuo.
The 10th IEEE/CVF Winter Conference on Applications of Computer Vision (WACV-2023), Waikoloa, HI, 2023, pp. 5881-5891. [CVF]
- Revisiting Model-Agnostic Private Learning: Faster Rates and Active Learning.
Chong Liu, Yuqing Zhu, Kamalika Chaudhuri, and Yu-Xiang Wang.
The 24th International Conference on Artificial Intelligence and Statistics (AISTATS-2021), San Diego, CA, 2021, pp. 838-846. [PMLR]
- Dual Set Multi-Label Learning.
Chong Liu, Peng Zhao, Sheng-Jun Huang, Yuan Jiang, and Zhi-Hua Zhou.
The 32nd AAAI Conference on Artificial Intelligence (AAAI-2018), New Orleans, LA, 2018, pp. 3635-3642. [Oral presentation] [Paper] [Supp.] [Slides] [Code]
Journal Publications
- Doubly Robust Crowdsourcing.
Chong Liu and Yu-Xiang Wang.
Journal of Artificial Intelligence Research (JAIR), 73:209-229, 2022. [JAIR] [Code]
- Revisiting Model-Agnostic Private Learning: Faster Rates and Active Learning.
Chong Liu, Yuqing Zhu, Kamalika Chaudhuri, and Yu-Xiang Wang.
Journal of Machine Learning Research (JMLR), 22(262):1−44, 2021. [JMLR] [Code]
Workshop Papers
- Cost-Sensitive Experimental Design for Atomistic Modeling.
Chong Liu, Anirudh Raju Natarajan, Derick Evan Ober, Anton Van der Ven, and Yu-Xiang Wang.
ICML Workshop on Adaptive Experimental Design and Active Learning in the Real World (ReALML-2022), Baltimore, MD, 2022. [Paper]
- Model-Agnostic Private Learning with Domain Adaptation.
Yuqing Zhu, Chong Liu, and Yu-Xiang Wang.
CCS Workshop on Theory and Practice of Differential Privacy (TPDP-2020), Virtual Event, 2020.