2025
- Bayesian Optimization with Inexact Acquisition: Is Random Grid Search Sufficient?
Hwanwoo Kim, Chong Liu, Yuxin Chen
The 41th Conference on Uncertainty in Artificial Intelligence (UAI-2025), Rio de Janeiro, Brazil, 2025, pp. 2202-2222. [PMLR] [ArXiv] [Code]
- Quantum Non-Linear Bandit Optimization
Zakaria Shams Siam, Chaowen Guan, Chong Liu
ArXiv Preprint: 2503.03023. [ArXiv]
- Constrained Multi-Objective Bayesian Optimization through Optimistic Constraints Estimation
Diantong Li, Fengxue Zhang, Chong Liu, Yuxin Chen
The 28th International Conference on Artificial Intelligence and Statistics (AISTATS-2025), Mai Khao, Thailand, 2025, pp. 370-378. [PMLR] [ArXiv] [Code]
- No-Regret Linear Bandits under Gap-Adjusted Misspecification
Chong Liu, Dan Qiao, Ming Yin, Ilija Bogunovic, Yu-Xiang Wang
ArXiv Preprint: 2501.05361. [ArXiv]
- Black-Box Optimization with Implicit Constraints for Public Policy
Wenqian Xing, Jungho Lee, Chong Liu, Shixiang Zhu
The 39th AAAI Conference on Artificial Intelligence (AAAI-2025) AI for Social Impact Track, Philadelphia, PA, 2025, pp. 28511-28519. [Oral Presentation] [AAAI] [ArXiv] [Code]
2024
- Binding Affinity Prediction: From Conventional to Machine Learning-Based Approaches
Xuefeng Liu, Songhao Jiang, Xiaotian Duan, Archit Vasan, Chong Liu, Chih-chan Tien, Heng Ma, Thomas Brettin, Fangfang Xia, Ian T. Foster, Rick L. Stevens
ArXiv Preprint: 2410.00709. [ArXiv]
- Communication-Efficient Federated Non-Linear Bandit Optimization
Chuanhao Li *, Chong Liu *, Yu-Xiang Wang (* equal contribution)
The 12th International Conference on Learning Representations (ICLR-2024), Vienna, Austria, 2024, pp. 1-23. [OpenReview] [ArXiv]
2023
- Adaptive Sequential Decision Making: Bandit Optimization and Active Learning
Chong Liu
PhD Thesis. University of California, Santa Barbara, 2023. [PDF]
- No-Regret Linear Bandits beyond Realizability
Chong Liu, Ming Yin, Yu-Xiang Wang
The 39th Conference on Uncertainty in Artificial Intelligence (UAI-2023), Pittsburgh, PA, 2023, pp. 1294-1303. [PMLR] [ArXiv]
- Global Optimization with Parametric Function Approximation
Chong Liu, Yu-Xiang Wang
The 40th International Conference on Machine Learning (ICML-2023), Honolulu, HI, 2023, pp. 22113-22136. [PMLR] [ArXiv] [Code]
- Human-in-the-Loop Video Semantic Segmentation Auto-Annotation
Nan Qiao, Yuyin Sun, Chong Liu, Lu Xia, Jiajia Luo, Ke Zhang, Cheng-Hao Kuo
The 10th IEEE/CVF Winter Conference on Applications of Computer Vision (WACV-2023), Waikoloa, HI, 2023, pp. 5881-5891. [CVF]
2022
- Cost-Sensitive Experimental Design for Atomistic Modeling
Chong Liu, Anirudh Raju Natarajan, Derick Evan Ober, Anton Van der Ven, Yu-Xiang Wang
ICML 2022 Workshop on Adaptive Experimental Design and Active Learning in the Real World (ReALML-2022), Baltimore, MD, 2022. [Paper]
- Doubly Robust Crowdsourcing
Chong Liu, Yu-Xiang Wang
Journal of Artificial Intelligence Research (JAIR), 73:209-229, 2022. [JAIR] [ArXiv] [Code]
2021
- Revisiting Model-Agnostic Private Learning: Faster Rates and Active Learning
Chong Liu, Yuqing Zhu, Kamalika Chaudhuri, Yu-Xiang Wang
Journal of Machine Learning Research (JMLR), 22(262):1−44, 2021. [JMLR] [ArXiv] [Code]
- Revisiting Model-Agnostic Private Learning: Faster Rates and Active Learning
Chong Liu, Yuqing Zhu, Kamalika Chaudhuri, Yu-Xiang Wang
The 24th International Conference on Artificial Intelligence and Statistics (AISTATS-2021), San Diego, CA, 2021, pp. 838-846. [PMLR]
2020
- Model-Agnostic Private Learning with Domain Adaptation
Yuqing Zhu, Chong Liu, Yu-Xiang Wang
CCS 2020 Workshop on Theory and Practice of Differential Privacy (TPDP-2020), Orlando, FL, 2020.
2018
- Dual Set Multi-Label Learning
Chong Liu, Peng Zhao, Sheng-Jun Huang, Yuan Jiang, 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]