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, Qinan Huang, Chong Liu, Michelle M. Li, Heng Ma, Thomas Brettin, Arvind Ramanathan, Fangfang Xia, Mengdi Wang, Abhishek Pandey, Marinka Zitnik, Ian T. Foster, Jinbo Xu, 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]