Welcome to my homepage! I'm Chong Liu (刘冲), a Ph.D. Candidate in Computer Science advised by Prof. Yu-Xiang Wang at University of California, Santa Barbara. In 2018, I received my M.S. in Computer Science advised by Prof. Yuan Jiang in LAMDA Group, Nanjing University, Nanjing, China. In 2015, I received my B.E. in Electrical Engineering from Northwestern Polytechnical University, Xi'an, China. From Aug. 2014 to Feb. 2015, I spent six months visiting Hanyang University, Ansan, South Korea. I'm an editorial board reviewer of the Journal of Machine Learning Research (JMLR). chongliu [at] cs.ucsb.edu

[Experiences] [Blogs] [Exploring the World]

News

• Jun. 2022: Two papers accepted by the ICML Workshop on Adaptive Experimental Design and Active Learning in the Real World Workshop (ReALML-2022)! See you in Baltimore, MD in July!

• Apr. 2022: Happy to be invited to speak at RPI CS Theory Seminar!

• Jan. 2022: Paper "Doubly Robust Crowdsourcing" has been published in the Journal of Artificial Intelligence Research (JAIR)! In this paper, crowdsourcing test-time label aggregation is formulated as a statistical estimation problem of inferring the expected voting score. Doubly robust estimator is used to improve crowdsourcing accuracy and save labeling cost at the same time.

• Nov. 2021: Paper "Revisiting Model-Agnostic Private Learning: Faster Rates and Active Learning" has been published in the Journal of Machine Learning Research (JMLR)! In this paper, active learning-based algorithm PATE-ASQ is able work in practice while saving privacy budget at the same time under the differential privacy framework.

Research Interests

My research interests include global optimization, active learning, and ensemble learning, as well as their applications in hyperparameter tuning and material design.

Publications

• Doubly Robust Crowdsourcing.
  Chong Liu and Yu-Xiang Wang.
  [J-2] 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.
  [J-1] Journal of Machine Learning Research (JMLR), 22(262):1−44, 2021. [JMLR] [Code]
  [C-2] The 24th International Conference on Artificial Intelligence and Statistics (AISTATS-2021), San Diego, California, USA, 2021, page 838-846. [PMLR]

• Dual Set Multi-Label Learning.
  Chong Liu, Peng Zhao, Sheng-Jun Huang, Yuan Jiang, and Zhi-Hua Zhou.
  [C-1] The 32nd AAAI Conference on Artificial Intelligence (AAAI-2018), New Orleans, Louisiana, USA, 2018, page 3635-3642. [Paper] [Supp.] [Slides] [Code]

Workshop Papers

• Global Optimization with Parametric Function Approximation.
  Chong Liu and Yu-Xiang Wang.
  [W-3] ICML Workshop on Adaptive Experimental Design and Active Learning in the Real World (ReALML-2022), Baltimore, MD, 2022.

• Cost-Sensitive Experimental Design for Atomistic Modeling.
  Chong Liu, Anirudh Raju Natarajan, Derick Evan Ober, Anton Van der Ven, and Yu-Xiang Wang.
  [W-2] ICML Workshop on Adaptive Experimental Design and Active Learning in the Real World (ReALML-2022), Baltimore, MD, 2022.

• Model-Agnostic Private Learning with Domain Adaptation.
  Yuqing Zhu, Chong Liu, and Yu-Xiang Wang.
  [W-1] CCS Theory and Practice of Differential Privacy Workshop (TPDP-2020).

Invited Talk

• Doubly Robust Crowdsourcing and Beyond.
  RPI CS Theory Seminar, Rensselaer Polytechnic Institute, Apr. 2022.

Contact

Chong Liu
2118 Henley Hall
University of California, Santa Barbara
Santa Barbara, CA 93106