I am currently a postdoctoral scholar at Department of Computational Medicine, UCLA, working with Dr. Kenneth Lange and Dr. Hua Zhou. I received my Ph.D. from North Carolina State University, where I primarily worked with Dr. Eric C. Chi (currently at Rice) on numerical optimization in statistical learning problems.
📝 Preprints & In Preparation
Minimum Covariance Determinant: Spectral Embedding and Subset Size Determination
Qiang Heng, Hui Shen, Kenneth Lange
Submitted
- A spectral approach to the minimum covariance determinant problem, coupled with a novel bootstrap procedure for estimating the number of inliers/outliers. [arXiv]
Bootstrap Estimation of the Proportion of Outliers in Robust Regression Models
Qiang Heng, Kenneth Lange
Working paper
Anderson Accelerated Operator Splitting Methods for Convex-nonconvex Regularized Problems
Qiang Heng, Xiaoqian Liu, Shiqian Ma, Eric C. Chi
To be submitted
- Globally convergent type-II Anderson acceleration for several common splitting methods, with application in convex-nonconvex regularization.
📝 Publications With Primary Role
Bayesian Trend Filtering via Proximal Markov Chain Monte Carlo
Qiang Heng, Hua Zhou, Eric C. Chi
Journal of Computational and Graphical Statistics, 2023
📝 Publications with Supporting Role
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FreeMatch: Self-adaptive Thresholding for Semi-supervised Learning [arXiv] Yidong Wang, Hao Chen, Qiang Heng, Wenxin Hou, Yue Fan, Zhen Wu, Jindong Wang, Marios Savvides, Takahiro Shinozaki, Bhiksha Raj, Bernt Schiele, Xing Xie. The Eleventh International Conference on Learning Representations (ICLR 2023).
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Proximal MCMC for Bayesian Inference of Constrained and Regularized Estimation [arXiv] Xinkai Zhou, Qiang Heng, Eric C. Chi, Hua Zhou. The American Statistician (accepted, 2024+).
📖 Educations
- 2019.08 - 2023.12, Ph.D. Statistics, North Carolina State University.
- 2015.09 - 2019.06, B.S. Statistics, Shanghai University of Finance and Economics.
💬 Presentations
- 2024.04 Nanjing Audit University, Minimum Covariance Determinant: Spectral Embedding and Subset Size Determination, Invited Talk, Seminar
- 2024.03 McGill University, Minimum Covariance Determinant: Spectral Embedding and Subset Size Determination, Invited Talk, Statistics Seminar
- 2023.07 Brown University, Anderson Accelerated Operator Splitting for Convex-nonconvex Regularization, Poster Presentation, Acceleration and Extrapolation Methods workshop at ICERM
- 2022.07 Lehigh University, Bayesian Trend Filtering via Proximal Markov Chain Monte Carlo, Invited Talk, ICCOPT 2022