Recent/Selected Publications
Zhu, Y. and Liu, R. (2023). Path following algorithms for -regularized M-estimation with approximation guarantee. Advances in Neural Information Processing Systems (NeurIPS). Accepted.
Zhu, Y., Shen, X., Jiang, H., and Wong, W. (2022). Collaborative multilabel classification. Journal of the American Statistical Association: Theory and Methods. Accepted.
Liu, R. and Zhu, Y. (2022). On the consistent estimation of Receiver Operating Characteristic (ROC) curve. Advances in Neural Information Processing Systems (NeurIPS). Accepted.
Zhu, Y. and Liu, R. (2021). An algorithmic view of L2 regularization and some path-following algorithms. Journal of Machine Learning Research. Accepted.
Luan, B., Lee, Y., and Zhu, Y. (2021). Predictive Model Degrees of Freedom in Linear Regression. arXiv:2106.15682.
Zhu, Y. (2020). A convex optimization formulation for multivariate linear regression.
Advances in Neural Information Processing Systems (NeurIPS).
Zhu, Y., Shen, X., Pan, W. (2020). On high-dimensional constrained maximum likelihood inference. Journal of American Statistical Association, 155(529), 217-230. [website]
Zhu, Y. and Li, L. (2018). Multiple matrix Gaussian graphs estimation. Journal of the Royal Statistical Society, Series B. [website]
Zhu, Y. (2017). An augmented ADMM algorithm with application to the generalized lasso problem. Journal of Computational and Graphical Statistics, 26(1), 195-204. [website]
Shen, X., Pan, W., Zhu, Y., and Zhou, H. (2013). On
constrained and regularized high-dimensional regression. The Annals of the Institute of Statistical Mathematics,1, 1-26. [website]
Shen, X., Pan, W., Zhu, Y. (2012). Likelihood-based selection and sharp parameter estimation. Journal of the American Statistical Association, 107(497), 223-232. [website]
See here for a full list of my publications.
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