My research is supported in part by National Natural Science Foundation of China and Science and Technology Commission of Shanghai Municipality.

Papers

* student under supervision; † equal contribution.

• Functional linear quantile regression on a two-dimensional domain
  Zhang, N., Liu, P., Kong, L., Jiang, B. and Huang, J. Z. (2024).
  Bernoulli, 30, 1800-1824.

• Bias-aware Boolean matrix factorization using disentangled representation learning
  Wang, X., Wang, J., Zhao, T., Wang, Y., Zhang, N., Zang, Y., Cao, S., Zhang, C. (2024).
  UAI 2024.

• Leverage classifier: another look at support vector machine
  Han, Y., Yu, J., Zhang, N., Meng, C., Ma, P., Zhong, W. and Zou, C. (2023+).
  Statistica Sinica, accepted.

• Contextual tensor decomposition by projected alternating least squares
  Zhang, N., Liu, Y.* and Yang, J.* (2023+).
  Communications in Statistics - Simulation and Computation, accepted.

• A joint estimation approach to sparse additive ordinary differential equations
  Zhang, N., Nanshan, M.* and Cao, J. (2022).
  Statistics and Computing, 32, 69.

• Density regression with conditional support points
  Chen, Y.* and Zhang, N. (2022).
  Technometrics, 64, 396-408.

• Dynamical modeling for non-Gaussian data with high-dimensional sparse ordinary differential equations
  Nanshan, M.*, Zhang, N., Xun, X. and Cao, J. (2022).
  Computational Statistics & Data Analysis, 173, 107483.

• Exponential family tensor completion with auxiliary information
  Yang, J.* and Zhang, N. (2020).
  Stat, 9, p.e296.

• Adaptive basis selection for exponential family smoothing splines with application in joint modeling of multiple sequencing samples.
  Ma, P.†, Zhang, N.†, Huang, J. Z. and Zhong, W. (2017).
  Statistica Sinica, 27, 1757-1777.

• Uncovering the spatio-temporal dynamics of memes in the presence of incomplete information
  Ge, H., and Caverlee, J., Zhang, N. and Squicciarini, A. (2016).
  Proceedings of the 25th ACM International on Conference on Information and Knowledge Management (CIKM), 1493-1502.

• Efficient computation of smoothing splines via adaptive basis sampling
  Ma, P., Huang, J. Z. and Zhang, N. (2015).
  Biometrika, 102, 631-645.