[Google Scholar] [ORCID]

Research Interests

Scalable Bayesian methods, Gaussian process, Bayesian nonparametrics, Spatial statistics, Quasi-Bayesian inference, Stochastic simulation, Machine learning.

Publications

* denotes alphabetical order; _ denotes PhD students, research assistants, or research fellows.

• Zhu, Y., Peruzzi, M., Li, C., and Dunson, D. B. (2024+) Radial neighbors for provably accurate scalable approximations of Gaussian processes.
  Biometrika. To appear. [arXiv:2211.14692][code]
• Zhai, Q., Ye, Z., Li, C., Revie, M., and Dunson, D. B. (2024+) Modeling recurrent failures on large directed networks.
  Journal of the American Statistical Association. To appear. [pdf][supplement]
• Li, C., Sun, S., and Zhu, Y. (2024) Fixed-domain posterior contraction rates for spatial Gaussian process model with nugget.
  Journal of the American Statistical Association 119 (546): 1336-1347. [arXiv:2207.10239][slides]
• Su, J., Yao, Z., Li, C., and Zhang, Y. (2023) A statistical approach to estimating adsorption-isotherm parameters in gradient-elution preparative liquid chromatography.
  Annals of Applied Statistics 17 (4): 3476-3499. [arXiv:2201.00958]
• Zhu, Y., Li, C., and Dunson, D. B. (2023) Classification trees for imbalanced data: Surface-to-volume regularization.
  Journal of the American Statistical Association 118 (543): 1707-1717. [arXiv:2004.12293] [code]
• *Guhaniyogi, R., Li, C., Savitsky, T. D., and Srivastava, S. (2023) Distributed Bayesian inference in massive spatial data.
  Statistical Science 38 (2): 262-284. [arXiv:1712.09767][pdf][supplement]
• Li, C., Gao, S., and Du, J. (2023) Convergence analysis of stochastic kriging-assisted simulation with random covariates.
  INFORMS Journal on Computing 35 (2): 386-402. [arXiv:2211.13685][code]
• Li, C. (2022) Bayesian fixed-domain asymptotics for covariance parameters in a Gaussian process model.
  Annals of Statistics 50 (6): 3334-3363. [arXiv:2010.02126] [pdf][supplement]
• *Guhaniyogi, R., Li, C., Savitsky, T. D., and Srivastava, S. (2022) Distributed Bayesian varying coefficient modeling using a Gaussian process prior.
  Journal of Machine Learning Research 23 (84): 1-59. [arXiv:2006.00783]
• Xie, W., Wang, B., Li, C., Xie, D. and Auclair, J. (2022) Interpretable biomanufacturing process risk and sensitivity analyses for quality-by-design and stability control.
  Naval Research Logistics 69 (3): 461-483. [arXiv:1909.04261]
• Xie, W., Li, C., Wu, Y., and Zhang, P. (2021) A nonparametric Bayesian framework for uncertainty quantification in stochastic simulation.
  SIAM/ASA Journal on Uncertainty Quantification 9 (4): 1527-1552. [arXiv:1910.03766]
• Gao, S., Li, C. and Du, J. (2019) Rate analysis for offline simulation online application.
  Proceedings of the 2019 Winter Simulation Conference, 3468-3479.
• Li, C., Lin, L. and Dunson, D. B. (2019) On posterior consistency of tail index for Bayesian kernel mixture models.
  Bernoulli 25 (3): 1999-2028. [arXiv:1511.02775] [pdf]
• Jiang, W. and Li, C. (2019) On Bayesian oracle properties.
  Bayesian Analysis 14 (1): 235-260. [arXiv:1507.05723]
• Srivastava, S., Li, C. and Dunson, D. B. (2018) Scalable Bayes via barycenter in Wasserstein space.
  Journal of Machine Learning Research 19 (8): 1-35. [arXiv:1508.05880][code]
• Xie, W., Li, C. and Zhang, P. (2017) A factor-based Bayesian framework for risk analysis in stochastic simulations.
  ACM Transactions on Modeling and Computer Simulation (TOMACS) 27, article 27. [pdf]
• Li, C., Srivastava, S. and Dunson, D. B. (2017) Simple, scalable and accurate posterior interval estimation.
  Biometrika 104 (3): 665-680. [arXiv:1605.04029][code]
• Li, C., Jiang, W. (2016) On oracle property and asymptotic validity of Bayesian generalized method of moments.
  Journal of Multivariate Analysis 145: 132-147. [arXiv:1405.6693]
• Xie, W., Li, C. and Sun, H. (2015) Quantifying statistical uncertainty for dependent input models with factor structure.
  Proceedings of the 2015 Winter Simulation Conference, 667-678. [pdf]
• Li, C., Jiang, W. and Tanner, M. A. (2014) General inequalities for Gibbs posterior with nonadditive empirical risk.
  Econometric Theory 30 (6): 1247-1271. [pdf]
• Li, C., Jiang, W. and Tanner, M. A. (2013) General oracle inequalities for Gibbs posterior with application to ranking.
  Proceedings of the 26th Annual Conference on Learning Theory (COLT), PMLR 30: 512-521.
• Li, C. (2013) Little’s test of missing completely at random.
  The Stata Journal 13 (4): 795-809. [pdf]

Manuscripts

• Cheng, Y. and Li, C. Enhancing scalability in Bayesian nonparametric factor analysis of spatiotemporal data. [arXiv:2312.05802][code]
• Zhang, X. and Li, C. Pigeonhole stochastic gradient Langevin dynamics for large crossed mixed effects models. [arXiv:2212.09126]