CONTACT

  • Assistant Professor
  • Department of Statistics and Applied Probability
  • National University of Singapore
  • Email: statsll@nus.edu.sg
  • Office: S16-07-112
  • Tel: 65168473

RESEARCH INTERESTS

  • Bayesian inference
  • Variational approximation
  • Hierarchical models
  • Mixture models
  • Stochastic optimization
  • Network modeling

PUBLICATIONS

  1. Nott, D. J., Tan, S. L., Villani, M. and Kohn, R. (2012). Regression density estimation with variational methods and stochastic approximation. Journal of Computational and Graphical Statistics, 21, 797–820 (Journal link)
  2. Tan, L. S. L. and Nott, D. J. (2013). Variational inference for generalized linear mixed models using partially non-centered parametrizations. Statistical Science, 28, 168–188 (Journal link, arXiv)
  3. Tan, S. L. and Nott, D. J. (2014). Variational approximation for mixtures of linear mixed models. Journal of Computational and Graphical Statistics, 23, 564–585 (Journal link, arXiv)
  4. Tan, L. S. L. and Nott, D. J. (2014). A stochastic variational framework for fitting and diagnosing generalized linear mixed models. Bayesian Analysis, 9, 963–1004 (Journal link, arXiv)
  5. Tan, L. S. L., Ong, V. M. H., Nott, D. J. and Jasra, A. (2016). Variational inference for sparse spectrum Gaussian process regression. Statistics and Computing, 26, 1243–1261. (journal link, arXiv)
  6. Tan, L. S. L., Chan, A. H. and Zheng, T. (2016). Topic-adjusted visibility metric for scientific articles. Annals of Applied Statistics, 10, 1–31  (Journal link, arXiv)
  7. Mensah, D. K., Nott, D. J., Tan, L. S. L. and Marshall, L. (2016). Functional longitudinal modeling with covariate dependent smoothness. Electronic Journal of Statistics, 10, 527–549 (Journal link)
  8. Tan, L. S. L. (2017). Stochastic variational inference for large-scale discrete choice models with adaptive batch sizes. Statistics and Computing, 27, 237–257.  (Journal link, arXiv)
  9. Tan, L. S. L., Jasra, A., De Iorio, M. and Ebbels, T. M. D. (2017). Bayesian inference for multiple Gaussian graphical models with application to metabolic association networks. Annals of Applied Statistics, 11, 2222–2251. (arXiv, journal link, pdf)
  10. Tan, L. S. L. and Nott, D. J. (2018). Gaussian variational approximation with sparse precision matrices. Statistics and Computing, 28, 259-275. (journal link, arXiv:1605.05622)
  11. Tan, L. S. L. and De Iorio, M. (2019). Dynamic degree-corrected blockmodels for social networks: a nonparametric approach. Statistical Modelling, 19, 386-411. (journal link, arXiv:1705.09088)
  12. Tan, L. S. L. (2019). Explicit inverse of tridiagonal matrix with applications in autoregressive modeling. IMA Journal of Applied Mathematics, 84, 679-695. (journal link, arXiv:1803.04516)
  13. Tan, L. S. L., Bhaskaran, A. and Nott, D. J. (2020). Conditionally structured Gaussian variational approximation with importance weights. Statistics and Computing, 30, 1255–1272. (journal link, arXiv:1904.09591)
  14. Tan, L. S. L. and Friel, N. (2020). Bayesian variational inference for exponential random graph models. Journal of Computational and Graphical Statistics, 29, 910-928. (journal link, arXiv:1811.04249)
  15. Tan, L. S. L. (2021). Use of model reparametrization to improve variational Bayes. Journal of the Royal Statistical Society: Series B (Statistical Methodology, 83, 30-57. (journal link, arXiv:1805.07267)

 MANUSCRIPTS

  1. Tan, L. S. L. (2021). Efficient data augmentation techniques for state space models. (arXiv:1712.08887)
  2. Tan, L. S. L. (2021). Analytic natural gradient updates for Cholesky factor in Gaussian variational approximation. (arXiv:2109.00375, pdf)