Publications

Books

1. Bhattacharya, R., Lin, L., and Patrangenaru, V (2016). A Course in Mathematical Statistics and Large Sample Theory. Springer Series in Statistics.

Research Articles

2. Ohn, I.+ and Lin, L. (2024). Adaptive variational Bayes: optimality, computation and applications. arXiv:2109.03204. Annals of Statistics,  Vol. 52(1), 335–363

3. Fang, Y∗., Ohn, I., Gupta, V. and Lin, L. (2024). Intrinsic and extrinsic deep learning on manifold. arXiv: 2302.08606. Electronic Journal of Statistics, Vol. 18, No. 1, 1160–1184.

4. Shen, L∗., Amini, A., Josephs, N. and Lin, L. (2024). Bayesian community detection for networks with covariates arXiv:2203.02090. Bayesian Analysis. Accepted.

5. S Winter, T Campbell, L Lin, S Srivastava, DB Dunson (2024). Engaging directions in Bayesian computation. arXiv:2304.11251. Statistical Science 2024, Vol. 39, No. 1, 62-89.

6. Ohn, I.+, Lin, L. and Kim, Y. (2023). A Bayesian factor model with adaptive posterior contraction. Bayesian Statistics, accepted.

7. Lee, K., You, K, and Lin, L. (2023). Bayesian optimal two-sample tests in high-dimension. arXiv:2112.02580 Bayesian Analysis. accepted.

8. Amini, A., Paez., M.,and Lin, L. (2023). Hierarchical stochastic block model for community detection in multiplex networks. Bayesian Analysis. accepted.

9. Chae, M.+, Kim, D., Kim, Y. and Lin, L. (2023). A likelihood approach to nonparametric estimation of a singular distribution using deep generative models. Journal of the Machine Learning Research, vol 24, 1-42.

10. Lee, K.+ and Lin, L. (2023). Scalable Bayesian high-dimensional local department and dependence learning. Bayesian analysis. 18(1): 25-47.

11. Ohn, I.+ and Lin, L. (2023). Optimal Bayesian estimation of Gaussian mixtures with growing number of components. Bernoulli. Vol. 29 (2), pp. 1195-1218

12. Josephs, N.∗, Lin, L., Rosenberg, S. and Kolaczyk, E. (2023). Bayesian classification, anomaly detection, and survival analysis using network inputs with application to the microbiome. Arxiv:2004.04765. Annals of Applied Statistics. vol 17 (1), 199-224.

13. Chen, L.∗, Zhou, J. and Lin, L. (2023). Hypothesis testing for population of networks. Communication in Statistics-Theory and Methods. vol 52 (11), 3661-3684. arXiv:1911.03783

14. Y Fang ∗, M Niu, P Cheung, L Lin (2023). Extrinsic Bayesian Optimization on Manifolds. Algorithms vol. 16 (2), 117

15. Lin, L., Lazar, D., Saparbayeva, B., and Dunson, D. B. (2022). Robust optimization and inference on manifolds. Statistics Sinica, accepted.

16. Chen, L.∗, Josephs, N., Lin, L., Zhou, J. and Kolaczyk, E. (2024). A spectral-based framework for hypothesis testing in populations of networks arXiv:2011.12416. Statistics Sinica vol 34, 87-110.

17. Jin, I., Jeon, M., Schweinberger, M. and Lin, L. (2022). Hierarchical network item response modeling for discovering differences between innovation and regular school systems in Korea. Arxiv:1810.07876. Journal of the Statistical Royal Society, ser. C. vol 71 (5), 1225–1244.

18. Lee, K.+, Lin, L., and Dunson, D. (2021). Maximum pairwise Bayes factors for covariance structure testing. Electronic Journal of Statistics. 15(2): 4384–4419.

19. Thomas, B., You, K., Lin, L.#, Lim, L., and Mukherjee, S (2021). Learning subspaces of different dimensions. Journal of the Computational and Graphical Statistics. DOI:

10.1080/10618600.2021.2000420. Arxiv:1404.6841. #-corresponding.

20. Hong, M, Lin, L. and Chen, Y. (2021). Asymptotically corrected person fit statistics for multidimensional constructs with simple structure and mixed item types. PsyArXiv, 30 Apr.2020. Psychometrika. Vol 86, 464–488.

21. Lee, K.+, Chae, M.+, and Lin, L. (2021). Bayesian high-dimensional semi-parametric inference beyond sub-Gaussian errors. Journal of the Korea Statistical Society, Vol 50, pp 511–527 .

22. Kolaczyk, E., Lin, L., Rosenberg, S., Xu., J and Jackson, W. (2020). Averages of unlabeled networks: geometric characterization and asymptotic behavior. Annals of Statistics,, Vol. 48, No. 1, 514–538.

23. Lee, K.+, and Lin, L. (2020). Bayesian bandwidth test and selection for high-dimensional banded precision matrices. Bayesian Analysis, Vol 15, No. 3 737–758.

24. Lee, K+., Lee, J. and Lin, L. (2019). Minimax posterior convergence rates and model selection consistency in high-dimensional DAG models based on sparse Cholesky factors. Annals of Statistics 2019, Vol. 47, No. 6, 3413–3437.

25. Niu, M.,Cheung, P., Lin, L.#, Dai, Z., Lawrence, N. and Dunson, D. B. (2019). Intrinsic Gaussian processes on complex constrained domains. Journal of the Royal Statistical Society, Ser. B. 81: 603– 627. #-corresponding.

26. Bhattacharya, R. and Lin, L. (2019). Differential geometry for model independent analysis of images and other non-Euclidean data: recent developments. In: Sidoravicius V. (eds) Sojourns in Probability Theory and Statistical Physics – II. Springer Proceedings in Mathematics & Statistics, vol 299. Springer.

27. Chae, M.+, Lin, L and Dunson, D.B. (2019) Bayesian sparse linear models with unknown symmetric errors. Information and Inference. vol 8 (3), 621–653.

28. Lin, L., Niu, M., Pokman, C. and Dunson. D.B. (2019). Extrinsic Gaussian process models for regression and classification on manifolds. Bayesian Analysis. vol.14, 907–926. Arxiv:1706.08757

29. Li, C., Lin, L. and Dunson, D. B. (2019). On posterior consistency of tail index for Bayesian kernel mixture models. Bernoulli, Vol. 25, No. 3, 1999–2028.

30. Sarpavayeva, B.+, Zhang, M.∗ and Lin, L. (2018). Communication efficient parallel algorithms for optimization on manifolds. Neural Information Processing Systems 2018.

31. Zhang, M.∗, Lam, H. and Lin, L. (2018). Robust and scalable Bayesian model selection. Computational Statistics & Data Analysis, Vol. 127, 229–247.

32. Lin, L., Thomas, B.∗, Zhu, H. and Dunson, D.B (2017). Extrinsic local regression on manifold-valued data. Journal of the American Statistical Association-Theory and Methods. 112(519), 1261-1273.

33. Bhattacharya, R. and Lin, L. (2017). Omnibus CLTs for Fr´echet means and nonparametric inference on non-Euclidean spaces. Proceedings of American Mathematical Society. Vol. 145, 413-428.

34. Minsker, S., Srivastava, S., Lin, L. and Dunson, D.B. (2017). Robust and scalable Bayes via a median of subset posterior measure. Journal of Machine Learning Research, 18(124):1–40.

35. Mukherjee, S. S., Sarkar, P., and Lin, L. (2017). On clustering network-valued data. Neural Information Processing Systems 2017.

36. Lin, L., Rao, V., and Dunson, D.B (2017). Bayesian nonparametric inference on Stiefel manifold. Statistics Sinica 27, 535–553.

37. Lazar, D. and Lin, L. (2017). Scale and curvature effects in principal geodesic analysis. Journal of the Multivariate Analysis 153, 64–82.

38. Borg, J.S., Lin, L. et al. (2017) Rat intersubjective decisions are encoded by frequency-specific oscillatory contexts. Brain and Behavior 7: e00710. DOI: 10.1002/brb3.710.

39. Hultman, R., Mague, S.D., Li, Q., Katz, B.M., Michel, N., Lin, L., et.al (2016). Dysregulation of cortical-mediated slow evolving limbic dynamics drives stress-induced emotional pathology. Neuron 91(2),439–452.

40. Rao, V., Lin, L., and Dunson, D.B (2016). Data augmentation for models based on rejection sampling. Biometrika 103 (2): 319–335.

41. Li, D., Wang X., Lin, L and Dey, D.(2016). Flexible link functions in nonparametric binary regression with Gaussian process priors. Biometrics 72, 707–719.

42. Lin, L, Piegorsch, W., and Bhattacharya, R. (2015). Nonparametric benchmark dose estimation with continuous dose-response data. Scandinavian Journal of Statistics 42, 713–731.

43. Lin, L. and Dunson, D. B. (2014). Bayesian monotone regression using Gaussian process projection. Biometrika, 101 (2): 303–317.

44. Piegorsch, W., Xiong, H, Bhattacharya, R., and Lin, L. (2014). Benchmark dose analysis via nonparametric regression modeling. Risk Analysis 34(1), 135–151.

45. Minsker, S., Srivastava, S., Lin, L., and Dunson, D.B. (2014) Scalable and robust Bayesian inference via the median posterior. ICML 2014.

46. Bhattacharya, R. and Lin, L. (2013). Recent progress in the nonparametric estimation of monotone curves -with applications to bioassay and environmental risk assessment. Computational Statistics & Data Analysis, 63, 63–80.

47. Bhattacharya, R., Majumdar, M., and Lin, L. (2013). Problem of ruin and survival in economics: application of limit theorems in probability. Sankhy¯a, Ser.B 75(2), 145–180

48. Piegorsch, W., Xiong, H., Bhattacharya, R., and Lin, L. (2012). Nonparametric estimation of benchmark doses in environmental risk assessment. Environmetrics 23 (8), 717–728.

49. Bhattacharya, R. and Lin, L. (2011). Nonparametric benchmark analysis in risk assessment: a comparative study by simulation and data analysis. Sankhy¯a, Ser.B 73(1), 144-163.

50. Bhattacharya, R. and Lin, L. (2010). An adaptive nonparametric method in benchmark analysis for bioassay and environmental Studies. Stat & Probab. Lett 80, 1947-1953.

Topological Data Analysis, Graph Neural Networks

51. Bao, D.+, You, K.∗ and Lin, L. (2022). Network distance based Laplacian flow on graphs. Arxiv:1810.02906. IEEE BigData 2022, 715-720.

52. Hu, Y., Zhao, T., Xu, S., Lin, L. and Xu, Z. (2022). Neural-PDE: a RNN based neural network for solving time dependent PDEs. Communications in Information and Systems. Volume 22 (2), 223–245.

53. Nguyen, D., Lin, X., Le, P. and Lin, L. (2022). A graph-theoretical approach to DNA similarity analysis. BioArxiv. Communications in Information and Systems (CIS), Volume 22 (3), 383–400.

54. Nguyen, D., Le, P., Lin, X. and Lin, L. (2022). A topological characterization of DNA sequences based on chaos geometry and persistent homology. BioArxiv. IEEE CSCI 2022..

55. Nguyen, D., Le, P., Hu, Z. and Lin, L. (2021). A topological approach to DNA similarity analysis from 5-dimensional representation. BioArxiv. Submitted.

56. Hu, Z., Fang, Y., and Lin, L. (2021). Training graph neural networks via graphon estimation (2021). Arxiv2109.01918. IEEE BigData 2021.

57. Nguyen, D., Lin, X. and Lin, L. (2020). Community detection, pattern recognition, and hypergraph based learning: approaches using metric geometry and persistent homology. Fuzzy Systems and Data Mining VI 457.

58. Izadi, M.∗, Fang, Y., Stevenson, R. and Lin, L. (2020). Optimization of Graph Neural Networks with Natural Gradient Descent. arXiv:2009.09624. IEEE BigData 2020.

Research Articles Under Review

59. Josephs, N., Amini, A., Paez, M. and Lin, L. (2023). Nested stochastic block model for simultaneously clustering networks and nodes. arXiv:2307.09210. Under review.

60. Fazeli-Asl, F., Zhang, M. and Lin, L. (2023). A Semi-Bayesian Nonparametric Hypothesis Test Using Maximum Mean Discrepancy with Applications in Generative Adversarial Networks. arXiv:2303.02637.  TMLR, accepted.

61. Lin, L., Sarpabayeva, B., Zhang, M. and Dunson, D. (2020). Accelerated algorithms for convex and non-convex optimizations on manifolds. Machine Learning. arXiv:2010.08908. Machine Learning, accepted.