Google Scholar site

Books:

  1. Zhou, Q. (2025). Latent Structure and Causality: Inference from Data, World Scientific.

Papers:

  1. Zhao, Y. and Zhou, Q. (2025). Causal bandits with backdoor adjustment on unknown Gaussian DAGs. arXiv Preprint, 2502.02020. [Preprint].
  2. Chen, A. and Zhou, Q. (2025). Causal discovery on dependent binary data. Artificial Intelligence and Statistics (AISTATS), accepted. [Preprint].
  3. Smith, S. and Zhou, Q. (2024). Coordinated multi-neighborhood learning on a directed acyclic graph. arXiv Preprint, 2405.15358. [Preprint].
  4. Li, H., Madrid Padilla, O.H., and Zhou, Q. (2024). Learning Gaussian DAGs from network data. Journal of Machine Learning Research, 25 (377): 1-52. [Reprint].
  5. Ye, Q., Amini, A.A., and Zhou, Q. (2024). Federated learning of generalized linear causal networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 46: 6623-6636. [Reprint].
  6. Kim, D.S. and Zhou, Q. (2023). Structure learning of latent factors via clique search on correlation thresholded graphs. Proceedings of Machine Learning Research, 202 (ICML): 16978-16996. [Reprint].
  7. Zhou, K., Li, K.C., and Zhou, Q. (2023). Honest confidence sets for high-dimensional regression by projection and shrinkage. Journal of the American Statistical Association, 118: 469-488. [Reprint] [Code and data].
  8. Huang, J. and Zhou, Q. (2023). Bayesian causal bandits with backdoor adjustment prior. Transactions on Machine Learning Research, 01/2023.
  9. Amini, A.A., Aragam, B., and Zhou, Q. (2022). A non-graphical representation of conditional independence via the neighborhood lattice. arXiv Preprint, 2206.05829. [Preprint].
  10. Ruiz, R., Madrid Padilla, O.H., and Zhou, Q. (2022). Sequentially learning the topological ordering of directed acyclic graphs with likelihood ratio scores. Transactions on Machine Learning Research, 12/2022.
  11. Peng, Z., and Zhou, Q. (2022). An empirical Bayes approach to stochastic blockmodels and graphons: shrinkage estimation and model selection. PeerJ Computer Science, 8: e1006. [Reprint].
  12. Huang, J., and Zhou, Q. (2022). Partitioned hybrid learning of Bayesian network structures. Machine Learning, 111: 1695-1738. [Reprint] [R package].
  13. Amini, A.A., Aragam, B., and Zhou, Q. (2022). On perfectness in Gaussian graphical models. Proceedings of Machine Learning Research, 151 (AISTATS): 7505-7517.[Reprint].
  14. Ye, Q., Amini, A.A., and Zhou, Q. (2021). Optimizing regularized Cholesky score for order-based learning of Bayesian networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43: 3555-3572.[Reprint].
  15. Wang, B., and Zhou, Q. (2021). Causal network learning with non-invertible functional relationships. Computational Statistics and Data Analysis, 156: 107141. [Preprint].
  16. Min, S. and Zhou, Q. (2021). Constructing confidence sets after lasso selection by randomized estimator augmentation. arXiv Preprint, 1904.08018v2. [Preprint] [R package].
  17. Gu, J. and Zhou, Q. (2020). Learning big Gaussian Bayesian networks: partition, estimation, and fusion. Journal of Machine Learning Research, 21(158): 1-31. [Reprint].
  18. Li, J., Zhou, Q., and Yeh, W.W.-G. (2020). A Bayesian hierarchical model for estimating the statistical parameters in a three-parameter log-normal distribution for monthly average streamflows. Journal of Hydrology, 591: 125265.
  19. Tran, S.S., Zhou, Q.*, and Xiao, X.* (2020). Statistical inference of differential RNA editing sites from RNA-sequencing data by hierarchical modeling. Bioinformatics, 36: 2796-2804. (*Corresponding authors.)
  20. Aragam, B., Amini, A.A., and Zhou, Q. (2019). Globally optimal score-based learning of directed acyclic graphs in high-dimensions. Advances in Neural Information Processing Systems (NeurIPS), 32: 4450-4462. [Reprint].
  21. Aragam, B., Gu, J., and Zhou, Q. (2019). Learning large-scale Bayesian networks with the sparsebn package. Journal of Statistical Software, 91: issue 11, 1-38. [Reprint] [R package].
  22. Gu, J., Fu, F., and Zhou, Q. (2019). Penalized estimation of directed acyclic graphs from discrete data. Statistics and Computing, 29: 161-176 (DOI: 10.1007/s11222-018-9801-y). [Preprint] [R package].
  23. Amini, A.A., Aragam, B., and Zhou, Q. (2019). The neighborhood lattice for encoding partial correlations in a Hilbert space. arXiv Preprint, 1711.00991v2. [Preprint].
  24. Zhou, Q. and Min, S. (2017). Estimator augmentation with applications in high-dimensional group inference. Electronic Journal of Statistics, 11: 3039-3080. [Reprint].
  25. Zhou, Q. and Min, S. (2017). Uncertainty quantification under group sparsity. Biometrika, 104: 613-632. [Preprint].
  26. Kok, J.F., Ridley, D.A., Zhou, Q., Miller, R.L., Zhao, C., Heald, C.L., Ward, D.S., Albani, S., and Haustein, K. (2017). Smaller desert dust cooling effect estimated from analysis of dust size and abundance. Nature Geoscience, 10: 274-278. [Reprint][Commentary]
  27. Aragam, B., Amini, A.A., and Zhou, Q. (2017). Learning directed acyclic graphs with penalized neighbourhood regression. arXiv Preprint, 1511.08963v3. [Preprint].
  28. Marchetti, Y. and Zhou, Q. (2016). Iterative subsampling in solution path clustering of noisy big data. Statistics and Its Interface, 9: 415-431. [Preprint] [R package]. (Invited submission for special issue on big data.)
  29. Aragam, B. and Zhou, Q. (2015). Concave penalized estimation of sparse Gaussian Bayesian networks. Journal of Machine Learning Research, 16: 2273-2328. [Reprint] [R package].
  30. Shen, S., Park, J.W., Lu, Z., Lin, L., Henry, M.D., Wu, Y.N., Zhou, Q., and Xing, Y. (2014). rMATS: Robust and flexible detection of differential alternative splicing from replicate RNA-Seq data. Proceedings of the National Academy of Sciences of USA, 111: E5593-E5601.
  31. Zhou, Q. (2014). Monte Carlo simulation for Lasso-type problems by estimator augmentation. Journal of the American Statistical Association, 109: 1495-1516. [Reprint].
  32. Marchetti, Y. and Zhou, Q. (2014). Solution path clustering with adaptive concave penalty. Electronic Journal of Statistics, 8: 1569-1603. [Reprint] [R package].
  33. Cha, M. and Zhou, Q. (2014). Detecting clustering and ordering binding patterns among transcription factors via point process models. Bioinformatics, 30: 2263-2271. [Reprint] [R code].
  34. Levinson, M. and Zhou, Q. (2014). A penalized Bayesian approach to predicting sparse protein-DNA binding landscapes. Bioinformatics, 30: 636-643. [Reprint] [Software].
  35. Zhao, K., Lu, Z., Park, J.W., Zhou, Q., and Xing, Y. (2013). GLiMMPS: Robust statistical model for regulatory variation of alternative splicing using RNA-Seq data. Genome Biology, 14: R74.
  36. Lee, Y. and Zhou, Q. (2013). Co-regulation in embryonic stem cells via context-dependent binding of transcription factors. Bioinformatics, 29: 2162-2168. [Software].
  37. Fu, F. and Zhou, Q. (2013). Learning sparse causal Gaussian networks with experimental intervention: Regularization and coordinate descent. Journal of the American Statistical Association, 108: 288-300. [Code] [Reprint]
  38. Tang, W. and Zhou, Q. (2012). Finding multiple minimum-energy conformations of the hydrophobic-polar protein model via multidomain sampling. Physical Review E, 86: 031909. [Reprint]
  39. Shen, S., Park, J.W., Huang, J., Dittmar, K.A., Lu, Z., Zhou, Q., Carstens, R.P., and Xing, Y. (2012). MATS: A Bayesian framework for flexible detection of differential alternative splicing from RNA-Seq data. Nucleic Acids Research, 40: e61.
  40. Zhou, Q. (2011). Multi-domain sampling with applications to structural inference of Bayesian networks. Journal of the American Statistical Association, 106: 1317-1330. [Reprint]
  41. Chen, G. and Zhou, Q. (2011). Searching ChIP-Seq genomic islands for combinatorial regulatory codes in mouse embryonic stem cells. BMC Genomics, 12: 515.
  42. Zhou, Q. (2011). Random walk over basins of attraction to construct Ising energy landscapes. Physical Review Letters, 106: 180602. [Reprint].
  43. Zhou, Q. (2010). On weight matrix and free energy models for sequence motif detection. Journal of Computational Biology, 17: 1621-1638. [Preprint].
  44. Mason, M.J., Plath, K., and Zhou, Q. (2010). Identification of context-dependent motifs by contrasting ChIP binding data. Bioinformatics, 26: 2826-2832. [Software].
  45. Chen, G. and Zhou, Q. (2010). Heterogeneity in DNA multiple alignments: Modeling, inference, and applications in motif finding. Biometrics, 66: 694-704. [Preprint] [Software].
  46. Ouyang, Z., Zhou, Q., and Wong, W.H. (2009). ChIP-Seq of transcription factors predicts absolute and differential gene expression in embryonic stem cells. Proceedings of the National Academy of Sciences of USA, 106: 21521-21526.
  47. Mason, M.J., Fan, G., Plath, K., Zhou, Q.*, and Horvath, S.* (2009). Signed weighted gene co-expression network analysis of transcriptional regulation in murine embryonic stem cells. BMC Genomics, 10: 327. (*Corresponding authors.)
  48. Gao, K., Zhou, H., Zhang, L., Lee, J.W., Zhou, Q., Hu, S., Wolinsky, L.E., Farrell, J., Eibl, G., and Wong, D.T. (2009). Systemic disease-induced salivary biomarker profiles in mouse models of melanoma and non-small cell lung cancer. PLoS One, 4: e5875.
  49. Zhou, Q. and Wong, W.H. (2009). Energy landscape of a spin-glass model: Exploration and characterization. Physical Review E, 79: 051117.
  50. Sridharan, R.*, Tchieu, J.*, Mason, M.J.*, Yachechko, R., Kuoy, E., Horvath, S., Zhou, Q., and Plath, K. (2009). Role of the murine reprogramming factors in the induction of pluripotency. Cell, 136: 364-377. (*Equally contributed authors.)
  51. Zhou, Q. and Wong, W.H. (2008). Reconstructing the energy landscape of a distribution from Monte Carlo samples. Annals of Applied Statistics, 2: 1307-1331. [Reprint].
  52. Zhou, Q. and Liu, J.S. (2008). Extracting sequence features to predict protein-DNA interactions: A comparative study. Nucleic Acids Research, 36: 4137-4148. [Preprint] [Supplemental materials].
  53. Zhou, Q., Chipperfield, H., Melton, D.A., and Wong, W.H. (2007). A gene regulatory network in mouse embryonic stem cells.  Proceedings of the National Academy of Sciences of USA, 104: 16438-16443.
  54. Zhou, Q. and Wong, W.H. (2007). Coupling hidden Markov models for the discovery of cis-regulatory modules in multiple species. Annals of Applied Statistics, 1: 36-65. [Reprint] [Webpage].
  55. Kou, S.C.*, Zhou, Q.*, and Wong, W.H. (2006). Equi-energy sampler with applications in statistical inference and statistical mechanics (with discussion). Annals of Statistics, 34: 1581-1652. (*Equally contributed authors.) [Reprint] [Discussion and Rejoinder].
  56. Johnson, D.S., Zhou, Q., Yagi, K., Satoh, N., Wong, W.H., and Sidow, A. (2005). De novo discovery of a tissue-specific gene regulatory module in a chordate. Genome Research, 15: 1315-1324.
  57. Hong, P., Liu, X.S., Zhou, Q., Lu, X., Liu, J.S., and Wong, W.H. (2005). A boosting approach for motif modeling using ChIP-chip data. Bioinformatics, 21: 2636-2643.
  58. Zhou, Q. and Wong, W.H. (2004). CisModule: De novo discovery of cis-regulatory modules by hierarchical mixture modeling. Proceedings of the National Academy of Sciences of USA, 101: 12114-12119. [Webpage].
  59. Zhou, Q. and Liu, J.S. (2004). Modeling within-motif dependence for transcription factor binding site predictions. Bioinformatics, 20: 909-916. [Webpage] [Supplementary Notes].
  60. Jensen, S.T., Liu, X.S., Zhou, Q., and Liu, J.S. (2004). Computational discovery of gene regulatory binding motifs: A Bayesian perspective. Statistical Science, 19: 188-204. [Reprint].
  61. Zhou, Q. and Li, Y.D. (2003). Directed variation in evolution strategies. IEEE Transactions on Evolutionary Computation, 7: 356-366.
  62. Ji, H.K.*, Zhou, Q.*, Wen, F., Xia, H.Y., Lu, X., and Li, Y.D. (2001). AsMamDB: An alternative splice database of mammals. Nucleic Acids Research, 29: 260-263. (*Equally contributed authors.)

Other Publications:

  1. Aragam, B., Gu, J., Amini, A.A., and Zhou, Q. (2017). Learning high-dimensional DAGs: Provable statistical guarantees and scalable approximation. NIPS Workshop on Advances in Modeling and Learning Interactions from Complex Data. [Reprint].
  2. Zhou, Q. (2010). Review of "A Guide to QTL Mapping with R/qtl" by Broman and Sen. Journal of Statistical Software, 32: book review 5.
  3. Zhou, Q. and Gupta, M. (2009). Regulatory motif discovery: From decoding to meta-analysis. New Developments in Biostatistics and Bioinformatics, chp 8, pp. 179-208, World Scientific. [Preprint].
  4. Liu J.S. and Zhou, Q. (2007). Predictive modeling approaches for studying protein-DNA binding. Proceedings of the Fourth International Congress of Chinese Mathematicians, Vol. 4: 151-167, Higher Education Press and International Press.
  5. Zhou, Q. (2006). Detecting cis-regulatory modules by modeling correlated structures in genomic sequences. Ph.D. Dissertation, Harvard University.