Shen, S., Demirdjian, L., Pan, Y., Stein, S., Xie, Z., Park, E., Wu, Y. N., Xing, Y. (2018) Detecting allele-specific alternative splicing from population-scale RNA-seq data. Under review.

Lu, H., Wu, Y. N., and Holyoak, K. J. (2018) Learning abstract semantic relations from non-relational word embeddings. Under review.

Wu, Y. N., Xie, J., Lu, Y., and Zhu, S. C. (2018) Sparse and deep generalizations of the FRAME model. Annals of Mathematical Sciences and Applications. Accepted.

Lu, Y., Gao, R., Zhu, S. C., and Wu, Y. N. (2018) Exploring generative perspective of convolutional neural networks by learning random field models. Statistics and Its Interface. Accepted.

Gao, R., Lu, Y., Zhou, J., Zhu, S. C., and Wu, Y. N. (2018) Learning generative ConvNets via multigrid modeling and sampling. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

Zhang, Q., Wu, Y. N., and Zhu, S. C. (2018) Interpretable convolutional neural networks. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

Xie., J, Lu, Y., Gao, R., and Wu, Y. N. (2018) Cooperative learning of energy-based model and latent variable model via MCMC teaching. AAAI-18: 32nd AAAI Conference on Artificial Intelligence.

Zhang, Q., Cao, R., Shi, F.,Wu, Y. N., and Zhu, S. C. (2018) Interpreting CNN knowledge via an explanatory graph. AAAI-18: 32nd AAAI Conference on Artificial Intelligence.

Xie, J., Zhu, S. C., and Wu, Y. N. (2017) Synthesizing dynamic patterns by spatial-temporal generative ConvNet. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

Xie, J., Xu, Y., Wu, Y. N., and Zhu, S. C. (2017) Generative hierarchical structural learning of sparse FRAME models. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

Zhang, Q., Cao, R., Wu, Y. N., and Zhu, S. C. (2017) Mining object parts from CNNs via active question-answering. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

Xie, J., Douglas, P., Wu, Y. N., Brody, A., Anderson, A. (2017) Decoding the encoding of functional brain networks: an fMRI classification comparison of non-negative matrix factorization (NMF), independent component analysis (ICA), and sparse coding algorithms. Journal of Neuroscience Methods, 282, 81-94.

Park, E., Guo, J., Shen, S., Demirdjian, L., Wu, Y. N., Lin, L., and Xing, Y. (2017) Population and allelic variation of A-to-I RNA editing in human transcriptomes. Genome Biology, 18:143.

Han, T., Lu, Y., Zhu, S. C. , and Wu, Y. N. (2017) Alternating back-propagation for generator network. AAAI-17: 31st AAAI Conference on Artificial Intelligence.

Zhang, Q., Cao, R., Wu, Y. N., and Zhu, S. C. (2017) Growing interpretable part graphs on ConvNets via multi-shot learning. AAAI-17: 31st AAAI Conference on Artificial Intelligence.

Xie, J., Lu, Y., Zhu, S. C., and Wu, Y. N. (2016) A theory of generative ConvNet. International Conference on Machine Learning.

Lu, Y., Zhu, S. C., and Wu, Y. N. (2016) Learning FRAME models using CNN filters. AAAI-16: 30th AAAI Conference on Artificial Intelligence.

Heinzerling, K., Demirdjian, L., Wu, Y. N., and Shoptaw S. (2016). Single nucleotide polymorphism near CREB1, rs7591784, is associated with pretreatment methamphetamine use frequency and outcome of outpatient treatment for methamphetamine use disorder. Journal Psychiatry Research, 74:22-9.

Shen, S., Wang, Y., Wang, C., Wu, Y. N., Xing Y. (2016) SURVIV: survival analysis of mRNA isoform variation. Nature Communications, 7:11548.

Wu, W. B. and Wu, Y. N. (2016) High-dimensional linear models with dependent observations. Electronic Journal of Statistics, 10, 352-379.

Dai, J., Lu, Y., and Wu, Y. N (2016) Generative modeling of convolutional neural networks. Statistics and Its Interface, 9, 485-496.

Lee, K. J., Chen, R. B., and Wu, Y. N. (2016) Bayesian variable selection for finite mixture model of linear regressions, Computational Statistics and Data Analysis, 96, 1-16.

Chen, R. B., Chu, C. H., Yuan, S. and Wu, Y. N. (2016) Bayesian sparse group selection. Journal of Computational and Graphical Statistics, 25, 665-683.

Xie, J., Lu, Y., Zhu, S. C., and Wu, Y. N. (2016) Inducing wavelets into random fields via generative boosting. Applied and Computational Harmonic Analysis, 41, 4-25.

Zhang, Q., Wu, Y. N., and Zhu, S. C. (2015) Mining and-or graphs for graph matching and object discovery, in Proc. of 15th International Conference on Computer Vision (ICCV).

Dai, J., Lu, Y., and Wu, Y. N (2015) Generative modeling of convolutional neural networks. International Conference on Learning Representations (ICLR).

Fleishman, G. M., Fletcher, P. T., Gutman, B. A., Prasad, G., Wu, Y., Thompson P. M. (2015) Geodesic refinement using James-Stein estimators, 5th MICCAI Workshop on Mathematical Foundations of Computational Anatomy.

Xie, J., Hu, W., Zhu, S. C., and Wu, Y. N. (2015). Learning sparse FRAME models for natural image patterns, International Journal of Computer Vision, 114，91-112.

Anderson, A., Douglas, P. K., Kerr, W. T., Haynes, V. S., Yuille, A. L., Xie, J., Wu, Y. N. , and Cohen, M. S. (2014) Non-negative matrix factorization of multimodal MRI, fMRI and phenotypic data reveals differential changes in default mode subnetworks in ADHD. NeuroImage, 102, 207-219.

Yi, H., Hu, W., Zi, Z., Zhu, S. C., and Wu, Y. N. (2014) Unsupervised learning of compositional sparse code for natural images. Quarterly of Applied Mathematics. 72, 373-406.

Xie, J., Hu, W., Zhu, S. C., and Wu, Y. N. (2014) Learning inhomogeneous FRAME models for object patterns. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

Dai, J., Hong, Y., Hu, W., Zhu, S. C., and Wu, Y. N. (2014) Unsupervised learning of dictionaries of hierarchical compositional models. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

Barbu, A., Wu, T., Wu, Y. N. (2014) Learning mixtures of Bernoulli templates by two-round EM with performance guarantee. Electronic Journal of Statistics, 8, 3004-3030.

Lee, J., Wu, Y. N., and Kim, G. (2014) Unbalanced data classification using support vector machines with active learning on scleroderma lung disease patterns. Journal of Applied Statistics, 42, 676-689.

Shen, S., Park, J. W., Lu, Z. X., Lin, L., Henry, M. D., Wu, Y. N., Zhou, Q., Xing, Y. (2014) rMATS: robust and flexible detection of differential alternative splicing from replicate RNA-seq data, Proceedings of National Academy of Science, 111(51):E5593-601.

Dai. J., Wu, Y. N., Zhou, J., and Zhu, S. C. (2013) Co-segmentation and co-sketch by unsupervised learning. Proceedings of International Conference of Computer Vision (ICCV).

Chen, R. B., Chu, C. H., Lai, T. Y. and Wu, Y. N. (2011) Stochastic matching pursuit for Bayesian variable selection. Statistics and Computing, 21, 247-259.

Hu, W., Wu, Y. N., and Zhu, S. C. (2011) Image representation by active curves. International Conference on Computer Vision.

Wu, Y. N., Si, Z., Gong, H., and Zhu, S. C. (2010) Learning active basis model for object detection and recognition. International Journal of Computer Vision, 90, 198-235.

Si, Z., Gong, H., Zhu, S. C., and Wu, Y. N., (2010) Learning active basis models by EM-type algorithms. Statistical Science, 25, 458–475.

Si, Z. and Wu, Y. N. (2010) Wavelet, active basis, and shape script --- a tour in the sparse land. ACM SIGMM International Conference on Multimedia Information Retrieval, Special session on Statistical Modeling and Learning for Multimedia.

Si, Z., Gong, H., Wu, Y. N., and Zhu, S. C. (2009) Learning mixed template for object recognition. Proceedings of Computer Vision and Pattern Recognition.

Wu, Y. N., Guo, C., and Zhu, S. C. (2008) From information scaling to regimes of statistical models. Quarterly of Applied Mathematics, 66, 81-122.

Guo, C., Zhu, S. C. and Wu, Y. N. (2007) Primal sketch: integrating structure and texture. Computer Vision and Image Understanding, 106, 5-19.

Li, J., Yang. X., Wu, Y. N. and Shoptaw, S. (2007) A random-effect Markov transition model for Poisson-distributed repeated measures with nonignorable missing values. Statistics in Medicine, 26, 2519-2532.

Zheng, M., Barrera, L. O., B. Ren, and Wu, Y. N. (2007) ChIP-chip: data, model, and analysis. Biometrics, 63, 787-796.

Wu, Y. N., Li, J., Liu, Z., and Zhu, S. C. (2007) Statistical principles in image modeling. Technometrics, 49, 249-261.

Chen, R. B. and Wu, Y. N. (2007) A null-space algorithm for overcomplete blind source separation. Computational Statistics and Data Analysis, 51, 5519-5536.

Wu, Y. N., Si, Z., Fleming, C., and Zhu, S. C. (2007) Deformable template as active basis. Proceedings of International Conference of Computer Vision.

Xing, Y. Yu, T., Wu, Y. N., Roy, M., Kim, J. and Lee C. (2006) An expectation-maximization algorithm for probabilistic reconstructions of full-length isoforms from splice graphs. Nucleic Acid Research, 34, 3150-3160.

Kim, T. H, Barrera, L. O., Zheng, M., Qu, C., Singer, M. A., Richmand, T. A., Wu, Y. N., Green, R. G. and Ren, B. (2005) A High-resolution map of active promoters in the human genome, Nature, 436, 876-880.

Wu, Y. N., Guo, C. E., and Zhu, S. C. (2004) Perceptual scaling. Applied Bayesian Modeling and Causal Inference from an Incomplete Data Perspective, Eds. Gelman and Meng, John Wiley.

Guo, C., Wu, Y. N., and Zhu, S. C. (2004) Information scaling laws in natural scenes. Proceedings of 2nd Workshop on Generative Model Based Vision.

Doretto, G., Chiuso, A, Wu, Y. N. and Soatto, S., (2003) Dynamic textures. International Journal of Computer Vision. 51, 91-109.

Guo, C., Zhu, S. C., and Wu, Y. N. (2003) Visual learning by integrating descriptive and generative models. International Journal of Computer Vision. 53, 5-29.

Guo, C., Zhu, S. C., and Wu, Y. N. (2003) Towards a mathematical theory of primal sketch and sketchability. Proceedings of International Conference of Computer Vision. 1228-1235.

Wu, Y. N., Zhu, S. C., and Guo, C. (2002) Statistical modeling of texture sketch. Proceedings of European Conference of Computer Vision, 240-254.

Zhu, S. C., Guo, C., Wu, Y. N. and Wang, Y. (2002) What are textons? Proceedings of European Conference of Computer Vision, 793-807.

Pinheiro J., Liu, C., and Wu, Y. N. (2001) Efficient algorithms for robust estimation in linear mixed-effects models using the multivariate t-distribution. Journal of Computational and Graphical Statistics, 10, 249-276.

Guo, C., Zhu, S. C., and Wu, Y. N. (2001) Visual learning by integrating descriptive and generative methods. Proceedings of International Conference on Computer Vision (ICCV).

Yuille, A. L., Coughlan, J., Wu, Y. N., and Zhu, S. C. (2001) Order parameters for detecting target curves in images: when does high level knowledge help? International Journal of Computer Vision. 41, 9-33.

Soatto, S., Doretto, G., and Wu, Y. N. (2001) Dynamic textures. Proceedings of International Conference of Computer Vision. 439-447.

Saisan, P., Doretto, G., Wu, Y. N., and Soatto, S. (2001) Dynamic texture recognition. Proceedings of Computer Vision and Pattern Recognition.

Zhu, S. C., Liu, X., and Wu, Y. N. (2000) Exploring texture ensembles by efficient Markov chain Monte Carlo - towards a `trichromacy' theory of texture. IEEE Pattern Analysis and Machine Intelligence, 22, 554-569.

Wu, Y. N., Zhu, S. C., and Liu, X. (2000) Equivalence of Julesz ensembles and FRAME models. International Journal of Computer Vision, 38, 245-261.

Yuille, A. L., Coughlan, J., Wu, Y. N., and Zhu, S. C. (2000), Order Parameter Theory for minimax entropy models: How Does High Level Knowledge Helps? Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

Liu, J. S. and Wu, Y. N. (1999) Parameter expansion for data augmentation. Journal of the American Statistical Association, 94, 1264-1274.

Wu, Y. N., Zhu, S. C., and Liu, X. (1999) Equivalence of Julesz and Gibbs texture ensembles. Proceedings of International Conference of Computer Vision, 1025-1032.

Zhu, S. C., Liu, X. W., and Wu, Y. N. (1999) Exploring Julesz Ensembles by Efficient MCMC, Proc. of Workshop on Statistical and Computational Theories of Vision (SCTV), Fort Collins, CO.

Matthysse S., Levy D. L., Wu Y. N., Rubin D. B., and Holzman P. (1999) Intermittent degradation in performance in schizophrenia, Schizophrenic Research, 40, 131-146.

Zhu, S. C., Wu, Y. N., and Mumford, D. B. (1998) Minimax entropy principle and its application to texture modeling. Neural Computation, 9, 1627-1660.

Liu, C., Rubin, D. B., and Wu, Y. N. (1998) Parameter expansion to accelerate EM - the PX-EM algorithm. Biometrika, 85, 755-770.

Rubin, D. B. and Wu, Y. N. (1997) Modeling schizophrenic behavior using general mixture components. Biometrics, 53, 243-261.

Zhu, S. C., Wu, Y. N., and Mumford, D. B. (1997) Filter, Random field, And Maximum Entropy (FRAME): towards a unified theory for texture modeling. International Journal of Computer Vision, 27, 107-126.

Zhu, S. C., Wu, Y. N., and Mumford, D. B. (1996), Filters, Random Fields, and Maximum Entropy (FRAME): towards a unified theory for texture modeling. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

Wu, Y. N. (1995) Random shuffling: a new approach to matching problem. Proceedings of American Statistical Association, 69-74.

Chernoff, H. and Wu, Y. N. (1994) Bounds on inconsistent inferences for sequences of trials with varying probabilities. In Probability, Statistics and Optimisation, Edited by F. P. Kelly. John Wiley & Sons. 351-366.