John Paisley Department of EECS University of California, Berkeley jpaisley@berkeley.edu   [Papers]   [Resume]   [Other]        Currently submitted Y. Huang, J. Paisley, X. Chen, X. Ding, F. Huang and X. Zhang (2013). MR image reconstruction from undersampled k-space with Bayesian dictionary learning. [arXiv] J. Paisley, C. Wang, D. Blei and M.I. Jordan (2012). Nested hierarchical Dirichlet processes. [arXiv] T. Broderick, L. Mackey, J. Paisley and M. Jordan (2012). Combinatorial clustering and the beta negative binomial process. [arXiv] M. Hoffman, D. Blei, C. Wang and J. Paisley (2012). Stochastic variational inference, to appear in Journal of Machine Learning Research [arXiv] 2012 J. Paisley, C. Wang and D. Blei (2012). The discrete infinite logistic normal distribution, Bayesian Analysis, vol. 7, no. 2, pp. 235-272. [PDF] J. Paisley, D. Blei and M. Jordan (2012). Variational Bayesian inference with stochastic search, International Conference on Machine Learning (ICML 2012), Edinburgh, Scotland. [PDF] J. Paisley, D. Blei and M. Jordan (2012). Stick-breaking beta processes and the Poisson process, International Conference on Artificial Intelligence and Statistics (AISTATS 2012), La Palma, Canary Islands. [PDF] M. Zhou, H. Chen, J. Paisley, L. Ren, L. Li, Z. Xing, D. Dunson, G. Sapiro and L. Carin (2012). Nonparametric Bayesian dictionary learning for analysis of noisy and incomplete images, IEEE Transactions on Image Processing, vol. 21, no. 1, pp. 130-144. [PDF] 2011 J. Paisley, L. Carin and D. Blei (2011). Variational inference for stick-breaking beta process priors, International Conference on Machine Learning (ICML 2011), Bellevue, WA. [PDF] J. Paisley, C. Wang and D. Blei (2011). The discrete infinite logistic normal distribution for mixed-membership modeling, International Conference on Artificial Intelligence and Statistics (AISTATS 2011), Fort Lauderdale, FL. [PDF] [C code] [Matlab code] (notable paper award) C. Wang, J. Paisley and D. Blei (2011). Online variational inference for the hierarchical Dirichlet process, International Conference on Artificial Intelligence and Statistics (AISTATS 2011), Fort Lauderdale, FL. [PDF] (oral presentation) M. Zhou, C. Wang, M. Chen, J. Paisley, D. Dunson and L. Carin (2011). Nonparametric Bayesian matrix completion, 9th International Conference on Sampling Theory and Applications (SampTA 2011), Singapore. [PDF] 2010 J. Paisley, X. Liao and L. Carin (2010). Active learning and basis selection for kernel-based linear models: A Bayesian perspective, IEEE Transactions on Signal Processing, vol. 58, no. 5, pp. 2686-2700. [PDF] B. Chen, M. Chen, J. Paisley, A. Zaas, C. Woods, G.S. Ginsburg, A. Hero III, J. Lucas, D. Dunson and L. Carin (2010). Bayesian inference of the number of factors in gene-expression analysis: Application to human virus challenge studies, BMC Bioinformatics, 11:552. [PDF] M. Chen, J. Silva, J. Paisley, C. Wang, D. Dunson and L. Carin (2010). Compressive sensing on manifolds using a nonparametric mixture of factor analyzers: Algorithm and performance bounds, IEEE Transactions on Signal Processing, vol. 58, no. 12, pp. 6140-6155. [PDF] J. Paisley, M. Zhou, G. Sapiro and L. Carin (2010). Nonparametric image Interpolation and dictionary learning using spatially-dependent Dirichlet and beta process priors, IEEE International Conference on Image Processing (ICIP 2010), Hong Kong. [PDF] [Code] J. Paisley, A. Zaas, C.W. Woods, G.S. Ginsburg and L. Carin (2010). A stick-breaking construction of the beta process, International Conference on Machine Learning (ICML 2010), Haifa, Israel [PDF] [Code] J. Paisley and L. Carin (2010). A nonparametric Bayesian model for kernel matrix completion, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2010), Dallas, TX. [PDF] B. Chen, J. Paisley and L. Carin (2010). Sparse linear regression with beta process priors, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2010), Dallas, TX. [PDF] I. Pruteanu-Malinici, L. Ren, J. Paisley, E. Wang and L. Carin (2010). Hierarchical Bayesian modeling of topics in time-stamped documents, IEEE Transactions Pattern Analysis and Machine Intelligence, vol. 32, no. 6, pp. 996-1011. [PDF] 2009 M. Zhou, H. Chen, J. Paisley, L. Ren, G. Sapiro and L. Carin (2009). Non-parametric Bayesian dictionary learning for sparse image representations, Neural Information Processing Systems (NIPS 2009), Vancouver, Canada. [PDF] (oral presentation) J. Paisley and L. Carin (2009). Nonparametric factor analysis with beta process priors, International Conference on Machine Learning (ICML 2009), Montreal, Canada. [PDF][Code] J. Paisley and L. Carin (2009). Hidden Markov models with stick breaking priors, IEEE Transactions on Signal Processing, vol. 57, pp. 3905-3917. [PDF] J. Paisley and L. Carin (2009). Dirichlet process mixture models with multiple modalities, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2009), Taipei, Taiwan. [PDF] 2008 K. Ni, J. Paisley, L. Carin and D. Dunson (2008). Multi-task learning for analyzing and sorting large databases of sequential data, IEEE Transactions on Signal Processing, vol. 56, pp. 3918-3931. [PDF] 2007 Y. Qi, J. Paisley, L. Carin (2007). Music analysis using hidden Markov mixture models, IEEE Transactions on Signal Processing, vol. 55, pp. 5209-5224. [PDF] Y. Qi, J. Paisley, L. Carin (2007). Dirichlet process HMM mixture models with application to music analysis, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2007), Honolulu, HI. [PDF] Other J. Paisley, L. Carin and D. Blei (2011). Constructing beta processes and approximate posterior inference with variational Bayes, 8th Workshop on Bayesian Nonparametrics (BNP 2011), Veracruz, Mexico. J. Paisley and D. Blei (2010). Low-rank matrix factorization using regularized logistic regression, NIPS 2010 Workshop on Practical Application of Sparse Modeling Open Issues and New Directions, Whister, BC. J. Paisley and D. Blei (2010). Latent factor topic models with rank-reducing beta process priors, NIPS 2010 Workshop on Low-rank Methods for Large-Scale Machine Learning, Whistler, BC. J. Paisley, S. Gerrish and D. Blei (2010). Dynamic modeling with the collaborative Kalman filter, 5th Annual ML Symposium, New York, NY. M. Zhou, J. Paisley and L. Carin (2009). Nonparametric learning of dictionaries for sparse representation of sensor signals, 3rd IEEE Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), Aruba. [PDF]

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