John Paisley

[ Main ]
[ Publications ]
[ Teaching ]
[ Personal ]

  Office: Shapiro CEPSR 712
  Email: jpaisley@columbia.edu
  Phone: (212) 854-8024
  Fax: (212) 932-9421

  Mail Address:
  Columbia University
  500 W. 120th St., Suite 1300
  New York, NY 10027



2014

  • Y. Huang, J. Paisley, Q. Lin, X. Ding, X. Fu and X. Zhang (2014). Bayesian nonparametric dictionary learning for compressed sensing MRI, IEEE Transactions on Image Processing, (to appear) [arXiv]
  • J. Paisley, C. Wang, D. Blei and M. Jordan (2014). Nested hierarchical Dirichlet processes, IEEE Transactions on Pattern Analysis and Machine Intelligence, (to appear) [arXiv]
  • T. Broderick, L. Mackey, J. Paisley and M. Jordan (2014). Combinatorial clustering and the beta negative binomial process, IEEE Transactions on Pattern Analysis and Machine Intelligence, (to appear) [arXiv]
  • D. Liang, J. Paisley and D. Ellis (2014). Codebook-based scalable music tagging with Poisson matrix factorization, International Society for Music Information Retrieval Conference (ISMIR), Taipei, Taiwan. [PDF]
  • X. Ding, Y. Jiang, Y. Huang and J. Paisley (2014). Pan-sharpening with a Bayesian nonparametric dictionary learning model, International Conference on Artificial Intelligence and Statistics (AISTATS), Reykjavik, Iceland. [PDF]
  • J. Paisley, D. Blei and M. Jordan (2014). Bayesian nonnegative matrix factorization with stochastic variational inference. In E.M. Airoldi, D. Blei, E.A. Erosheva & S.E. Fienberg (Eds.), Handbook of Mixed Membership Models and Their Applications. Chapman and Hall/CRC Handbooks of Modern Statistical Methods. [PDF]

2013
  • M. Hoffman, D. Blei, C. Wang and J. Paisley (2013). Stochastic variational inference, Journal of Machine Learning Research, vol. 14, pp. 1303-1347. [PDF]
  • X. Ding, J. Paisley, Y. Huang, X. Chen, F. Huang, X.P. Zhang (2013). Compressed sensing MRI with Bayesian dictionary learning, IEEE International Conference on Image Processing (ICIP), Melbourne, Australia. [PDF]
  • J. Xie, Y. Huang, J. Paisley, X. Ding and X.P. Zhang (2013). Pan-sharpening based on nonparametric Bayesian adaptive dictionary learning, IEEE International Conference on Image Processing (ICIP), Melbourne, Australia. [PDF]

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] [C code] [Matlab code]
  • J. Paisley, D. Blei and M. Jordan (2012). Variational Bayesian inference with stochastic search, International Conference on Machine Learning (ICML), 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), 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), 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), 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), Fort Lauderdale, FL. [PDF]
  • 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), 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), 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), 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), 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), 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 and before
  • M. Zhou, H. Chen, J. Paisley, L. Ren, G. Sapiro and L. Carin (2009). Non-parametric Bayesian dictionary learning for sparse image representations, Advances in Neural Information Processing Systems (NIPS), Vancouver, Canada. [PDF]
  • J. Paisley and L. Carin (2009). Nonparametric factor analysis with beta process priors, International Conference on Machine Learning (ICML), 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), Taipei, Taiwan. [PDF]
  • 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]
  • 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), Honolulu, HI. [PDF]