2024

• A. Nazaret, C. Shi, and D. Blei. On the misspecification of linear assumptions in synthetic controls. Artificial Intelligence and Statistics, 2024.   [paper]
• Y. Park and D. Blei. Density uncertainty layers for reliable uncertainty estimation. Artificial Intelligence and Statistics, 2024.   [paper]
• C. de Bacco, Y. Wang, and D. Blei. A causality-inspired plus-minus model for player evaluation in team sports. Causal Learning and Reasoning, 2024.   [paper]
• G. Moran, D. Blei, and R. Ranganath. Holdout predictive checks for Bayesian model criticism.  Journal of the Royal Statistical Society, Series B, 86(1):194-214, 2024.  [paper]
• M. Yin, C. Shi, Y. Wang, and D. Blei.  Conformal sensitivity analysis for individual treatment effects. Journal of the American Statistical Association, 19(545):122-135, 2024.  [paper]

2023

• K. Vafa, E. Palikot, T. Du, A. Kanodia, S. Athey, and D. Blei. CAREER: A foundation model for labor sequence data. Transactions on Machine Learning Research , 2024.   [paper]
• C. Zheng, K. Vafa, and D. Blei. Revisiting topic-guided language models. Transactions on Machine Learning Research, 2023.   [paper]
• G. Moran, J. Cunningham, D. Blei.  The posterior predictive null.  Bayesian Analysis, 18(4):194-214, 2023.   [paper] [code]
• A. Feder, Y. Wald, C. Shi, S. Saria, and D. Blei.  Causal-structure driven augmentations for text OOD generalization. Neural Information Processing Systems, 2023.  [paper]
• C. Modi, R. Gower, C. Margossian, Y. Yao, D. Blei, and L. Saul.  Variational inference with Gaussian score matching. Neural Information Processing Systems, 2023.  [paper]
• N. Scherrer, C. Shi, A. Feder, and D. Blei.  Evaluating the moral beliefs encoded in LLMs. Neural Information Processing Systems, 2023.  [paper]
• J. von Kugelgen, M. Besserve, W. Liang, L. Gresele, A. Kekic, E. Bareinboim, D. Blei, and B. Scholkopf.  Nonparametric identifiability of causal representations from unknown interventions. Neural Information Processing Systems, 2023.  [paper]
• L. Wu, B. Trippe, C. Naesseth, D. Blei, and J. Cunningham.  Practical and asymptotically exact conditional sampling in diffusion models. Neural Information Processing Systems, 2023.  [paper]
• L. Zhang, D. Blei, and C. Naesseth.  Transport score climbing: Variational inference using forward KL and adaptive neural transport.  Transactions on Machine Learning Research, 2023.  [paper]
• C. Zheng, C. Shi, K. Vafa, A. Feder, and D. Blei.  An invariant learning characterization of controlled text generation. Association for Computational Linguistics, 2023.  [paper]
• Y. Wang, D. Sridhar, and D. Blei.  Adjusting machine learning decisions for equal opportunity and counterfactual fairness.   Transactions on Machine Learning Research, 2023.  [paper]
• C. Modi, Y. Li, and D. Blei.  Reconstructing the universe with variational self-boosted sampling. Journal of Cosmology and Astroparticle Physics, 059, 2023.  [paper]
• Z. Wang, R. Gao, M. Yin, M. Zhou, and D. Blei. Probabilistic conformal prediction using conditional random samples.  Artificial Intelligence and Statistics, 2023.  [paper]

2022

• L. Zhang, Y. Wang, M. Schuemie, D. Blei, and G. Hripcsak.  Adjusting for indirectly measured confounding using large-scale propensity score.  Journal of Biomedical Informatics, 134, 2022.  [paper]
• D. Sridhar and D. Blei.  Causal inference from text: A commentary. Science Advances, 8(42), 2022.  [paper]
• G. Moran, D. Sridhar, Y. Wang, and D. Blei.  Identifiable deep generative models via sparse decoding.  Transactions on Machine Learning Research, 2022.  [paper]
• A. Miller, L. Anderson, B. Leistedt, J. Cunningham, D. Hogg, and D. Blei.  Mapping interstellar dust with Gaussian processes.  Annals of Applied Statistics, 16:2672-2692, 2022.  [paper]
• A. Nazaret and D. Blei.  Variational inference for infinitely deep neural networks. International Conference on Machine Learning, 2022.  [paper]
• C. Shi, D. Sridhar, V. Misra, and D. Blei. On the assumptions of synthetic control methods. Artificial Intelligence and Statistics, 2022.  [paper] [code]
• D. Sridhar, C. D. Bacco, and D. Blei. Estimating social influence from observational data. Causal Learning and Reasoning, 2022.  [paper] [code]
• S. Menon, D. Blei, and C. Vondrick.  Forget-me-not! Contrastive critics for mitigating posterior collapse.  Uncertainty in Artificial Intelligence, 2022.  [paper]
• D. Sridhar, H. Daume, and D. Blei. Heterogeneous supervised topic models for text prediction.  Transactions of the Association for Computational Linguistics, 10:732-745, 2022.  [paper] [code]
• W. Tansey, V. Veitch, H. Zhang, R. Rabadan, D. Blei. The holdout randomization test for feature selection in black box models.  Journal of Computational and Graphical Statistics , 31(1):151-162, 2022.  [paper] [code]
• W. Tansey, C. Tosh, and D. Blei. A Bayesian model of dose-response for cancer drug studies.  Annals of Applied Statistics, 16(2):680-705.  [paper] [code]

2021

• J. Loper, D. Blei, J. Cunningham, and L. Paninski.  A general linear-time inference method for Gaussian processes on one dimension. Journal of Machine Learning Research, 22(234):1–36, 2021.  [paper]
• Y. Wang, D. Blei, and J. Cunningham.  Posterior collapse and latent variable non-identifiability. Neural Information Processing Systems, 2021.  [paper]
• Y. Park, S. Lee, G. Kim, and D. Blei. Unsupervised representation learning via neural activation coding.   International Conference on Machine Learning, 2021.   [paper] [code]
• Y. Wang and D. Blei. A proxy variable view of shared confounding.  International Conference on Machine Learning , 2021.   [paper] [code]
• C. Shi, V. Veitch, and D. Blei. Invariant representation learning for treatment effect estimation.   Uncertainty in Artificial Intelligence, 2021.   [paper] [code]
• A. Moretti and L. Zhang and C. Naesseth and H. Venner and D. Blei and I. Pe’er. Variational combinatorial sequential Monte Carlo methods for Bayesian phylogenetic inference.   Uncertainty in Artificial Intelligence, 2021.   [paper] [code]
• L. Wu, A. Miller, L. Anderson, G. Pleiss, D. Blei, and J. Cunningham. Hierachical inducing point Gaussian process for inter-domian observations.   Artificial Intelligence and Statistics, 2021.   [paper] [code]
• A. Schein, K. Vafa, D. Sridhar, V. Veitch, J. Quinn, J. Moffet, D. Blei, and D. Green. Assessing the effects of friend-to-friend texting on turnout in the 2018 US midterm elections. The Web Conference, 2021.  [paper] [code]
• W. Tansey, K. Li, H. Zhang, S. Linderman, D. Blei, R. Rabadan, and C. Wiggins. Dose-response modeling in high-throughput cancer drug screenings: An end-to-end approach.  Biostatistics, to appear.  [paper] [code]

2020

• W. Tansey, Y. Wang, R. Rabadan, and D. Blei. Double empirical Bayes testing.  International Statistical Review, 88, 2020.  [paper] [code]
• C. Naesseth, F. Lindsten, and D. Blei. Markovian score climbing: Variational inference with KL(p||q). Neural Information Processing Systems, 2020.  [paper] [code]
• Y. Wang, D. Liang, L. Charlin, and D. Blei. Causal inference for recommender systems.  ACM Conference on Recommender Systems, 2020.  [paper] [code]
• A. Dieng, F. Ruiz, and D. Blei. Topic modeling in embedding spaces.   Transactions of the Association for Computational Linguistics, 8:439-453, 2020.   [paper] [code]
• F. Ruiz, S. Athey, and D. Blei.  SHOPPER: A probabilistic model of consumer choice with substitutes and complements.   Annals of Applied Statistics, 14(1):1-27, 03 2020.   [paper] [code]
• K. Vafa, S. Naidu, and D. Blei.   Text-based ideal points.   Association for Computational Linguistics, 2020.   [paper] [code] [tutorial] [slides]
• V. Veitch, D. Sridhar, and D. Blei.  Adapting text embeddings for causal inference.   Uncertainty in Artificial Intelligence, 2020.  [paper] [code]

2019

• Y. Wang and D. Blei. The blessings of multiple causes.  Journal of the American Statistical Association, 114:528, 1574-1596, 2019.   [paper] [slides] [tutorial] [code]
• C. Shi, D. Blei, and V. Veitch.  Adapting neural networks for the estimation of treatment effects. In Neural Information Processing Systems, 2019.  [paper] [code]
• V. Veitch, Y. Wang, and D. Blei.  Using embeddings to correct for unobserved confounding in networks. In Neural Information Processing Systems, 2019.  [paper] [code]
• Y. Wang and D. Blei.  Variational Bayes under model misspecification. In Neural Information Processing Systems, 2019.  [paper] [code]
• A. Schein, S. Linderman, M. Zhou, D. Blei, and H. Wallach.  Poisson-randomized gamma dynamical systems. In Neural Information Processing Systems, 2019.  [paper] [code]
• Y. Wang and D. Blei.   Frequentist consistency of variational Bayes.   Journal of the American Statistical Association, 114:527, 1147-1161, 2019.   [paper]
• L. Zhang, Y. Wang, A. Ostropolets, J. Mulgrave, D. Blei, and G. Hripcsak. The medical deconfounder: Assessing treatment effects with electronic health records. In Machine Learning for Health Care, 2019.  [paper] [code]
• H. Levitin, J. Yuan, Y. Cheng, F. Ruiz, E. Bush, J. Bruce, P. Canoll, A. Iavarone, A. Lasorella, D. Blei, and P. Sims. De novo gene signature identification from single-cell RNA-seq with hierarchical Poisson factorization. Molecular Systems Biology, 15(e8557), 2019. [paper] [code]
• A. Dieng, Y. Kim, A. Rush, and D. Blei. Avoiding latent variable collapse with generative skip models. Artificial Intelligence and Statistics, 2019. [paper] [code]
• V. Veitch, M. Austern, W. Zhou, D. Blei, and P. Orbanz. Empirical risk minimization and stochastic gradient descent for relational data. Artificial Intelligence and Statistics, 2019.  [paper] [code]

2018

• R. Ranganath and D. Blei. Correlated random measures. Journal of the American Statistical Association, 113(521):417-430, 2018.   [paper]
• C. Wang and D. Blei. A general method for robust Bayesian modeling.   Bayesian Analysis, 4(13):1163-1191, 2018.   [paper]
• J. Manning, X. Zhu, T. Willke, R. Ranganath, K. Stachenfeld, U. Hasson, D. Blei, and K. Norman. A probabilistic approach to discovering dynamic full-brain functional connectivity patterns. NeuroImage, 180:243-252.   [paper]
• S. Athey, D. Blei, R. Donnelly, F. Ruiz, and T. Schmidt.  Estimating heterogeneous consumer preferences for restaurants and travel time using mobile location data.  AEA Papers and Proceedings, 108:64-67, 2018.  [paper]
• A. Dieng, R. Ranganath, J. Altosaar, and D. Blei. Noisin: Unbiased regularization for recurrent neural networks.  International Conference on Machine Learning, 2018.  [paper]
• F. Ruiz, M. Titsias, A. Dieng, and D. Blei.  Augment and reduce: Stochastic inference for large categorical distributions.  International Conference on Machine Learning, 2018.  [paper]
• W. Tansey, Y. Wang, D. Blei, and R. Rabadan.  Black box FDR.  International Conference on Machine Learning, 2018.  [paper]
• D. Tran and D. Blei.  Implicit causal models for genome-wide association studies.  International Conference on Learning Representations, 2018.  [paper]
• D. Blei.  Expressive probabilistic models and scalable method of moments.  Communications of the ACM, 61(4):84, 2018. [paper]
• A. Gerow, Y. Hu, J. Boyd-Graber, D. Blei, and J. Evans.  Measuring discursive influence across scholarship.  Proceedings of the National Academy of Sciences, 115(13):3308-3313, 2018.   [paper]
• C. Naesseth, S. Linderman, R. Ranganath, and D. Blei.  Variational sequential Monte Carlo. Artificial Intelligence and Statistics, 2018.  [paper]
• J. Altosaar, R. Ranganath, and D. Blei.  Proximity variational inference. Artificial Intelligence and Statistics, 2018.  [paper]
• M. Rudolph and D. Blei. Dynamic embeddings for language evolution. International World Wide Web Conference, 2018.  [paper]

2017

• S. Mandt, M. Hoffman, and D. Blei. Stochastic gradient descent as approximate Bayesian inference. Journal of Machine Learning Research, 18:1-35, 2017.   [paper]
• M. Rudolph, F. Ruiz, S. Athey, and D. Blei.  Structured embedding models for grouped data.  Neural Information Processing Systems, 2017.  [paper]
• A. Dieng, D. Tran, R. Ranganath, J. Paisley, and D. Blei.  Variational inference via chi upper bound minimization.  Neural Information Processing Systems, 2017.  [paper]
• L. Liu, F. Ruiz, S. Athey, and D. Blei.  Context selection for embedding models. Neural Information Processing Systems, 2017.  [paper]
• D. Tran, R. Ranganath, and D. Blei.   Hierararchical implicit models and likelihood-free variational inference. Neural Information Processing Systems, 2017.   [paper]
• D. Blei and P. Smyth.   Science and data science. Proceedings of the National Academy of Sciences, 114(33):8689-8692, 2017.   [paper]
• L. Liu and D. Blei.   Zero-inflated exponential family embeddings.   International Conference on Machine Learning, 2017.   [paper]
• Y. Wang, A. Kucukelbir, and D. Blei.   Robust probabilistic modeling with Bayesian data reweighting.   International Conference on Machine Learning, 2017.   [paper] [code]
• A. Kucukelbir, Y. Wang, and D. Blei.   Evaluating Bayesian models with posterior dispersion indices.   International Conference on Machine Learning, 2017.   [paper] [code]
• D. Blei, A. Kucukelbir, and J. McAuliffe.   Variational inference: A review for statisticians.  Journal of the American Statistical Association, 112(518):859-877, 2017.   [paper] [errata]
• A. Kucukelbir, D. Tran, R. Ranganath, A. Gelman, and D. Blei.   Automatic differentiation variational inference.   Journal of Machine Learning Research, 18(14):1-45, 2017.   [paper]
• S. Linderman, M. Johnson, A. Miller, R. Adams, D. Blei, and L. Paninski.   Bayesian learning and inference in recurrent switching linear dynamical systems.   Artificial Intelligence and Statistics, 2017.   [paper]
• C. Naesseth, F. Ruiz, S. Linderman, and D. Blei.   Reparameterization gradients through acceptance-rejection sampling algorithms. Artificial Intelligence and Statistics, 2017.   [paper]
• D. Tran, M. Hoffman, R. Sauraus, E. Brevdo, K. Murphy, and D. Blei.   Deep probabilistic programming.   International Conference on Learning Representations, 2017.   [paper]

2016

• P. Gopalan, W. Hao, D. Blei, and J. Storey. Scaling probabilistic models of genetic variation to millions of humans.   Nature Genetics, 48:1587-1590.   [paper] [preprint] [errata]
• M. Rudolph, F. Ruiz, S. Mandt, and D. Blei.   Exponential family embeddings.   Neural Information Processing Systems, 2016.   [paper]
• R. Ranganath, J. Altosaar, D. Tran, and D. Blei.   Operator variational inference.   Neural Information Processing Systems, 2016.   [paper]
• F. Ruiz, M. Titsias, and D. Blei.   The generalized reparameterization gradient.   Neural Information Processing Systems, 2016.   [paper]
• R. Ranganath, A. Perotte, N. Elhadad, and D. Blei.   Deep survival analysis.   Machine Learning for Health Care, 2016.   [paper]
• A. Chaney, H. Wallach, M. Connelly, and D. Blei.   Detecting and characterizing events.   Empirical Methods in Natural Language Processing, 2016.   [paper]
• D. Liang, J. Altosaar, L. Charlin, and D. Blei.   Factorization meets the item embedding: Regularizing matrix factorization with item co-occurrence.   ACM Conference on Recommendation Systems, 2016.   [paper]
• F. Ruiz, M. Titsias, and D. Blei.   Overdispersed black-box variational inference.   Uncertainty in Artificial Intelligence, 2016.   [paper]
• R. Ranganath, D. Tran, and D. Blei.   Hierarchical variational models.   International Conference on Machine Learning, 2016.   [paper]
• A. Schein, M. Zhou, D. Blei, and H. Wallach.   Bayesian Poisson Tucker decomposition for learning the structure of international relations.   International Conference on Machine Learning, 2016.   [paper]
• S. Mandt, M. Hoffman, and D. Blei.   A variational analysis of stochastic gradient algorithms.   International Conference on Machine Learning, 2016.   [paper]
• S. Mandt, J. McInerney, F. Abrol, R. Ranganath, and D. Blei.   Variational tempering.   Artificial Intelligence and Statistics, 2016.   [paper]
• D. Tran, R. Ranganath, and D. Blei.   The variational Gaussian process.   International Conference on Learning Representations, 2016.   [paper]
• D. Liang, L. Charlin, J. McInerney, and D. Blei.   Modeling user exposure in recommendation.   International World Wide Web Conference, 2016. [paper] [code]
• M. Rudolph, J. Ellis, and D. Blei.   Objective variables for probabilistic revenue maximization in second-price auctions with reserve.   International World Wide Web Conference, 2016.   [paper]

2015

• A. Kucukelbir, R. Ranganath, A. Gelman, and D. Blei.   Automatic variational inference in Stan.   Neural Information Processing Systems , 2015.   [paper]
• J. McInerney, R. Ranganath, and D. Blei.   The population posterior and Bayesian inference on streams.   Neural Information Processing Systems , 2015.   [paper]
• D. Tran, D. Blei, and E. Airoldi.   Copula variational inference.   Neural Information Processing Systems , 2015.   [paper]
• L. Charlin, R. Ranganath, J. McInerney, and D. Blei.   Dynamic Poisson factorization.   ACM Conference on Recommendation Systems , 2015.   [paper]
• A. Chaney, D. Blei, and T. Eliassi-Rad.   A probabilistic model for using social networks in personalized item recommendation   ACM Conference on Recommendation Systems , 2015.   [paper]
• D. Mimno, D. Blei, and B. Engelhardt.   Posterior predictive checks to quantify lack-of-fit in admixture models of latent population structure.   Proceedings of the National Academy of Sciences, 112(26), 2015.   [paper]
• P. Gopalan, J. Hofman, and D. Blei.   Scalable recommendation with hierarchical Poisson factorization.   Uncertainty in Artificial Intelligence, 2015.   [paper]
• A. Kucukelbir and D. Blei.   Population empirical Bayes.   Uncertainty in Artificial Intelligence, 2015.   [paper]
• R. Ranganath, A. Perotte, N. Elhadad, and D. Blei.   The survival filter: Joint survival analysis with a latent time series.   Uncertainty in Artificial Intelligence, 2015.   [paper]
• A. Schein, J. Paisley, D. Blei, and H. Wallach.   Bayesian Poisson tensor factorization for inferring multilateral relations from sparse dyadic event counts.   Knowledge Discovery and Data Mining, 2015.   [paper]
• R. Ranganath, L. Tang, L. Charlin, and D. Blei.   Deep exponential families.   Artificial Intelligence and Statistics, 2015.   [paper]
• M. Hoffman and D. Blei.   Structured stochastic variational inference.   Artificial Intelligence and Statistics, 2015.   [paper]
• A. Perotte, R. Ranganath, J. Hirsch, D. Blei, and N. Elhadad.   Risk prediction for chronic kidney disease progression using heterogeneous electronic health record data and time series analysis.   Journal of the American Medical Informatics Association, 22(4), 2015.   [paper]
• J. Paisley, C. Wang, D. Blei, and M. Jordan.   Nested hierarchical Dirichlet processes.   IEEE Transactions on Pattern Analysis and Machine Intelligence, 37 (2), 2015.   [paper]
• G. Polatkan, M. Zhou, L. Carin, D. Blei, and I. Daubechies.   A Bayesian nonparametric approach to image super-resolution.   IEEE Transactions on Pattern Analysis and Machine Intelligence, 37 (2), 2015.   [paper]
• S. Gershman, P. Frazier, and D. Blei.   Distance dependent infinite latent feature models.   IEEE Transactions on Pattern Analysis and Machine Intelligence, 37 (2), 2015.   [paper] [supplement] [code]

2014

• S. Mandt and D. Blei.   Smoothed gradients for stochastic variational inference.   Neural Information Processing Systems, 2014.   [paper]
• P. Gopalan, L. Charlin, and D. Blei.   Content-based recommendations with Poisson factorization.   Neural Information Processing Systems, 2014.   [paper]
• N. Houlsby and D. Blei.   A filtering approach to stochastic variational inference.   Neural Information Processing Systems, 2014.   [paper]
• S. Gershman, D. Blei, K. Norman, and P. Sederberg.   Decomposing spatiotemporal brain patterns into topographic latent sources.   NeuroImage, 98:91--102.   [Preprint] [code]
• J. Manning, R. Ranganath, K. Norman, and D. Blei.   Topographic factor analysis: A Bayesian model for inferring brain networks from neural data.   PLoS ONE, 9(5), 2014.   [paper]
• R. Ranganath, S. Gerrish, and D. Blei.   Black box variational inference.   Artificial Intelligence and Statistics, 2014.   [paper]
• P. Gopalan, F. Ruiz, R. Ranganath, and D. Blei.   Bayesian nonparametric Poisson factorization for recommendation systems.   Artificial Intelligence and Statistics, 2014.   [paper]
• D. Blei.   Build, compute, critique, repeat: Data analysis with latent variable models.   Annual Review of Statistics and Its Applicaton 1:203-232, 2014.   [paper]
• M. Rabinovich and D. Blei.   The inverse regression topic model.   International Conference on Machine Learning, 2014.   [paper] [supplement]

2013

• P. Gopalan, C. Wang, and D. Blei.   Modeling overlapping communities with node popularities.   Neural Information Processing Systems, 2013.   [paper]
• D. Kim, P. Gopalan, D. Blei, and E. Sudderth.   Efficient online inference for Bayesian nonparametric relational models.   Neural Information Processing Systems, 2013   [paper]
• P. Gopalan and D. Blei.   Efficient discovery of overlapping communities in massive networks.   Proceedings of the National Academy of Sciences, 110 (36) 14534-14539, 2013.   [paper]
• P. DiMaggio, M. Nag, and D. Blei. Exploiting affinities between topic modeling and the sociological perspective on culture: Application to newspaper coverage of U.S. government arts funding.   Poetics, 41:6, 2013.   [Poetics]
• M. Hoffman, D. Blei, J. Paisley, and C. Wang.   Stochastic variational inference.   Journal of Machine Learning Research, 14:1303-1347, 2013.   [paper]
• C. Wang and D. Blei.   Variational inference in nonconjugate models.   Journal of Machine Learning Research, 14:1005-1031, 2013.   [paper] [code]
• D. Blei.   Topic modeling and digital humanities.   Journal of Digital Humanities, 2(1), 2013.   [Link]
• R. Ranganath, C. Wang, D. Blei, and E. Xing.  An adaptive learning rate for stochastic variational inference.   International Conference on Machine Learning, 2013. [paper]
• B. Chen, G. Polatkan, G. Sapiro, D. Blei, D. Dunson, and L. Carin.   Deep learning with hierarchical convolutional factor analysis.   IEEE Transactions on Pattern Analysis and Machine Intelligence, 8:1887-1901, 2013.   [paper]

2012

• S. Gerrish and D. Blei.   How they vote: Issue-adjusted models of legislative behavior   Neural Information Processing Systems, 2012.   [paper] [code]
• P. Gopalan, D. Mimno, S. Gerrish, M. Freedman, and D. Blei.   Scalable inference of overlapping communities.   Neural Information Processing Systems, 2012.   [paper]
• C. Wang and D. Blei.   Truncation-free stochastic variational inference for Bayesian nonparametric models.   Neural Information Processing Systems, 2012.   [paper]
• D. Blei.   Probabilistic topic models.   Communications of the ACM, 55(4):77–84, 2012.   [paper]
• J. Paisley, C. Wang, and D. Blei.   The discrete infinite logistic normal distribution.   Bayesian Analysis, 7(2):235–272, 2012.   [paper] [code] [matlab]
• J. Paisley, D. Blei, and M. Jordan.   Variational Bayesian inference with stochastic search.   International Conference on Machine Learning, 2012.   [paper]
• D. Mimno, M. Hoffman, and D. Blei.   Sparse stochastic inference for latent Dirichlet allocation.   International Conference on Machine Learning, 2012.   [paper] [code]
• S. Gershman, M. Hoffman, and D. Blei.   Nonparametric variational inference.   International Conference on Machine Learning, 2012.   [paper] [code]
• A. Chaney and D. Blei.   Visualizing topic models.   International AAAI Conference on Social Media and Weblogs, 2012.   [paper]
• J. Paisley, D. Blei, and M. Jordan.   Stick-breaking beta processes and the Poisson process.   Artificial Intelligence and Statistics, 2012.   [paper]
• S. Gershman and D. Blei.   A tutorial on Bayesian nonparametric models.   Journal of Mathematical Psychology, 56:1–12, 2012.   [paper]

2011

• S. Ghosh, A. Ungureunu, E. Sudderth, and D. Blei.   Spatial distance dependent Chinese restaurant processes for image segmentation.   Neural Information Processing Systems, 2011.   [paper]
• D. Blei. and P. Frazier.   Distance dependent Chinese restaurant processes.   Journal of Machine Learning Reseach, 12:2461–2488, 2011.   [paper] [code]
• L. Hannah, D. Blei, and W. Powell.   Dirichlet process mixtures of generalized linear models.   Journal of Machine Learning Research, 12:1923–1953.   [paper]
• C. Wang and D. Blei.   Collaborative topic modeling for recommending scientific articles. Knowledge Discovery and Data Mining, 2011.   (Best Student Paper Award)   [paper] [code]
• D. Mimno and D. Blei.   Bayesian checking of topic models.   Empirical Methods in Natural Language Processing, 2011.   [paper]
• S. Gershman, D. Blei, F. Pereira, and K. Norman.   A topographic latent source model for fMRI data.   NeuroImage, 57:89–100, 2011.
• J. Paisley, L. Carin, and D. Blei.   Variational inference for stick-breaking beta processes.   International Conference on Machine Learning, 2011.   [paper]
• S. Gerrish and D. Blei.   Predicting legislative roll calls from text.   International Conference on Machine Learning, 2011.   (Distinguished Application Paper Award)   [paper]
• J. Paisley, C. Wang, and D. Blei.   The discrete infinite logistic normal distribution for mixed-membership modeling.   Artificial Intelligence and Statistics, 2011.   (Notable Paper Award)   [paper] [code] [matlab]
• C. Wang, J. Paisley, and D. Blei.   Online variational inference for the hierarchical Dirichlet process.   Artificial Intelligence and Statistics , 2011. [paper] [code]

2010

• M. Hoffman, D. Blei, and F. Bach.   Online learning for latent Dirichlet allocation   Neural Information Processing Systems, 2010.   [paper] [supplement] [code]
• L. Hannah, W. Powell, and D. Blei.   Nonparametric density estimation for stochastic optimization with an observable state variable   Neural Information Processing Systems, 2010.   [paper] [supplement] [long paper]
• J. Chang and D. Blei.   Hierarchical relational models for document networks.   Annals of Applied Statistics, 4(1):124–150, 2010.   [paper] [code]
• D. Blei and P. Frazier.   Distance dependent Chinese restaurant processes.   International Conference on Machine Learning, 2010.   [paper] [long paper] [code]
• S. Gerrish and D. Blei.   A language-based approach to measuring scholarly impact.   International Conference on Machine Learning, 2010.   [paper]
• M. Hoffman, D. Blei, and P. Cook.   Bayesian nonparametric matrix factorization for recorded music.   International Conference on Machine Learning, 2010.   [paper]
• S. Williamson, C. Wang, K. Heller, and D. Blei.   The IBP compound Dirichlet process and its application to focused topic modeling.   International Conference on Machine Learning, 2010.   [paper]
• J. Li, C. Wang, Y. Lim, D. Blei, and L. Fei-Fei.   Building and using a semantivisual image hierarchy.   Computer Vision and Pattern Recognition, 2010.   [paper]
• S. Cohen, D. Blei, and N. Smith.   Variational inference for adaptor grammars.   North American Chapter of the Association for Computational Linguistics, 2010.   [paper]
• L. Hannah, D. Blei, and W. Powell.   Dirichlet process mixtures of generalized linear models. Artificial Intelligence and Statistics, 2010.   [paper]
• A. Lorbert, D. Eis, V. Kostina, D. Blei, and P. Ramadge.   Exploiting covariate similarity in sparse regression via the pairwise elastic net. Artificial Intelligence and Statistics, 2010.   [paper]
• D. Blei, T. Griffiths, and M. Jordan.   The nested Chinese restaurant process and Bayesian nonparametric inference of topic hierarchies.   Journal of the ACM, 57:2 1–30, 2010.   [paper] [code] [JACM abstracts]
• S. Gershman, D. Blei, and Y. Niv.   Context, Learning and Extinction   Psychological Review 117:1 197–209, 2010.   [paper]

2009

• J. Chang, J. Boyd-Graber, S. Gerrish, C. Wang, and D. Blei.   Reading tea leaves: How humans interpret topic models .   Neural Information Processing Systems, 2009.   [paper]
• C. Wang and D. Blei.   Decoupling sparsity and smoothness in the discrete hierarchical Dirichlet process.   Neural Information Processing Systems, 2009.   [paper] [supplement]
• C. Wang and D. Blei.   Variational inference for the nested Chinese restaurant process. Neural Information Processing Systems, 2009.   [paper]
• R. Socher, S. Gershman, A. Perotte, P. Sederberg, D. Blei, and K. Norman. A Bayesian analysis of dynamics in free recall. Neural Information Processing Systems, 2009.   [paper] [code and data]
• M. Hoffman, D. Blei, P. Cook.   Finding Latent Sources in Recorded Music With a Shift-Invariant HDP.   International Conference on Digital Audio Effects, 2009.   [paper]
• J. Boyd-Graber and D. Blei.   Multilingual topic models for unaligned text.   Uncertainty in Artificial Intelligence, 2009.   [paper]
• J. Chang, J. Boyd-Graber, and D. Blei.   Connections between the lines: Augmenting social networks with text.   Knowledge Discovery and Data Mining, 2009.   [paper] [code]
• C. Wang, D. Blei., and L. Fei-Fei.   Simultaneous image classification and annotation.   Computer Vision and Pattern Recognition, 2009.   [paper] [code]
• M. Hoffman, P. Cook, and D. Blei.   Bayesian spectral matching: Turning Young MC into MC Hammer via MCMC sampling International Computer Music Conference, 2009.   [paper]
• J. Chang and D. Blei.   Relational Topic Models for Document Networks . Artificial Intelligence and Statistics, 2009.   [paper] [long version]
• C. Wang, B. Thiesson, C. Meek, and D. Blei.   Markov topic models.   Artificial Intelligence and Statistics, 2009.   [paper]
• M. Hoffman, D. Blei, and P. Cook.   Easy as CBA: A simple probabilistic model for tagging music. International Conference on Music Information Retrieval, 2009.   (Best Student Paper Award)   [paper]
• D. Blei and J. Lafferty.   Topic Models.   In A. Srivastava and M. Sahami, editors, Text Mining: Classification, Clustering, and Applications . Chapman & Hall/CRC Data Mining and Knowledge Discovery Series, 2009.   [paper]

2008

• E. Airoldi, D. Blei, S. Fienberg, and E. Xing.   Mixed membership stochastic blockmodels.   Journal of Machine Learning Research , 9:1981--2014, 2008 .   [paper] [code] [Shorter version from NIPS 2008]
• I. Mukherjee and D. Blei.   Relative performance guarantees for approximate inference in latent Dirichlet allocation.   Neural Information Processing Systems, 2008.   [paper]
• J. Boyd-Graber and D. Blei.   Syntactic topic models.   Neural Information Processing Systems, 2008.   [paper] [supplement] [long version]
• M. Hoffman, D. Blei, and P. Cook. Content-based musical similarity computation using the hierarchical Dirichlet process. In International Conference on Music Information Retrieval, 2008. [paper]
• M. Hoffman, P. Cook, and D. Blei. Data-driven recomposition using the hierarchical Dirichlet process hidden Markov model. In International Computer Music Conference, 2008. [paper]
• C. Wang, D. Blei, and D. Heckerman. Continuous time dynamic topic models. In Uncertainty in Artificial Intelligence [UAI], 2008. [paper]

2007

• D. Blei, J. McAuliffe. Supervised topic models. Neural Information Processing Systems 21, 2007. [paper] [long version] [digg data] [code]
• J. Boyd-Graber, D. Blei, and X. Zhu. A topic model for word sense disambiguation. In Empirical Methods in Natural Language Processing, 2007. [paper]
• W. Li, D. Blei, and A. McCallum. Nonparametric Bayes pachinko allocation. In The 23rd Conference on Uncertainty in Artificial Intelligence, 2007. [paper]
• D. Kaplan and D. Blei. A computational approach to style in American poetry. In IEEE Conference on Data Mining, 2007.
• D. Blei and J. Lafferty. A correlated topic model of Science. Annals of Applied Statistics. 1:1 17–35, 2007. [paper] [code] [browser]
• M. Dudik, D. Blei, and R. Schapire. Hierarchical maximum entropy density estimation. Proceedings of the 24th International Conference on Machine Learning, 2007. [paper]

2006

• D. Blei and J. Lafferty. Dynamic topic models. In Proceedings of the 23rd International Conference on Machine Learning, 2006. [paper]
• D. Blei and J. Lafferty. Correlated Topic Models. Neural Information Processing Systems, 2006. [paper] [long version] [code]
• J. McAuliffe, D. Blei, and M. Jordan. Nonparametric empirical Bayes for the Dirichlet process mixture model. Statistics and Computing, 16[1]:5–14, 2006. [Springer] [TR paper] [R code]
• D. Blei and M. Jordan. Variational inference for Dirichlet process mixtures. Journal of Bayesian Analysis, 1[1]:121–144, 2006. [A shorter version appeared in ICML 2004]. [paper]
• Y. Teh, M. Jordan, M. Beal, and D. Blei. Hierarchical Dirichlet processes. Journal of the American Statistical Association, 2006. 101[476]:1566-1581. [paper] [matlab] [code]

Before 2006

• T. Griffiths, M. Steyvers, D. Blei, and J. Tenenbaum. Integrating topics and syntax. Neural Information Processing Systems 17, 2005. [paper]
• D. Blei. Probabilistic Models of Text and Images. PhD thesis, U.C. Berkeley, Division of Computer Science, 2004. [paper]
• D. Blei and M. Jordan. Modeling annotated data. In Proceedings of the 26th annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 127–134. ACM Press, 2003. [paper]
• D. Blei, T. Griffiths, M. Jordan, and J. Tenenbaum. Hierarchical topic models and the nested Chinese restaurant process. Neural Information Processing Systems 16, 2003. [paper]
• K. Barnard, P. Duygulu, N. de Freitas, D. Forsyth, D. Blei, and M. Jordan. Matching words and pictures. Journal of Machine Learning Research, 3:1107–1135, 2003. [paper]
• D. Blei, A. Ng, and M. Jordan. Hierarchical Bayesian models for applications in information retrieval. In J. Bernardo, J. Berger, A. Dawid, D. Heckerman, A. Smith, and M. West, editors, Bayesian Statistics 7, volume 7, pages 25–44. Oxford University Press, 2003.
• D. Blei, A. Ng, and M. Jordan. Latent Dirichlet allocation. Journal of Machine Learning Research, 3:993–1022, January 2003. [A shorter version appeared in NIPS 2002]. [paper] [code]
• D. Blei, J. Bagnell, and A. McCallum. Learning with scope, with application to information extraction and classification. In Uncertainty in Artificial Intelligence: Proceedings of the Eighteenth Conference [UAI-2002], pages 53–60, San Francisco, CA, 2002. Morgan Kaufmann Publishers.
• D. Blei and P. Moreno. Topic segmentation with an aspect hidden Markov model. In Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval, pages 343–348. ACM Press, 2001. [paper]
• D. Blei and L. Kaelbling. Shortest paths in a dynamic uncertain domain. In IJCAI Workshop on Adaptive Spatial Representations of Dynamic Environments, 1999. [paper]