David M. Blei

2025

Andrew Jesson, Nicolas Beltran-Velez, and David Blei. Can generative AI solve your in-context learning problem? A martingale perspective. In International Conference on Learning Representations. 2025. [paper]
S. Salazar, M. Kucer, Y. Wang, E. Casleton, and D. Blei. Posterior mean matching: Generative modeling through online Bayesian inference. In Artificial Intelligence and Statistics. 2025. [paper]
A. Nazaret, J. Fan, V. Lavallée, C. Burdziak, A. Cornish, V. Kiseliovas, R. Bowman, I. Masilionis, J. Chun, S. Eisman, J. Wang, J. Hong, L. Shi, R. Levine, L. Mazutis, D. Blei, D. Pe'er, and E. Azizi. Joint representation and visualization of derailed cell states with decipher. Genome Biology, 2025. [paper]
K. Vafa, S. Athey, and D. Blei. Estimating wage disparities using foundation models. Proceedings of the National Academy of Sciences, 2025. [paper]

2024

S. Salehi, A. Nazaret, S. Shah, and D. Blei. Population priors for matrix factorization. Transactions on Machine Learning Research, 2024. [paper]
C. Shi, N. Beltran-Velez, A. Nazaret, C. Zheng, A. Garriga-Alonso, A. Jesson, M. Makar, and D. Blei. Hypothesis testing the circuit hypothesis in LLMs. In Neural Information Processing Systems. 2024. [paper]
A. Jesson, N. Beltran Velez, Q. Chu, S. Karlekar, J. Kossen, Y. Gal, J. Cunningham, and D. Blei. Estimating the hallucination rate of generative AI. In Neural Information Processing Systems. 2024. [paper]
N. Beltran-Velez, A. Grande, A. Nazaret, A. Kucukelbir, and D. Blei. Treeffuser: Probabilistic prediction via conditional diffusions with gradient-boosted trees. In Neural Information Processing Systems. 2024. [paper]
D. Cai, C. Modi, C. Margossian, R. Gower, D. Blei, and L. Saul. EigenVI: Score-based variational inference with orthogonal function expansions. In Neural Information Processing Systems. 2024. [paper]
M. Yin, Y. Wang, and D. Blei. Optimization-based causal estimation from heterogenous environments. Journal of Machine Learning Research, 2024. [paper]
C. Margossian and D. Blei. Amortized variational inference: When and why. In Uncertainty in Artificial Intelligence. 2024. [paper]
A. Nazaret and D. Blei. Extremely greedy equivalence search. In Uncertainty in Artificial Intelligence. 2024. [paper]
A. Nazaret, J. Hong, E. Azizi, and D. Blei. Stable differentiable causal discovery. In International Conference on Machine Learning. 2024. [paper]
C. Bradshaw and D. Blei. A Bayesian model of underreporting for sexual assault on college campuses. Annals of Applied Statistics, 2024. [paper]
S. He, Y. Jin, A. Nazaret, L. Shi, X. Chen, S. Rampersaud, B. Dhillon, I. Valdez, L. Friend, J. Fan, Y. Park, R. Mintz, Y. Lao, D. Carrera, K. Fang, K. Mehdi, M. Rohde, J. McFaline-Figueroa, D. Blei, K. Leong, A. Rudensky, G. Plitas, and E. Azizi. Starfysh integrates spatial transcriptomic and histologic data to reveal heterogeneous tumor–immune hubs. Nature Biotechnology, 2024. URL: https://doi.org/10.1038/s41587-024-02173-8, doi:10.1038/s41587-024-02173-8. [paper]
D. Cai, C. Modi, L. Pillaud-Vivien, C. Margossian, R. Gower, D.. Blei, and L.. Saul. Batch and match: Black-box variational inference with a score-based divergence. In International Conference on Machine Learning. 2024. [paper]
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]
A. Nazaret, C. Shi, and D. Blei. On the misspecification of linear assumptions in synthetic controls. In Artificial Intelligence and Statistics. 2024. [paper]
Y. Park and D. Blei. Density uncertainty layers for reliable uncertainty estimation. In 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. In 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, 119(545):122–135, 2024. [paper]

2023

C. Zheng, K. Vafa, and D. Blei. Revisiting topic-guided language models. Transactions on Machine Learning Research, 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. URL: https://openreview.net/forum?id=7KW7zvKd7J. [paper]
J. von Kügelgen, M. Besserve, W. Liang, L. Gresele, A. Kekić, E. Bareinboim, D. Blei, and B. Schölkopf. Nonparametric identifiability of causal representations from unknown interventions. In 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. In Neural Information Processing Systems. 2023. [paper]
N. Scherrer, C. Shi, A. Feder, and D. Blei. Evaluating the moral beliefs encoded in LLMs. In Neural Information Processing Systems. 2023. [paper]
A. Feder, Y. Wald, C. Shi, S. Saria, and D. Blei. Causal-structure driven augmentations for text OOD generalization. In 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. In Neural Information Processing Systems. 2023. [paper]
C. Zheng, C. Shi, K. Vafa, A. Feder, and D. Blei. An invariant learning characterization of controlled text generation. In 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, 2023. [paper]
Z. Wang, R. Gao, M. Yin, M. Zhou, and D. Blei. Probabilistic conformal prediction using conditional random samples. In Artificial Intelligence and Statistics. 2023. [paper]
G. Moran, J. Cunningham, and D. Blei. The posterior predictive null. Bayesian Analysis, 18(4):194–214, 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, 2022. [paper]
D. Sridhar and D. Blei. Causal inference from text: A commentary. Science Advances, 2022. [paper]
G. Moran, D. Sridhar, Y. Wang, and D. Blei. Identifiable deep generative models via sparse decoding. Transactions on Machine Learning Research, 2022. URL: https://openreview.net/forum?id=vd0onGWZbE. [paper]
A. Nazaret and D. Blei. Variational inference for infinitely deep neural networks. International Conference on Machine Learning, 2022. [paper]
S. Menon, D. Blei, and C. Vondrick. Forget-me-not! Contrastive critics for mitigating posterior collapse. In Uncertainty in Artificial Intelligence. 2022. [paper]
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, 2022. [paper]
W. Tansey, V. Veitch, H. Zhang, R. Rabadan, and 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]
D. Sridhar, H. Daume, and D. Blei. Heterogeneous supervised topic models for text prediction. Transactions of the Association for Computational Linguistics, pages 732–745, 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(4):2672–2692, 2022. [paper]
D. Sridhar, C. De Bacco, and D. Blei. Estimating social influence from observational data. In Causal Learning and Reasoning. 2022. [paper]
C. Shi, D. Sridhar, V. Misra, and D. Blei. On the assumptions of synthetic control methods. In Artificial Intelligence and Statistics. 2022. [paper]
C. Shi, V. Veitch, and D. Blei. Invariant representation learning for treatment effect estimation. In Uncertainty in Artificial Intelligence. 2022. [paper]
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, 23(2):643–665, 2022. [paper]

2021

K. Vafa, Y. Deng, D. Blei, and A. Rush. Rationales for sequential predictions. In Empirical Methods in Natural Language Processing. 2021. [paper]
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]
R. Donnelly, F. Ruiz, D. Blei, and S. Athey. Counterfactual inference for consumer choice across many product categories. Quantitative Marketing and Economics, 19:369–407, 2021. [paper]
Y. Wang, D. Blei, and J. Cunningham. Posterior collapse and latent variable non-identifiability. In Neural Information Processing Systems. 2021. [paper]
L. Wu, A. Miller, L. Anderson, G. Pleiss, D. Blei, and J. Cunningham. Hierachical inducing point Gaussian process for inter-domian observations. In Artificial Intelligence and Statistics. 2021. [paper]
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. In The Web Conference, 2025–2036. 2021. [paper]
Y. Park, S. Lee, G. Kim, and D. Blei. Unsupervised representation learning via neural activation coding. In International Conference on Machine Learning. 2021. [paper]
Y. Wang and D. Blei. A proxy variable view of shared confounding. In International Conference on Machine Learning. 2021. [paper]
A. Moretti, L. Zhang, C. Naesseth, H. Venner, D. Blei, and I. Pe'er. Variational combinatorial sequential Monte Carlo methods for Bayesian phylogenetic inference. In Uncertainty in Artificial Intelligence. 2021. [paper]

2020

W. Tansey, Y. Wang, R. Rabadan, and D. Blei. Double empirical Bayes testing. International Statistical Review, 2020. [paper]
C. Naesseth, F. Lindsten, and D. Blei. Markovian score climbing: variational inference with KL(p || q). In Neural Information Processing Systems. 2020. [paper]
Y. Wang, D. Liang, L. Charlin, and D. Blei. Causal inference for recommender systems. In ACM Conference on Recommender Systems. 2020. [paper]
K. Vafa, S. Naidu, and D. Blei. Text-based ideal points. In Association for Computational Linguistics. 2020. [paper]
V. Veitch, D. Sridhar, and D. Blei. Adapting text embeddings for causal inference. In Uncertainty in Artificial Intelligence. 2020. [paper]
A. Dieng, F. Ruiz, and D. Blei. Topic modeling in embedding spaces. Transactions of the Association for Computational Linguistics, 8:439–453, 2020. [paper]
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. URL: https://doi.org/10.1214/19-AOAS1265, doi:10.1214/19-AOAS1265. [paper]

2019

Y. Wang and D. Blei. The blessings of multiple causes: rejoinder. Journal of the American Statistical Association, 114(528):1616–1619, 2019. URL: https://doi.org/10.1080/01621459.2019.1690841, arXiv:https://doi.org/10.1080/01621459.2019.1690841, doi:10.1080/01621459.2019.1690841. [paper]
Y. Wang and D. Blei. Variational Bayes under model misspecification. In Neural Information Processing Systems. 2019. [paper]
A. Schein, S. Linderman, M. Zhou, D. Blei, and H. Wallach. Poisson-randomized gamma dynamical systems. In Neural Information Processing Systems. 2019. [paper]
V. Veitch, Y. Wang, and D. Blei. Using embeddings to correct for unobserved confounding in networks. In Neural Information Processing Systems. 2019. [paper]
C. Shi, D. Blei, and V. Veitch. Adapting neural networks for the estimation of treatment effects. In Neural Information Processing Systems. 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]
Y. Wang, A Miller, and D. Blei. Comment: Variational autoencoders as empirical Bayes. Statistical Science, 34(2):229–233, 2019. doi:10.1214/19-STS710. [paper]
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, 2019. [paper]
V. Veitch, M Austern, W Zhou, D. Blei, and P. Orbanz. Empirical risk minimization and stochastic gradient descent for relational data. In Artificial Intelligence and Statistics. 2019. [paper]
A. Dieng, Y. Kim, A. Rush, and D. Blei. Avoiding latent variable collapse with generative skip models. In Artificial Intelligence and Statistics. 2019. [paper]
Y. Wang and D. Blei. The blessings of multiple causes. Journal of the American Statistical Association, 114(528):1574–1596, 2019. [paper]

2018

S. Linderman and D. Blei. A discussion of "Nonparametric Bayes modeling of populations of networks.". Journal of the American Statistical Association, 2018. [paper]
R. Ranganath and D. Blei. Correlated random measures. Journal of the American Statistical Association, 113(521):417–430, 2018. URL: https://doi.org/10.1080/01621459.2016.1260468, arXiv:https://doi.org/10.1080/01621459.2016.1260468, doi:10.1080/01621459.2016.1260468. [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 estimating heterogeneous consumer preferences for restaurants and travel time using mobile location data. AEA Papers and Proceedings, 108:64–67, 2018. [paper]
W. Tansey, Y. Wang, D. Blei, and R. Rabadan. Black box FDR. In International Conference on Machine Learning. 2018. [paper]
A. Dieng, R. Ranganath, J. Altosaar, and D. Blei. Noisin: Unbiased regularization for recurrent neural networks. In 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. In International Conference on Machine Learning. 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]
M. Rudolph and D. Blei. Dynamic embeddings for language evolution. In International World Wide Web Conference. 2018. [paper]
J. Altosaar, R. Ranganath, and D. Blei. Proximity variational inference. In Artificial Intelligence and Statistics. 2018. [paper]
C. Naesseth, S. Linderman, R. Ranganath, and D. Blei. Variational sequential Monte Carlo. In Artificial Intelligence and Statistics. 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, 2018. [paper]
C. Wang and D. Blei. A general method for robust Bayesian modeling. Bayesian Analysis, 13(4):1163–1191, 2018. [paper]

2017

L. Liu and D. Blei. Zero-inflated exponential family embeddings. In International Conference on Machine Learning. 2017. [paper]
M. Rudolph, F. Ruiz, S. Athey, and D. Blei. Structured embedding models for grouped data. In Neural Information Processing Systems. 2017. [paper]
A. Dieng, D. Tran, R. Ranganath, J. Paisley, and D. Blei. Variational inference via χ upper bound minimization. In Neural Information Processing Systems. 2017. [paper]
L. Liu, F. Ruiz, S. Athey, and D. Blei. Context selection for embedding models. In Neural Information Processing Systems. 2017. [paper]
D. Tran, R. Ranganath, and D. Blei. Hierararchical implicit models and likelihood-free variational inference. In Neural Information Processing Systems. 2017. [paper]
A. Kucukelbir, Y. Wang, and D. Blei. Evaluating Bayesian models with posterior dispersion indices. In International Conference on Machine Learning, 1925–1934. 2017. URL: http://proceedings.mlr.press/v70/kucukelbir17a.html. [paper]
Y. Wang, A. Kucukelbir, and D. Blei. Robust probabilistic modeling with Bayesian data reweighting. In International Conference on Machine Learning. 2017. URL: http://proceedings.mlr.press/v70/wang17g.html. [paper]
D. Blei and P. Smyth. Science and data science. Proceedings of the National Academy of Sciences, 114(33):8689–8692, 2017. [paper]
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]
D. Tran and D. Blei. Comment on “Fast approximate inference for arbitrarily large semiparametric regression models via message passing”. Journal of the American Statistical Association, 112(517):156–158, 2017. URL: http://dx.doi.org/10.1080/01621459.2016.1270044, arXiv:http://dx.doi.org/10.1080/01621459.2016.1270044, doi:10.1080/01621459.2016.1270044. [paper]
S. Linderman, M. Johnson, A. Miller, R. Adams, D. Blei, and L. Paninski. Bayesian learning and inference in recurrent switching linear dynamical systems. In Artificial Intelligence and Statistics. 2017. [paper]
S. Mandt, M. Hoffman, and D. Blei. Stochastic gradient descent as approximate Bayesian inference. Journal of Machine Learning Research, 18:1–35, 2017. [paper]
C. Naesseth, F. Ruiz, S. Linderman, and D. Blei. Reparameterization gradients through acceptance-rejection sampling algorithms. In Artificial Intelligence and Statistics. 2017. [paper]
D. Tran, M. Hoffman, R. Sauraus, E. Brevdo, K. Murphy, and D. Blei. Deep probabilistic programming. In International Conference on Learning Representations. 2017. [paper]
D. Blei, A. Kucukelbir, and J. McAuliffe. Variational inference: A review for statisticians. Journal of American Statistical Association, 112(518):859–877, 2017. [paper]

2016

D. Blei. Comment on "Improving and evaluating topic models and other models of text". Journal of the American Statistical Association, 111(516):1408–1410, 2016. URL: http://dx.doi.org/10.1080/01621459.2016.1245071, arXiv:http://dx.doi.org/10.1080/01621459.2016.1245071, doi:10.1080/01621459.2016.1245071. [paper]
R. Ranganath, A. Perotte, N. Elhadad, and D. Blei. Deep survival analysis. Machine Learning for Health Care, 2016. [paper]
R. Ranganath, J. Altosaar, D. Tran, and D. Blei. Operator variational inference. In Neural Information Processing Systems. 2016. [paper]
M. Rudolph, F. Ruiz, S. Mandt, and D. Blei. Exponential family embeddings. In Neural Information Processing Systems. 2016. [paper]
F. Ruiz, M. Titsias, and D. Blei. The generalized reparameterization gradient. In Neural Information Processing Systems. 2016. [paper]
A. Chaney, H. Wallach, M. Connelly, and D. Blei. Detecting and characterizing events. In 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. In ACM Conference on Recommendation Systems. 2016. [paper]
F. Ruiz, M. Titsias, and D. Blei. Overdispersed black-box variational inference. In Uncertainty in Artificial Intelligence. 2016. [paper]
A. Schein, M. Zhou, D. Blei, and H. Wallach. Bayesian Poisson Tucker decomposition for learning the structure of international relations. In International Conference on Machine Learning. 2016. [paper]
R. Ranganath, D. Tran, and D. Blei. Hierarchical variational models. In International Conference on Machine Learning. 2016. [paper]
S. Mandt, M. Hoffman, and D. Blei. A variational analysis of stochastic gradient algorithms. In International Conference on Machine Learning. 2016. [paper]
S. Mandt, J. McInerney, F. Abrol, R. Ranganath, and D. Blei. Variational tempering. In Artificial Intelligence and Statistics. 2016. [paper]
D. Tran, R. Ranganath, and D. Blei. The variational Gaussian process. In International Conference on Learning Representations. 2016. [paper]
M. Rudolph, J. Ellis, and D. Blei. Objective variables for probabilistic revenue maximization in second-price auctions with reserve. In International Conference on World Wide Web. 2016. [paper]
D. Liang, L. Charlin, J. McInerney, and D. Blei. Modeling user exposure in recommendation. In International Conference on World Wide Web. 2016. [paper]
P. Gopalan, W. Hao, D. Blei, and J. Storey. Scaling probabilistic models of genetic variation to millions of humans. Nature Genetics, 48:1587–1590, 2016. [paper]

2015

J. McInerney, R. Ranganath, and D. Blei. The population posterior and Bayesian modeling on streams. In Neural Information Processing Systems. 2015. [paper]
D. Tran, D. Blei, and E. Airoldi. Copula variational inference. In Neural Information Processing Systems. 2015. [paper]
A. Kucukelbir, R. Ranganath, A. Gelman, and D. Blei. Automatic variational inference in Stan. In Neural Information Processing Systems. 2015. [paper]
L. Charlin, R. Ranganath, J. McInerney, and D. Blei. Dynamic Poisson factorization. In 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. In ACM Conference on Recommendation Systems. 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, 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, 2015. [paper]
A. Schein, J. Paisley, D. Blei, and H. Wallach. Bayesian Poisson tensor factorization for inferring multilateral relations from sparse dyadic event counts. In Knowledge Discovery and Data Mining, 1045–1054. 2015. [paper]
S. Gershman, P. Frazier, and D. Blei. Distance dependent infinite latent feature models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015. [paper]
J. Paisley, C. Wang, D. Blei, and M. Jordan. Nested hierarchical Dirichlet processes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015. [paper]
A. Kucukelbir and D. Blei. Population empirical Bayes. In 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. In Uncertainty in Artificial Intelligence. 2015. [paper]
M. Hoffman and D. Blei. Structured stochastic variational inference. In Artificial Intelligence and Statistics. 2015. [paper]
R. Ranganath, L. Tang, L. Charlin, and D. Blei. Deep exponential families. In Artificial Intelligence and Statistics. 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, 2015. [paper]

2014

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

2013

P. DiMaggio, M. Nag, and D. Blei. Exploiting affinities between topic modeling and the sociological perspective on culture: application to newspaper coverage of us government arts funding. Poetics, 41(6):570–606, 2013. [paper]
B. Chen, G. PolatkSan, G. Sapiro, D. Blei, D. Dunson, and L. Carin. Deep learning with hierarchical convolutional factor analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(8):1887–1901, 2013. [paper]
D. Kim, P. Gopalan, D. Blei, and E. Sudderth. Efficient online inference for bayesian nonparametric relational models. In Neural Information Processing Systems. 2013. [paper]
P. Gopalan, C. Wang, and D. Blei. Modeling overlapping communities with node popularities. In Neural Information Processing Systems. 2013. [paper]
D. Blei. Topic modeling and digital humanities. Journal of Digital Humanities, 2013. URL: https://journalofdigitalhumanities.org/2-1/topic-modeling-and-digital-humanities-by-david-m-blei/.
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]
R. Ranganath, C. Wang, D. Blei, and E. Xing. An adaptive learning rate for stochastic variational inference. In International Conference on Machine Learning. 2013. [paper]
C. Wang and D. Blei. Variational inference in nonconjugate models. Journal of Machine Learning Research, 14:1005–1031, 2013. [paper]
M. Hoffman, D. Blei, C. Wang, and J. Paisley. Stochastic variational inference. Journal of Machine Learning Research, 14:1303–1347, 2013. [paper]

2012

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

2011

S. Ghosh, A. Ungureanu, E. Sudderth, and D. Blei. Spatial distance dependent Chinese restaurant processes for image segmentation. In Neural Information Processing Systems. 2011. [paper]
D. Blei and P. Frazier. Distance dependent Chinese restaurant processes. Journal of Machine Learning Research, 12:2461–2488, 2011. [paper]
D. Mimno and D. Blei. Bayesian checking for topic models. In Empirical Methods in Natural Language Processing, 227–237. 2011. [paper]
C. Wang and D. Blei. Collaborative topic modeling for recommending scientific articles. In Knowledge Discovery and Data Mining. 2011. [paper]
L. Hannah, D. Blei, and W. Powell. Dirichlet process mixtures of generalized linear models. Journal of Machine Learning Research, 12:1923–1953, 2011. [paper]
S. Gershman, D. Blei, F. Pereira, and K. Norman. A topographic latent source model for fMRI data. NeuroImage, 57:89–100, 2011. [paper]
C. Wang, J. Paisley, and D. Blei. Online variational inference for the hierarchical Dirichlet process. In Artificial Intelligence and Statistics. 2011. [paper]
S. Gerrish and D. Blei. Predicting legislative roll calls from text. In International Conference on Machine Learning. 2011. [paper]
J. Paisley, C. Wang, and D. Blei. The discrete infinite logistic normal distribution for mixed-membership modeling. In Artificial Intelligence and Statistics. 2011. [paper]
J. Paisley, L. Carin, and D. Blei. Variational inference for stick-breaking beta processes. In International Conference on Machine Learning. 2011. [paper]

2010

D. Blei, L. Carin, and D. Dunson. Probabilistic topic models. Signal Processing, 27(6):55–65, 2010. [paper]
L. Hannah, W. Powell, and D. Blei. Nonparametric density estimation for stochastic optimization with an observable state variable. In Neural Information Processing Systems. 2010. [paper]
M. Hoffman, D. Blei, and F. Bach. On-line learning for latent Dirichlet allocation. In Neural Information Processing Systems. 2010. [paper]
S. Williamson, C. Wang, K. Heller, and D. Blei. The IBP compound Dirichlet process and its application to focused topic modeling. In International Conference on Machine Learning. 2010. [paper]
M. Hoffman, D. Blei, and P. Cook. Bayesian nonparametric matrix factorization for recorded music. In International Conference on Machine Learning. 2010. [paper]
D. Blei and P. Frazier. Distance dependent Chinese restaurant processes. In International Conference on Machine Learning. 2010. [paper]
S. Gerrish and D. Blei. A language-based approach to measuring scholarly impact. In 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. In Computer Vision and Pattern Recognition. 2010. [paper]
S. Cohen, D. Blei, and N. Smith. Variational inference for adaptor grammars. In 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. In 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. In Artificial Intelligence and Statistics. 2010. [paper]
J. Chang and D. Blei. Hierarchical relational models for document networks. Annals of Applied 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]
S. Gershman, D. Blei, and Y. Niv. Context, learning and extinction. Psychological Review, 117(1):197–209, 2010. [paper]

2009

C. Wang and D. Blei. Variational inference for the nested Chinese restaurant process. In Neural Information Processing Systems. 2009. [paper]
C. Wang and D. Blei. Decoupling sparsity and smoothness in the discrete hierarchical dirichlet process. In Neural Information Processing Systems. 2009. [paper]
M. Hoffman, P. Cook, and D. Blei. Bayesian spectral matching: Turning Young MC into MC Hammer via MCMC sampling. In International Computer Music Conference. 2009. [paper]
M. Hoffman, D. Blei, and P. Cook. Easy as CBA: A simple probabilistic model for tagging music. In International Conference on Music Information Retrieval. 2009. [paper]
M. Hoffman, D. Blei, and P. Cook. Finding latent sources in recorded music with a shift-invariant HDP. In International Conference on Digital Audio Effects. 2009. [paper]
R. Socher, S. Gershman, A. Perotte, P. Sederberg, D. Blei, and K. Norman. A Bayesian analysis of dynamics in free recall. In Neural Information Processing Systems. 2009. [paper]
J. Chang, J. Boyd-Graber, C. Wang, S. Gerrish, and D. Blei. Reading tea leaves: How humans interpret topic models. In Neural Information Processing Systems. 2009. [paper]
C. Wang and D. Blei. Decoupling sparsity and smoothness in the discrete hierarchical Dirichlet process. In Neural Information Processing Systems. 2009. [paper]
J. Boyd-Graber and D. Blei. Multilingual topic models for unaligned text. In Uncertainty in Artificial Intelligence. 2009. [paper]
J. Chang, J. Boyd-Graber, and D. Blei. Connections between the lines: Augmenting social networks with text. In Knowledge Discovery and Data Mining. 2009. [paper]
C. Wang, D. Blei, and F. Li. Simultaneous image classification and annotation. In Computer Vision and Pattern Recognition, 1903–1910. 2009. [paper]
J. Chang and D. Blei. Relational topic models for document networks. In Artificial Intelligence and Statistics. 2009. [paper]
C. Wang, B. Thiesson, C. Meek, and D. Blei. Markov topic models. In Artificial Intelligence and Statistics. 2009. [paper]
D. Blei and J. Lafferty. Topic models. In A. Srivastava and M. Sahami, editors, Text Mining: Theory and Applications, pages 71–116. Taylor and Francis, 2009. [paper]
E. Airoldi, D. Blei, S. Fienberg, and E. Xing. Mixed membership stochastic blockmodels. In Neural Information Processing Systems. 2009. [paper]
J. Boyd-Graber and D. Blei. Syntactic topic models. In Neural Information Processing Systems. 2009. [paper]
I. Mukherjee and D. Blei. Relative performance guarantees for approximate inference in latent Dirichlet allocation. In Neural Information Processing Systems. 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]
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

J. Boyd-Graber, D. Blei, and X. Zhu. A topic model for word sense disambiguation. In Empirical Methods in Natural Language Processing. 2007. [paper]
D. Kaplan and D. Blei. A computational approach to style in American poetry. In IEEE Conference on Data Mining. 2007. [paper]
D. Blei and J. McAuliffe. Supervised topic models. In Neural Information Processing Systems, 121–128. 2007. [paper]
D. Blei and S. Fienberg. Discussion of model-based clustering for social networks. Journal of the Royal Statistical Society, Series A, 170:332, 2007.
W. Li, D. Blei, and A. McCallum. Nonparametric Bayes pachinko allocation. In Uncertainty in Artificial Intelligence. 2007. [paper]
D. Blei and J. Lafferty. A correlated topic model of Science. Annals of Applied Statistics, 1(1):17–35, 2007. [paper]
M. Dudik, D. Blei, and R. Schapire. Hierarchical maximum entropy density estimation. In International Conference on Machine Learning. 2007. [paper]
E. Airoldi, D. Blei, S. Fienberg, and E. Xing. Combining stochastic block models and mixed membership for statistical network analysis. In Statistical Network Analysis: Models, Issues and New Directions, Lecture Notes in Computer Science, pages 57–74. Springer-Verlag, 2007. [paper]

2006

Y. Teh, M. Jordan, M. Beal, and D. Blei. Hierarchical Dirichlet processes. Journal of the American Statistical Association, 101(476):1566–1581, 2006. [paper]
D. Blei, K. Franks, M. Jordan, and S. Mian. Statistical modeling of biomedical corpora: Mining the Caenorhabditis Genetic Center Bibliography for genes related to life span. BMC Bioinformatics, 2006. [paper]
D. Blei and J. Lafferty. Dynamic topic models. In International Conference on Machine Learning. 2006. [paper]
D. Blei and J. Lafferty. Correlated topic models. In Neural Information Processing Systems. 2006. [paper]
D. Blei and M. Jordan. Variational inference for Dirichlet process mixtures. Journal of Bayesian Analysis, 1(1):121–144, 2006. [paper]
J. McAuliffe, D. Blei, and M. Jordan. Nonparametric empirical Bayes for the Dirichlet process mixture model. Statistics and Computing, 16(1):5–14, 2006. [paper]

2005

T. Griffiths, M. Steyvers, D. Blei, and J. Tenenbaum. Integrating topics and syntax. In Neural Information Processing Systems. 2005. [paper]

2004

D. Blei and M. Jordan. Variational methods for the Dirichlet process. In International Conference on Machine Learning. 2004. [paper]
D. Blei. Probabilistic Models of Text and Images. PhD thesis, U.C. Berkeley, Division of Computer Science, 2004. [paper]

2003

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.
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. Latent Dirichlet allocation. Journal of Machine Learning Research, 3:993–1022, January 2003. [paper]
D. Blei and M. Jordan. Modeling annotated data. In ACM SIGIR Conference on Research and Development in Information Retrieval, 127–134. ACM Press, 2003. [paper]
D. Blei, T. Griffiths, M. Jordan, and J. Tenenbaum. Hierarchical topic models and the nested Chinese restaurant process. In Neural Information Processing Systems. 2003. [paper]

2002

D. Blei, J. Bagnell, and A. McCallum. Learning with scope, with application to information extraction and classification. In Uncertainty in Artificial Intelligence, 53–60. San Francisco, CA, 2002. Morgan Kaufmann Publishers. [paper]

2001

D. Blei and P. Moreno. Topic segmentation with an aspect hidden Markov model. In ACM SIGIR Conference on Research and Development in Information Retrieval, 343–348. ACM Press, 2001. [paper]
D. Blei, A. Ng, and M. Jordan. Latent Dirichlet allocation. In T. G. Dietterich, S. Becker, and Z. Ghahramani, editors, Neural Information Processing Systems. Cambridge, MA, 2001. MIT Press. [paper]

1999

D. Blei and L. Kaelbling. Shortest paths in a dynamic uncertain domain. In IJCAI Workshop on Adaptive Spatial Representations of Dynamic Environments. 1999. [paper]