| 2025 | AISTATS | Posterior Mean Matching: Generative Modeling through Online Bayesian Inference. | Sebastian Salazar, Michal Kucer, Yixin Wang, Emily M. Casleton, David M. Blei |
| 2025 | ICLR | Can Generative AI Solve Your In-Context Learning Problem? A Martingale Perspective. | Andrew Jesson, Nicolas Beltran-Velez, David M. Blei |
| 2025 | UAI | HDP-Flow: Generalizable Bayesian Nonparametric Model for Time Series State Discovery. | Sana Tonekaboni, Tina Behrouzi, Addison Weatherhead, Emily B. Fox, David M. Blei, Anna Goldenberg |
| 2024 | AISTATS | On the Misspecification of Linear Assumptions in Synthetic Controls. | Achille O. R. Nazaret, Claudia Shi, David M. Blei |
| 2024 | AISTATS | Density Uncertainty Layers for Reliable Uncertainty Estimation. | Yookoon Park, David M. Blei |
| 2024 | ICML | Batch and match: black-box variational inference with a score-based divergence. | Diana Cai, Chirag Modi, Loucas Pillaud-Vivien, Charles Margossian, Robert M. Gower, David M. Blei, Lawrence K. Saul |
| 2024 | ICML | Stable Differentiable Causal Discovery. | Achille Nazaret, Justin Hong, Elham Azizi, David M. Blei |
| 2024 | UAI | Amortized Variational Inference: When and Why? | Charles C. Margossian, David M. Blei |
| 2024 | UAI | Extremely Greedy Equivalence Search. | Achille Nazaret, David M. Blei |
| 2023 | ACL | An Invariant Learning Characterization of Controlled Text Generation. | Carolina Zheng, Claudia Shi, Keyon Vafa, Amir Feder, David M. Blei |
| 2023 | AISTATS | Probabilistic Conformal Prediction Using Conditional Random Samples. | Zhendong Wang, Ruijiang Gao, Mingzhang Yin, Mingyuan Zhou, David M. Blei |
| 2022 | AISTATS | On the Assumptions of Synthetic Control Methods. | Claudia Shi, Dhanya Sridhar, Vishal Misra, David M. Blei |
| 2022 | ICML | Variational Inference for Infinitely Deep Neural Networks. | Achille Nazaret, David M. Blei |
| 2022 | UAI | Forget-me-not! Contrastive critics for mitigating posterior collapse. | Sachit Menon, David M. Blei, Carl Vondrick |
| 2021 | AISTATS | Hierarchical Inducing Point Gaussian Process for Inter-domian Observations. | Luhuan Wu, Andrew Miller, Lauren Anderson, Geoff Pleiss, David M. Blei, John P. Cunningham |
| 2021 | EMNLP | Rationales for Sequential Predictions. | Keyon Vafa, Yuntian Deng, David M. Blei, Alexander M. Rush |
| 2021 | ICML | Unsupervised Representation Learning via Neural Activation Coding. | Yookoon Park, Sangho Lee, Gunhee Kim, David M. Blei |
| 2021 | ICML | A Proxy Variable View of Shared Confounding. | Yixin Wang, David M. Blei |
| 2021 | WWW | Assessing the Effects of Friend-to-Friend Texting onTurnout in the 2018 US Midterm Elections. | Aaron Schein, Keyon Vafa, Dhanya Sridhar, Victor Veitch, Jeffrey Quinn, James Moffet, David M. Blei, Donald P. Green |
| 2021 | UAI | variational combinatorial sequential monte carlo methods for bayesian phylogenetic inference. | Antonio Khalil Moretti, Liyi Zhang, Christian A. Naesseth, Hadiah Venner, David M. Blei, Itsik Pe'er |
| 2021 | UAI | Invariant representation learning for treatment effect estimation. | Claudia Shi, Victor Veitch, David M. Blei |
| 2020 | ACL | Text-Based Ideal Points. | Keyon Vafa, Suresh Naidu, David M. Blei |
| 2020 | AMIA | Causal Inference from Observational Healthcare Data: Implications, Impacts and Innovations. | George Hripcsak, David M. Blei, Elias Bareinboim, Martijn J. Schuemie, Linying Zhang |
| 2020 | AMIA | The Multi-Outcome Medical Deconfounder: Assessing Treatment Effect on Multiple Renal Measures. | Linying Zhang, Yixin Wang, Anna Ostropolets, Ruijun Chen, David M. Blei, George Hripcsak |
| 2020 | RecSys | Causal Inference for Recommender Systems. | Yixin Wang, Dawen Liang, Laurent Charlin, David M. Blei |
| 2020 | UAI | Adapting Text Embeddings for Causal Inference. | Victor Veitch, Dhanya Sridhar, David M. Blei |
| 2019 | AISTATS | Avoiding Latent Variable Collapse with Generative Skip Models. | Adji B. Dieng, Yoon Kim, Alexander M. Rush, David M. Blei |
| 2019 | AISTATS | Empirical Risk Minimization and Stochastic Gradient Descent for Relational Data. | Victor Veitch, Morgane Austern, Wenda Zhou, David M. Blei, Peter Orbanz |
| 2018 | AISTATS | Proximity Variational Inference. | Jaan Altosaar, Rajesh Ranganath, David M. Blei |
| 2018 | AISTATS | Variational Sequential Monte Carlo. | Christian A. Naesseth, Scott W. Linderman, Rajesh Ranganath, David M. Blei |
| 2018 | ICLR | Implicit Causal Models for Genome-wide Association Studies. | Dustin Tran, David M. Blei |
| 2018 | ICML | Noisin: Unbiased Regularization for Recurrent Neural Networks. | Adji Bousso Dieng, Rajesh Ranganath, Jaan Altosaar, David M. Blei |
| 2018 | ICML | Augment and Reduce: Stochastic Inference for Large Categorical Distributions. | Francisco J. R. Ruiz, Michalis K. Titsias, Adji B. Dieng, David M. Blei |
| 2018 | ICML | Black Box FDR. | Wesley Tansey, Yixin Wang, David M. Blei, Raul Rabadan |
| 2018 | WWW | Dynamic Embeddings for Language Evolution. | Maja Rudolph, David M. Blei |
| 2017 | AISTATS | Bayesian Learning and Inference in Recurrent Switching Linear Dynamical Systems. | Scott W. Linderman, Matthew J. Johnson, Andrew C. Miller, Ryan P. Adams, David M. Blei, Liam Paninski |
| 2017 | AISTATS | Reparameterization Gradients through Acceptance-Rejection Sampling Algorithms. | Christian A. Naesseth, Francisco J. R. Ruiz, Scott W. Linderman, David M. Blei |
| 2017 | ICLR | Deep Probabilistic Programming. | Dustin Tran, Matthew D. Hoffman, Rif A. Saurous, Eugene Brevdo, Kevin Murphy, David M. Blei |
| 2017 | ICML | Evaluating Bayesian Models with Posterior Dispersion Indices. | Alp Kucukelbir, Yixin Wang, David M. Blei |
| 2017 | ICML | Zero-Inflated Exponential Family Embeddings. | Li-Ping Liu, David M. Blei |
| 2017 | ICML | Robust Probabilistic Modeling with Bayesian Data Reweighting. | Yixin Wang, Alp Kucukelbir, David M. Blei |
| 2016 | AISTATS | Variational Tempering. | Stephan Mandt, James McInerney, Farhan Abrol, Rajesh Ranganath, David M. Blei |
| 2016 | EMNLP | Detecting and Characterizing Events. | Allison June-Barlow Chaney, Hanna M. Wallach, Matthew Connelly, David M. Blei |
| 2016 | ICML | A Variational Analysis of Stochastic Gradient Algorithms. | Stephan Mandt, Matthew D. Hoffman, David M. Blei |
| 2016 | ICML | Hierarchical Variational Models. | Rajesh Ranganath, Dustin Tran, David M. Blei |
| 2016 | ICML | Bayesian Poisson Tucker Decomposition for Learning the Structure of International Relations. | Aaron Schein, Mingyuan Zhou, David M. Blei, Hanna M. Wallach |
| 2016 | RecSys | Factorization Meets the Item Embedding: Regularizing Matrix Factorization with Item Co-occurrence. | Dawen Liang, Jaan Altosaar, Laurent Charlin, David M. Blei |
| 2016 | WWW | Modeling User Exposure in Recommendation. | Dawen Liang, Laurent Charlin, James McInerney, David M. Blei |
| 2016 | WWW | Objective Variables for Probabilistic Revenue Maximization in Second-Price Auctions with Reserve. | Maja R. Rudolph, Joseph G. Ellis, David M. Blei |
| 2016 | UAI | Overdispersed Black-Box Variational Inference. | Francisco J. R. Ruiz, Michalis K. Titsias, David M. Blei |
| 2015 | AISTATS | Stochastic Structured Variational Inference. | Matthew D. Hoffman, David M. Blei |
| 2015 | AISTATS | Deep Exponential Families. | Rajesh Ranganath, Linpeng Tang, Laurent Charlin, David M. Blei |
| 2015 | KDD | Bayesian Poisson Tensor Factorization for Inferring Multilateral Relations from Sparse Dyadic Event Counts. | Aaron Schein, John W. Paisley, David M. Blei, Hanna M. Wallach |
| 2015 | RecSys | A Probabilistic Model for Using Social Networks in Personalized Item Recommendation. | Allison June-Barlow Chaney, David M. Blei, Tina Eliassi-Rad |
| 2015 | RecSys | Dynamic Poisson Factorization. | Laurent Charlin, Rajesh Ranganath, James McInerney, David M. Blei |
| 2015 | UAI | Scalable Recommendation with Hierarchical Poisson Factorization. | Prem Gopalan, Jake M. Hofman, David M. Blei |
| 2015 | UAI | Population Empirical Bayes. | Alp Kucukelbir, David M. Blei |
| 2015 | UAI | The Survival Filter: Joint Survival Analysis with a Latent Time Series. | Rajesh Ranganath, Adler J. Perotte, Nomie Elhadad, David M. Blei |
| 2014 | AISTATS | Bayesian Nonparametric Poisson Factorization for Recommendation Systems. | Prem Gopalan, Francisco J. R. Ruiz, Rajesh Ranganath, David M. Blei |
| 2014 | AISTATS | Black Box Variational Inference. | Rajesh Ranganath, Sean Gerrish, David M. Blei |
| 2014 | ICML | The Inverse Regression Topic Model. | Maxim Rabinovich, David M. Blei |
| 2013 | ICML | An Adaptive Learning Rate for Stochastic Variational Inference. | Rajesh Ranganath, Chong Wang, David M. Blei, Eric P. Xing |
| 2012 | ICML | Nonparametric variational inference. | Samuel Gershman, Matthew D. Hoffman, David M. Blei |
| 2012 | ICML | Sparse stochastic inference for latent Dirichlet allocation. | David M. Mimno, Matthew D. Hoffman, David M. Blei |
| 2012 | ICML | Variational Bayesian Inference with Stochastic Search. | John W. Paisley, David M. Blei, Michael I. Jordan |
| 2012 | ICWSM | Visualizing Topic Models. | Allison June-Barlow Chaney, David M. Blei |
| 2011 | EMNLP | Bayesian Checking for Topic Models. | David M. Mimno, David M. Blei |
| 2011 | ICML | Predicting Legislative Roll Calls from Text. | Sean Gerrish, David M. Blei |
| 2011 | ICML | Variational Inference for Stick-Breaking Beta Process Priors. | John W. Paisley, Lawrence Carin, David M. Blei |
| 2011 | KDD | Collaborative topic modeling for recommending scientific articles. | Chong Wang, David M. Blei |
| 2010 | CVPR | Building and using a semantivisual image hierarchy. | Li-Jia Li, Chong Wang, Yongwhan Lim, David M. Blei, Li Fei-Fei |
| 2010 | ICML | Distance dependent Chinese restaurant processes. | David M. Blei, Peter I. Frazier |
| 2010 | ICML | A Language-based Approach to Measuring Scholarly Impact. | Sean Gerrish, David M. Blei |
| 2010 | ICML | Bayesian Nonparametric Matrix Factorization for Recorded Music. | Matthew D. Hoffman, David M. Blei, Perry R. Cook |
| 2010 | ICML | The IBP Compound Dirichlet Process and its Application to Focused Topic Modeling. | Sinead Williamson, Chong Wang, Katherine A. Heller, David M. Blei |
| 2010 | NAACL | Variational Inference for Adaptor Grammars. | Shay B. Cohen, David M. Blei, Noah A. Smith |
| 2009 | CVPR | Simultaneous image classification and annotation. | Chong Wang, David M. Blei, Li Fei-Fei |
| 2009 | KDD | Connections between the lines: augmenting social networks with text. | Jonathan D. Chang, Jordan L. Boyd-Graber, David M. Blei |
| 2009 | UAI | Multilingual Topic Models for Unaligned Text. | Jordan L. Boyd-Graber, David M. Blei |
| 2008 | UAI | Continuous Time Dynamic Topic Models. | Chong Wang, David M. Blei, David Heckerman |
| 2007 | EMNLP | A Topic Model for Word Sense Disambiguation. | Jordan L. Boyd-Graber, David M. Blei, Xiaojin Zhu |
| 2007 | ICDM | A Computational Approach to Style in American Poetry. | David M. Kaplan, David M. Blei |
| 2007 | ICML | Hierarchical maximum entropy density estimation. | Miroslav Dudk, David M. Blei, Robert E. Schapire |
| 2007 | UAI | Nonparametric Bayes Pachinko Allocation. | Wei Li, David M. Blei, Andrew McCallum |
| 2006 | ICML | Combining Stochastic Block Models and Mixed Membership for Statistical Network Analysis. | Edoardo M. Airoldi, David M. Blei, Stephen E. Fienberg, Eric P. Xing |
| 2006 | ICML | Panel Discussion. | David M. Blei |
| 2006 | ICML | Dynamic topic models. | David M. Blei, John D. Lafferty |
| 2005 | KDD | A latent mixed membership model for relational data. | Edoardo M. Airoldi, David M. Blei, Eric P. Xing, Stephen E. Fienberg |
| 2004 | ICML | Variational methods for the Dirichlet process. | David M. Blei, Michael I. Jordan |
| 2003 | SIGIR | Modeling annotated data. | David M. Blei, Michael I. Jordan |
| 2002 | UAI | Learning with Scope, with Application to Information Extraction and Classification. | David M. Blei, J. Andrew Bagnell, Andrew Kachites McCallum |
| 2001 | SIGIR | Topic Segmentation with an Aspect Hidden Markov Model. | David M. Blei, Pedro J. Moreno |