David M. Blei - RankMe

324 papers A* 60A 44Misc 2Journal 158Unranked 56

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Duel-Evolve: Reward-Free Test-Time Scaling via LLM Self-Preferences. CoRR Sweta Karlekar, Carolina Zheng, Magnus Saebo, Nicolas Beltran-Velez, Shuyang Yu, John Bowlan, Michal Kucer, David M. Blei
2026	J	`jnl`	Extending Mean-Field Variational Inference via Entropic Regularization: Theory and Computation. J. Mach. Learn. Res. Bohan Wu, David M. Blei
2026	J	`jnl`	Hierarchical Causal Models. J. Mach. Learn. Res. Eli N. Weinstein, David M. Blei
2026	J	`jnl`	Multi-Domain Empirical Bayes for Linearly-Mixed Causal Representations. CoRR Bohan Wu, Julius von Kügelgen, David M. Blei
2025	J	`jnl`	Bayesian Invariance Modeling of Multi-Environment Data. CoRR Luhuan Wu, Mingzhang Yin, Yixin Wang, John P. Cunningham, David M. Blei
2025	A*	`conf`	Can Generative AI Solve Your In-Context Learning Problem? A Martingale Perspective. ICLR Andrew Jesson, Nicolas Beltran-Velez, David M. Blei
2025	J	`jnl`	Extremely Greedy Equivalence Search. CoRR Achille Nazaret, David M. Blei
2025	J	`jnl`	Fisher meets Feynman: score-based variational inference with a product of experts. CoRR Diana Cai, Robert M. Gower, David M. Blei, Lawrence K. Saul
2025	A	`conf`	HDP-Flow: Generalizable Bayesian Nonparametric Model for Time Series State Discovery. UAI Sana Tonekaboni, Tina Behrouzi, Addison Weatherhead, Emily B. Fox, David M. Blei, Anna Goldenberg
2025	J	`jnl`	Input Adaptive Bayesian Model Averaging. CoRR Yuli Slavutsky, Sebastian Salazar, David M. Blei
2025	J	`jnl`	Model Directions, Not Words: Mechanistic Topic Models Using Sparse Autoencoders. CoRR Carolina Zheng, Nicolas Beltran-Velez, Sweta Karlekar, Claudia Shi, Achille Nazaret, Asif Mallik, Amir Feder, David M. Blei
2025	A	`conf`	Posterior Mean Matching: Generative Modeling through Online Bayesian Inference. AISTATS Sebastian Salazar, Michal Kucer, Yixin Wang, Emily M. Casleton, David M. Blei
2025	J	`jnl`	Quantifying Uncertainty in the Presence of Distribution Shifts. CoRR Yuli Slavutsky, David M. Blei
2024	—	`conf`	A causality-inspired plus-minus model for player evaluation in team sports. CLeaR Caterina De Bacco, Yixin Wang, David M. Blei
2024	A	`conf`	Amortized Variational Inference: When and Why? UAI Charles C. Margossian, David M. Blei
2024	A*	`conf`	Batch and match: black-box variational inference with a score-based divergence. ICML Diana Cai, Chirag Modi, Loucas Pillaud-Vivien, Charles Margossian, Robert M. Gower, David M. Blei, Lawrence K. Saul
2024	J	`jnl`	Batch and match: black-box variational inference with a score-based divergence. CoRR Diana Cai, Chirag Modi, Loucas Pillaud-Vivien, Charles C. Margossian, Robert M. Gower, David M. Blei, Lawrence K. Saul
2024	J	`jnl`	CAREER: A Foundation Model for Labor Sequence Data. Trans. Mach. Learn. Res. Keyon Vafa, Emil Palikot, Tianyu Du, Ayush Kanodia, Susan Athey, David M. Blei
2024	J	`jnl`	Can Generative AI Solve Your In-Context Learning Problem? A Martingale Perspective. CoRR Andrew Jesson, Nicolas Beltran-Velez, David M. Blei
2024	J	`jnl`	Causal fairness assessment of treatment allocation with electronic health records. J. Biomed. Informatics Linying Zhang, Lauren R. Richter, Yixin Wang, Anna Ostropolets, Noémie Elhadad, David M. Blei, George Hripcsak
2024	A	`conf`	Density Uncertainty Layers for Reliable Uncertainty Estimation. AISTATS Yookoon Park, David M. Blei
2024	A*	`conf`	EigenVI: score-based variational inference with orthogonal function expansions. NeurIPS Diana Cai, Chirag Modi, Charles Margossian, Robert M. Gower, David M. Blei, Lawrence K. Saul
2024	J	`jnl`	EigenVI: score-based variational inference with orthogonal function expansions. CoRR Diana Cai, Chirag Modi, Charles C. Margossian, Robert M. Gower, David M. Blei, Lawrence K. Saul
2024	J	`jnl`	Estimating Wage Disparities Using Foundation Models. CoRR Keyon Vafa, Susan Athey, David M. Blei
2024	J	`jnl`	Estimating the Causal Effects of T Cell Receptors. CoRR Eli N. Weinstein, Elizabeth B. Wood, David M. Blei
2024	A*	`conf`	Estimating the Hallucination Rate of Generative AI. NeurIPS Andrew Jesson, Nicolas Beltran-Velez, Quentin Chu, Sweta Karlekar, Jannik Kossen, Yarin Gal, John P. Cunningham, David M. Blei
2024	J	`jnl`	Estimating the Hallucination Rate of Generative AI. CoRR Andrew Jesson, Nicolas Beltran-Velez, Quentin Chu, Sweta Karlekar, Jannik Kossen, Yarin Gal, John P. Cunningham, David M. Blei
2024	J	`jnl`	Extending Mean-Field Variational Inference via Entropic Regularization: Theory and Computation. CoRR Bohan Wu, David M. Blei
2024	A	`conf`	Extremely Greedy Equivalence Search. UAI Achille Nazaret, David M. Blei
2024	A*	`conf`	Hypothesis Testing the Circuit Hypothesis in LLMs. NeurIPS Claudia Shi, Nicolas Beltran-Velez, Achille Nazaret, Carolina Zheng, Adrià Garriga-Alonso, Andrew Jesson, Maggie Makar, David M. Blei
2024	J	`jnl`	Hypothesis Testing the Circuit Hypothesis in LLMs. CoRR Claudia Shi, Nicolas Beltran-Velez, Achille Nazaret, Carolina Zheng, Adrià Garriga-Alonso, Andrew Jesson, Maggie Makar, David M. Blei
2024	J	`jnl`	Multi-environment Topic Models. CoRR Dominic Sobhani, Amir Feder, David M. Blei
2024	A	`conf`	On the Misspecification of Linear Assumptions in Synthetic Controls. AISTATS Achille O. R. Nazaret, Claudia Shi, David M. Blei
2024	J	`jnl`	Optimization-based Causal Estimation from Heterogeneous Environments. J. Mach. Learn. Res. Mingzhang Yin, Yixin Wang, David M. Blei
2024	J	`jnl`	Population Priors for Matrix Factorization. Trans. Mach. Learn. Res. Sohrab Salehi, Achille Nazaret, Sohrab P. Shah, David M. Blei
2024	J	`jnl`	Posterior Mean Matching: Generative Modeling through Online Bayesian Inference. CoRR Sebastian Salazar, Michal Kucer, Yixin Wang, Emily M. Casleton, David M. Blei
2024	A*	`conf`	Stable Differentiable Causal Discovery. ICML Achille Nazaret, Justin Hong, Elham Azizi, David M. Blei
2024	J	`jnl`	Treeffuser: Probabilistic Predictions via Conditional Diffusions with Gradient-Boosted Trees. CoRR Nicolas Beltran-Velez, Alessandro Antonio Grande, Achille Nazaret, Alp Kucukelbir, David M. Blei
2024	A*	`conf`	Treeffuser: probabilistic prediction via conditional diffusions with gradient-boosted trees. NeurIPS Nicolas Beltran-Velez, Alessandro Antonio Grande, Achille Nazaret, Alp Kucukelbir, David M. Blei
2023	J	`jnl`	A Computational Approach to Style in American Poetry. CoRR David M. Kaplan, David M. Blei
2023	J	`jnl`	Adjusting Machine Learning Decisions for Equal Opportunity and Counterfactual Fairness. Trans. Mach. Learn. Res. Yixin Wang, Dhanya Sridhar, David M. Blei
2023	J	`jnl`	Amortized Variational Inference: When and Why? CoRR Charles C. Margossian, David M. Blei
2023	—	`conf`	An Invariant Learning Characterization of Controlled Text Generation. ACL (1) Carolina Zheng, Claudia Shi, Keyon Vafa, Amir Feder, David M. Blei
2023	J	`jnl`	An Invariant Learning Characterization of Controlled Text Generation. CoRR Carolina Zheng, Claudia Shi, Keyon Vafa, Amir Feder, David M. Blei
2023	A*	`conf`	Causal-structure Driven Augmentations for Text OOD Generalization. NeurIPS Amir Feder, Yoav Wald, Claudia Shi, Suchi Saria, David M. Blei
2023	J	`jnl`	Causal-structure Driven Augmentations for Text OOD Generalization. CoRR Amir Feder, Yoav Wald, Claudia Shi, Suchi Saria, David M. Blei
2023	J	`jnl`	Density Uncertainty Layers for Reliable Uncertainty Estimation. CoRR Yookoon Park, David M. Blei
2023	A*	`conf`	Evaluating the Moral Beliefs Encoded in LLMs. NeurIPS Nino Scherrer, Claudia Shi, Amir Feder, David M. Blei
2023	J	`jnl`	Evaluating the Moral Beliefs Encoded in LLMs. CoRR Nino Scherrer, Claudia Shi, Amir Feder, David M. Blei
2023	A*	`conf`	Nonparametric Identifiability of Causal Representations from Unknown Interventions. NeurIPS Julius von Kügelgen, Michel Besserve, Wendong Liang, Luigi Gresele, Armin Kekic, Elias Bareinboim, David M. Blei, Bernhard Schölkopf
2023	J	`jnl`	Nonparametric Identifiability of Causal Representations from Unknown Interventions. CoRR Julius von Kügelgen, Michel Besserve, Wendong Liang, Luigi Gresele, Armin Kekic, Elias Bareinboim, David M. Blei, Bernhard Schölkopf
2023	J	`jnl`	Posterior Collapse and Latent Variable Non-identifiability. CoRR Yixin Wang, David M. Blei, John P. Cunningham
2023	A*	`conf`	Practical and Asymptotically Exact Conditional Sampling in Diffusion Models. NeurIPS Luhuan Wu, Brian L. Trippe, Christian A. Naesseth, David M. Blei, John P. Cunningham
2023	J	`jnl`	Practical and Asymptotically Exact Conditional Sampling in Diffusion Models. CoRR Luhuan Wu, Brian L. Trippe, Christian A. Naesseth, David M. Blei, John P. Cunningham
2023	A	`conf`	Probabilistic Conformal Prediction Using Conditional Random Samples. AISTATS Zhendong Wang, Ruijiang Gao, Mingzhang Yin, Mingyuan Zhou, David M. Blei
2023	J	`jnl`	Revisiting Topic-Guided Language Models. CoRR Carolina Zheng, Keyon Vafa, David M. Blei
2023	J	`jnl`	Revisiting Topic-Guided Language Models. Trans. Mach. Learn. Res. Carolina Zheng, Keyon Vafa, David M. Blei
2023	J	`jnl`	Stable Differentiable Causal Discovery. CoRR Achille Nazaret, Justin Hong, Elham Azizi, David M. Blei
2023	J	`jnl`	Transport Score Climbing: Variational Inference Using Forward KL and Adaptive Neural Transport. Trans. Mach. Learn. Res. Liyi Zhang, David M. Blei, Christian A. Naesseth
2023	A*	`conf`	Variational Inference with Gaussian Score Matching. NeurIPS Chirag Modi, Robert M. Gower, Charles Margossian, Yuling Yao, David M. Blei, Lawrence K. Saul
2023	J	`jnl`	Variational Inference with Gaussian Score Matching. CoRR Chirag Modi, Charles Margossian, Yuling Yao, Robert M. Gower, David M. Blei, Lawrence K. Saul
2022	J	`jnl`	A Bayesian Causal Inference Approach for Assessing Fairness in Clinical Decision-Making. CoRR Linying Zhang, Lauren R. Richter, Yixin Wang, Anna Ostropolets, Noemie Elhadad, David M. Blei, George Hripcsak
2022	J	`jnl`	Adjusting for indirectly measured confounding using large-scale propensity score. J. Biomed. Informatics Linying Zhang, Yixin Wang, Martijn J. Schuemie, David M. Blei, George Hripcsak
2022	—	`conf`	Estimating Social Influence from Observational Data. CLeaR Dhanya Sridhar, Caterina De Bacco, David M. Blei
2022	J	`jnl`	Estimating Social Influence from Observational Data. CoRR Dhanya Sridhar, Caterina De Bacco, David M. Blei
2022	J	`jnl`	Forget-me-not! Contrastive Critics for Mitigating Posterior Collapse. CoRR Sachit Menon, David M. Blei, Carl Vondrick
2022	A	`conf`	Forget-me-not! Contrastive critics for mitigating posterior collapse. UAI Sachit Menon, David M. Blei, Carl Vondrick
2022	J	`jnl`	Heterogeneous Supervised Topic Models. Trans. Assoc. Comput. Linguistics Dhanya Sridhar, Hal Daumé III, David M. Blei
2022	J	`jnl`	Identifiable Deep Generative Models via Sparse Decoding. Trans. Mach. Learn. Res. Gemma E. Moran, Dhanya Sridhar, Yixin Wang, David M. Blei
2022	J	`jnl`	Learning Transferrable Representations of Career Trajectories for Economic Prediction. CoRR Keyon Vafa, Emil Palikot, Tianyu Du, Ayush Kanodia, Susan Athey, David M. Blei
2022	A	`conf`	On the Assumptions of Synthetic Control Methods. AISTATS Claudia Shi, Dhanya Sridhar, Vishal Misra, David M. Blei
2022	J	`jnl`	Probabilistic Conformal Prediction Using Conditional Random Samples. CoRR Zhendong Wang, Ruijiang Gao, Mingzhang Yin, Mingyuan Zhou, David M. Blei
2022	J	`jnl`	The Holdout Randomization Test for Feature Selection in Black Box Models. J. Comput. Graph. Stat. Wesley Tansey, Victor Veitch, Haoran Zhang, Raul Rabadan, David M. Blei
2022	J	`jnl`	Transport Score Climbing: Variational Inference Using Forward KL and Adaptive Neural Transport. CoRR Liyi Zhang, Christian A. Naesseth, David M. Blei
2022	A*	`conf`	Variational Inference for Infinitely Deep Neural Networks. ICML Achille Nazaret, David M. Blei
2022	J	`jnl`	Variational Inference for Infinitely Deep Neural Networks. CoRR Achille Nazaret, David M. Blei
2021	A*	`conf`	A Proxy Variable View of Shared Confounding. ICML Yixin Wang, David M. Blei
2021	J	`jnl`	A general linear-time inference method for Gaussian Processes on one dimension. J. Mach. Learn. Res. Jackson Loper, David M. Blei, John P. Cunningham, Liam Paninski
2021	A*	`conf`	Assessing the Effects of Friend-to-Friend Texting onTurnout in the 2018 US Midterm Elections. WWW Aaron Schein, Keyon Vafa, Dhanya Sridhar, Victor Veitch, Jeffrey Quinn, James Moffet, David M. Blei, Donald P. Green
2021	J	`jnl`	Hierarchical Inducing Point Gaussian Process for Inter-domain Observations. CoRR Luhuan Wu, Andrew Miller, Lauren Anderson, Geoff Pleiss, David M. Blei, John P. Cunningham
2021	A	`conf`	Hierarchical Inducing Point Gaussian Process for Inter-domian Observations. AISTATS Luhuan Wu, Andrew Miller, Lauren Anderson, Geoff Pleiss, David M. Blei, John P. Cunningham
2021	J	`jnl`	Identifiable Variational Autoencoders via Sparse Decoding. CoRR Gemma E. Moran, Dhanya Sridhar, Yixin Wang, David M. Blei
2021	A	`conf`	Invariant representation learning for treatment effect estimation. UAI Claudia Shi, Victor Veitch, David M. Blei
2021	J	`jnl`	Optimization-based Causal Estimation from Heterogenous Environments. CoRR Mingzhang Yin, Yixin Wang, David M. Blei
2021	A*	`conf`	Posterior Collapse and Latent Variable Non-identifiability. NeurIPS Yixin Wang, David M. Blei, John P. Cunningham
2021	—	`conf`	Rationales for Sequential Predictions. EMNLP (1) Keyon Vafa, Yuntian Deng, David M. Blei, Alexander M. Rush
2021	J	`jnl`	Rationales for Sequential Predictions. CoRR Keyon Vafa, Yuntian Deng, David M. Blei, Alexander M. Rush
2021	A*	`conf`	Unsupervised Representation Learning via Neural Activation Coding. ICML Yookoon Park, Sangho Lee, Gunhee Kim, David M. Blei
2021	J	`jnl`	Unsupervised Representation Learning via Neural Activation Coding. CoRR Yookoon Park, Sangho Lee, Gunhee Kim, David M. Blei
2021	J	`jnl`	Variational Combinatorial Sequential Monte Carlo Methods for Bayesian Phylogenetic Inference. CoRR Antonio Khalil Moretti, Liyi Zhang, Christian A. Naesseth, Hadiah Venner, David M. Blei, Itsik Pe'er
2021	A	`conf`	variational combinatorial sequential monte carlo methods for bayesian phylogenetic inference. UAI Antonio Khalil Moretti, Liyi Zhang, Christian A. Naesseth, Hadiah Venner, David M. Blei, Itsik Pe'er
2020	A	`conf`	Adapting Text Embeddings for Causal Inference. UAI Victor Veitch, Dhanya Sridhar, David M. Blei
2020	A	`conf`	Causal Inference for Recommender Systems. RecSys Yixin Wang, Dawen Liang, Laurent Charlin, David M. Blei
2020	Misc	`conf`	Causal Inference from Observational Healthcare Data: Implications, Impacts and Innovations. AMIA George Hripcsak, David M. Blei, Elias Bareinboim, Martijn J. Schuemie, Linying Zhang
2020	J	`jnl`	General linear-time inference for Gaussian Processes on one dimension. CoRR Jackson Loper, David M. Blei, John P. Cunningham, Liam Paninski
2020	J	`jnl`	Invariant Representation Learning for Treatment Effect Estimation. CoRR Claudia Shi, Victor Veitch, David M. Blei
2020	A*	`conf`	Markovian Score Climbing: Variational Inference with KL(p\|\|q). NeurIPS Christian A. Naesseth, Fredrik Lindsten, David M. Blei
2020	J	`jnl`	Markovian Score Climbing: Variational Inference with KL(p\|\|q). CoRR Christian A. Naesseth, Fredrik Lindsten, David M. Blei
2020	A*	`conf`	Text-Based Ideal Points. ACL Keyon Vafa, Suresh Naidu, David M. Blei
2020	J	`jnl`	Text-Based Ideal Points. CoRR Keyon Vafa, Suresh Naidu, David M. Blei
2020	Misc	`conf`	The Multi-Outcome Medical Deconfounder: Assessing Treatment Effect on Multiple Renal Measures. AMIA Linying Zhang, Yixin Wang, Anna Ostropolets, Ruijun Chen, David M. Blei, George Hripcsak
2020	J	`jnl`	Topic Modeling in Embedding Spaces. Trans. Assoc. Comput. Linguistics Adji Bousso Dieng, Francisco J. R. Ruiz, David M. Blei
2020	J	`jnl`	Towards Clarifying the Theory of the Deconfounder. CoRR Yixin Wang, David M. Blei
2019	A*	`conf`	Adapting Neural Networks for the Estimation of Treatment Effects. NeurIPS Claudia Shi, David M. Blei, Victor Veitch
2019	J	`jnl`	Adapting Neural Networks for the Estimation of Treatment Effects. CoRR Claudia Shi, David M. Blei, Victor Veitch
2019	A	`conf`	Avoiding Latent Variable Collapse with Generative Skip Models. AISTATS Adji B. Dieng, Yoon Kim, Alexander M. Rush, David M. Blei
2019	J	`jnl`	Bayesian Tensor Filtering: Smooth, Locally-Adaptive Factorization of Functional Matrices. CoRR Wesley Tansey, Christopher Tosh, David M. Blei
2019	J	`jnl`	Counterfactual Inference for Consumer Choice Across Many Product Categories. CoRR Robert Donnelly, Francisco J. R. Ruiz, David M. Blei, Susan Athey
2019	A	`conf`	Empirical Risk Minimization and Stochastic Gradient Descent for Relational Data. AISTATS Victor Veitch, Morgane Austern, Wenda Zhou, David M. Blei, Peter Orbanz
2019	J	`jnl`	Equal Opportunity and Affirmative Action via Counterfactual Predictions. CoRR Yixin Wang, Dhanya Sridhar, David M. Blei
2019	J	`jnl`	Multiple Causes: A Causal Graphical View. CoRR Yixin Wang, David M. Blei
2019	A*	`conf`	Poisson-Randomized Gamma Dynamical Systems. NeurIPS Aaron Schein, Scott W. Linderman, Mingyuan Zhou, David M. Blei, Hanna M. Wallach
2019	J	`jnl`	Poisson-Randomized Gamma Dynamical Systems. CoRR Aaron Schein, Scott W. Linderman, Mingyuan Zhou, David M. Blei, Hanna M. Wallach
2019	J	`jnl`	Population Predictive Checks. CoRR Rajesh Ranganath, David M. Blei
2019	J	`jnl`	Prescribed Generative Adversarial Networks. CoRR Adji B. Dieng, Francisco J. R. Ruiz, David M. Blei, Michalis K. Titsias
2019	J	`jnl`	The Blessings of Multiple Causes: A Reply to Ogburn et al. (2019). CoRR Yixin Wang, David M. Blei
2019	J	`jnl`	The Dynamic Embedded Topic Model. CoRR Adji B. Dieng, Francisco J. R. Ruiz, David M. Blei
2019	J	`jnl`	The Medical Deconfounder: Assessing Treatment Effect with Electronic Health Records (EHRs). CoRR Linying Zhang, Yixin Wang, Anna Ostropolets, Jami J. Mulgrave, David M. Blei, George Hripcsak
2019	—	`conf`	The Medical Deconfounder: Assessing Treatment Effects with Electronic Health Records. MLHC Linying Zhang, Yixin Wang, Anna Ostropolets, Jami J. Mulgrave, David M. Blei, George Hripcsak
2019	J	`jnl`	Topic Modeling in Embedding Spaces. CoRR Adji B. Dieng, Francisco J. R. Ruiz, David M. Blei
2019	A*	`conf`	Using Embeddings to Correct for Unobserved Confounding in Networks. NeurIPS Victor Veitch, Yixin Wang, David M. Blei
2019	J	`jnl`	Using Embeddings to Correct for Unobserved Confounding. CoRR Victor Veitch, Yixin Wang, David M. Blei
2019	J	`jnl`	Using Text Embeddings for Causal Inference. CoRR Victor Veitch, Dhanya Sridhar, David M. Blei
2019	A*	`conf`	Variational Bayes under Model Misspecification. NeurIPS Yixin Wang, David M. Blei
2019	J	`jnl`	Variational Bayes under Model Misspecification. CoRR Yixin Wang, David M. Blei
2018	J	`jnl`	A Probabilistic Model of Cardiac Physiology and Electrocardiograms. CoRR Andrew C. Miller, Ziad Obermeyer, David M. Blei, John P. Cunningham, Sendhil Mullainathan
2018	J	`jnl`	A probabilistic approach to discovering dynamic full-brain functional connectivity patterns. NeuroImage Jeremy R. Manning, Xia Zhu, Theodore L. Willke, Rajesh Ranganath, Kimberly L. Stachenfeld, Uri Hasson, David M. Blei, Kenneth A. Norman
2018	A*	`conf`	Augment and Reduce: Stochastic Inference for Large Categorical Distributions. ICML Francisco J. R. Ruiz, Michalis K. Titsias, Adji B. Dieng, David M. Blei
2018	J	`jnl`	Augment and Reduce: Stochastic Inference for Large Categorical Distributions. CoRR Francisco J. R. Ruiz, Michalis K. Titsias, Adji B. Dieng, David M. Blei
2018	J	`jnl`	Avoiding Latent Variable Collapse With Generative Skip Models. CoRR Adji B. Dieng, Yoon Kim, Alexander M. Rush, David M. Blei
2018	A*	`conf`	Black Box FDR. ICML Wesley Tansey, Yixin Wang, David M. Blei, Raul Rabadan
2018	J	`jnl`	Black Box FDR. CoRR Wesley Tansey, Yixin Wang, David M. Blei, Raul Rabadan
2018	A*	`conf`	Dynamic Embeddings for Language Evolution. WWW Maja Rudolph, David M. Blei
2018	J	`jnl`	Empirical Risk Minimization and Stochastic Gradient Descent for Relational Data. CoRR Victor Veitch, Morgane Austern, Wenda Zhou, David M. Blei, Peter Orbanz
2018	J	`jnl`	Equation Embeddings. CoRR Kriste Krstovski, David M. Blei
2018	J	`jnl`	Estimating Heterogeneous Consumer Preferences for Restaurants and Travel Time Using Mobile Location Data. CoRR Susan Athey, David M. Blei, Robert Donnelly, Francisco J. R. Ruiz, Tobias Schmidt
2018	—	`conf`	Implicit Causal Models for Genome-wide Association Studies. ICLR (Poster) Dustin Tran, David M. Blei
2018	A*	`conf`	Noisin: Unbiased Regularization for Recurrent Neural Networks. ICML Adji Bousso Dieng, Rajesh Ranganath, Jaan Altosaar, David M. Blei
2018	J	`jnl`	Noisin: Unbiased Regularization for Recurrent Neural Networks. CoRR Adji B. Dieng, Rajesh Ranganath, Jaan Altosaar, David M. Blei
2018	A	`conf`	Proximity Variational Inference. AISTATS Jaan Altosaar, Rajesh Ranganath, David M. Blei
2018	J	`jnl`	Technical perspective: Expressive probabilistic models and scalable method of moments. Commun. ACM David M. Blei
2018	J	`jnl`	The Blessings of Multiple Causes. CoRR Yixin Wang, David M. Blei
2018	J	`jnl`	The Deconfounded Recommender: A Causal Inference Approach to Recommendation. CoRR Yixin Wang, Dawen Liang, Laurent Charlin, David M. Blei
2018	A	`conf`	Variational Sequential Monte Carlo. AISTATS Christian A. Naesseth, Scott W. Linderman, Rajesh Ranganath, David M. Blei
2017	J	`jnl`	Automatic Differentiation Variational Inference. J. Mach. Learn. Res. Alp Kucukelbir, Dustin Tran, Rajesh Ranganath, Andrew Gelman, David M. Blei
2017	A	`conf`	Bayesian Learning and Inference in Recurrent Switching Linear Dynamical Systems. AISTATS Scott W. Linderman, Matthew J. Johnson, Andrew C. Miller, Ryan P. Adams, David M. Blei, Liam Paninski
2017	—	`conf`	Context Selection for Embedding Models. NIPS Li-Ping Liu, Francisco J. R. Ruiz, Susan Athey, David M. Blei
2017	—	`conf`	Deep Probabilistic Programming. ICLR (Poster) Dustin Tran, Matthew D. Hoffman, Rif A. Saurous, Eugene Brevdo, Kevin Murphy, David M. Blei
2017	J	`jnl`	Deep Probabilistic Programming. CoRR Dustin Tran, Matthew D. Hoffman, Rif A. Saurous, Eugene Brevdo, Kevin Murphy, David M. Blei
2017	J	`jnl`	Deep and Hierarchical Implicit Models. CoRR Dustin Tran, Rajesh Ranganath, David M. Blei
2017	J	`jnl`	Dynamic Bernoulli Embeddings for Language Evolution. CoRR Maja Rudolph, David M. Blei
2017	A*	`conf`	Evaluating Bayesian Models with Posterior Dispersion Indices. ICML Alp Kucukelbir, Yixin Wang, David M. Blei
2017	J	`jnl`	Frequentist Consistency of Variational Bayes. CoRR Yixin Wang, David M. Blei
2017	—	`conf`	Hierarchical Implicit Models and Likelihood-Free Variational Inference. NIPS Dustin Tran, Rajesh Ranganath, David M. Blei
2017	J	`jnl`	Implicit Causal Models for Genome-wide Association Studies. CoRR Dustin Tran, David M. Blei
2017	J	`jnl`	Proximity Variational Inference. CoRR Jaan Altosaar, Rajesh Ranganath, David M. Blei
2017	A	`conf`	Reparameterization Gradients through Acceptance-Rejection Sampling Algorithms. AISTATS Christian A. Naesseth, Francisco J. R. Ruiz, Scott W. Linderman, David M. Blei
2017	A*	`conf`	Robust Probabilistic Modeling with Bayesian Data Reweighting. ICML Yixin Wang, Alp Kucukelbir, David M. Blei
2017	J	`jnl`	SHOPPER: A Probabilistic Model of Consumer Choice with Substitutes and Complements. CoRR Francisco J. R. Ruiz, Susan Athey, David M. Blei
2017	J	`jnl`	Science and data science. Proc. Natl. Acad. Sci. USA David M. Blei, Padhraic Smyth
2017	J	`jnl`	Stochastic Gradient Descent as Approximate Bayesian Inference. CoRR Stephan Mandt, Matthew D. Hoffman, David M. Blei
2017	J	`jnl`	Stochastic Gradient Descent as Approximate Bayesian Inference. J. Mach. Learn. Res. Stephan Mandt, Matthew D. Hoffman, David M. Blei
2017	—	`conf`	Structured Embedding Models for Grouped Data. NIPS Maja Rudolph, Francisco J. R. Ruiz, Susan Athey, David M. Blei
2017	J	`jnl`	Structured Embedding Models for Grouped Data. CoRR Maja Rudolph, Francisco J. R. Ruiz, Susan Athey, David M. Blei
2017	—	`conf`	Variational Inference via \chi Upper Bound Minimization. NIPS Adji Bousso Dieng, Dustin Tran, Rajesh Ranganath, John W. Paisley, David M. Blei
2017	A*	`conf`	Zero-Inflated Exponential Family Embeddings. ICML Li-Ping Liu, David M. Blei
2016	A*	`conf`	A Variational Analysis of Stochastic Gradient Algorithms. ICML Stephan Mandt, Matthew D. Hoffman, David M. Blei
2016	J	`jnl`	A Variational Analysis of Stochastic Gradient Algorithms. CoRR Stephan Mandt, Matthew D. Hoffman, David M. Blei
2016	J	`jnl`	Automatic Differentiation Variational Inference. CoRR Alp Kucukelbir, Dustin Tran, Rajesh Ranganath, Andrew Gelman, David M. Blei
2016	A*	`conf`	Bayesian Poisson Tucker Decomposition for Learning the Structure of International Relations. ICML Aaron Schein, Mingyuan Zhou, David M. Blei, Hanna M. Wallach
2016	J	`jnl`	Bayesian Poisson Tucker Decomposition for Learning the Structure of International Relations. CoRR Aaron Schein, Mingyuan Zhou, David M. Blei, Hanna M. Wallach
2016	—	`conf`	Deep Survival Analysis. MLHC Rajesh Ranganath, Adler J. Perotte, Noémie Elhadad, David M. Blei
2016	J	`jnl`	Deep Survival Analysis. CoRR Rajesh Ranganath, Adler J. Perotte, Noémie Elhadad, David M. Blei
2016	A*	`conf`	Detecting and Characterizing Events. EMNLP Allison June-Barlow Chaney, Hanna M. Wallach, Matthew Connelly, David M. Blei
2016	J	`jnl`	Edward: A library for probabilistic modeling, inference, and criticism. CoRR Dustin Tran, Alp Kucukelbir, Adji B. Dieng, Maja Rudolph, Dawen Liang, David M. Blei
2016	—	`conf`	Exponential Family Embeddings. NIPS Maja Rudolph, Francisco J. R. Ruiz, Stephan Mandt, David M. Blei
2016	J	`jnl`	Exponential Family Embeddings. CoRR Maja Rudolph, Francisco J. R. Ruiz, Stephan Mandt, David M. Blei
2016	A	`conf`	Factorization Meets the Item Embedding: Regularizing Matrix Factorization with Item Co-occurrence. RecSys Dawen Liang, Jaan Altosaar, Laurent Charlin, David M. Blei
2016	A*	`conf`	Hierarchical Variational Models. ICML Rajesh Ranganath, Dustin Tran, David M. Blei
2016	A*	`conf`	Modeling User Exposure in Recommendation. WWW Dawen Liang, Laurent Charlin, James McInerney, David M. Blei
2016	A*	`conf`	Objective Variables for Probabilistic Revenue Maximization in Second-Price Auctions with Reserve. WWW Maja R. Rudolph, Joseph G. Ellis, David M. Blei
2016	—	`conf`	Operator Variational Inference. NIPS Rajesh Ranganath, Dustin Tran, Jaan Altosaar, David M. Blei
2016	J	`jnl`	Operator Variational Inference. CoRR Rajesh Ranganath, Jaan Altosaar, Dustin Tran, David M. Blei
2016	A	`conf`	Overdispersed Black-Box Variational Inference. UAI Francisco J. R. Ruiz, Michalis K. Titsias, David M. Blei
2016	J	`jnl`	Posterior Dispersion Indices. CoRR Alp Kucukelbir, David M. Blei
2016	J	`jnl`	Reweighted Data for Robust Probabilistic Models. CoRR Yixin Wang, Alp Kucukelbir, David M. Blei
2016	J	`jnl`	The $χ$-Divergence for Approximate Inference. CoRR Adji B. Dieng, Dustin Tran, Rajesh Ranganath, John W. Paisley, David M. Blei
2016	—	`conf`	The Generalized Reparameterization Gradient. NIPS Francisco J. R. Ruiz, Michalis K. Titsias, David M. Blei
2016	A*	`conf`	Variational Gaussian Process. ICLR Dustin Tran, Rajesh Ranganath, David M. Blei
2016	J	`jnl`	Variational Inference: A Review for Statisticians. CoRR David M. Blei, Alp Kucukelbir, Jon D. McAuliffe
2016	A	`conf`	Variational Tempering. AISTATS Stephan Mandt, James McInerney, Farhan Abrol, Rajesh Ranganath, David M. Blei
2015	J	`jnl`	A Bayesian Nonparametric Approach to Image Super-Resolution. IEEE Trans. Pattern Anal. Mach. Intell. Gungor Polatkan, Mingyuan Zhou, Lawrence Carin, David M. Blei, Ingrid Daubechies
2015	A	`conf`	A Probabilistic Model for Using Social Networks in Personalized Item Recommendation. RecSys Allison June-Barlow Chaney, David M. Blei, Tina Eliassi-Rad
2015	—	`conf`	Automatic Variational Inference in Stan. NIPS Alp Kucukelbir, Rajesh Ranganath, Andrew Gelman, David M. Blei
2015	A*	`conf`	Bayesian Poisson Tensor Factorization for Inferring Multilateral Relations from Sparse Dyadic Event Counts. KDD Aaron Schein, John W. Paisley, David M. Blei, Hanna M. Wallach
2015	J	`jnl`	Bayesian Poisson Tensor Factorization for Inferring Multilateral Relations from Sparse Dyadic Event Counts. CoRR Aaron Schein, John W. Paisley, David M. Blei, Hanna M. Wallach
2015	—	`conf`	Copula variational inference. NIPS Dustin Tran, David M. Blei, Edoardo M. Airoldi
2015	A	`conf`	Deep Exponential Families. AISTATS Rajesh Ranganath, Linpeng Tang, Laurent Charlin, David M. Blei
2015	J	`jnl`	Distance Dependent Infinite Latent Feature Models. IEEE Trans. Pattern Anal. Mach. Intell. Samuel Gershman, Peter I. Frazier, David M. Blei
2015	A	`conf`	Dynamic Poisson Factorization. RecSys Laurent Charlin, Rajesh Ranganath, James McInerney, David M. Blei
2015	J	`jnl`	Dynamic Poisson Factorization. CoRR Laurent Charlin, Rajesh Ranganath, James McInerney, David M. Blei
2015	J	`jnl`	Hierarchical Variational Models. CoRR Rajesh Ranganath, Dustin Tran, David M. Blei
2015	J	`jnl`	Modeling User Exposure in Recommendation. CoRR Dawen Liang, Laurent Charlin, James McInerney, David M. Blei
2015	J	`jnl`	Nested Hierarchical Dirichlet Processes. IEEE Trans. Pattern Anal. Mach. Intell. John W. Paisley, Chong Wang, David M. Blei, Michael I. Jordan
2015	J	`jnl`	Objective Variables for Probabilistic Revenue Maximization in Second-Price Auctions with Reserve. CoRR Maja R. Rudolph, Joseph G. Ellis, David M. Blei
2015	A	`conf`	Population Empirical Bayes. UAI Alp Kucukelbir, David M. Blei
2015	A*	`ed.`	Proceedings of the 32nd International Conference on Machine Learning, ICML 2015, Lille, France, 6-11 July 2015 ICML Francis R. Bach, David M. Blei
2015	J	`jnl`	Risk prediction for chronic kidney disease progression using heterogeneous electronic health record data and time series analysis. J. Am. Medical Informatics Assoc. Adler J. Perotte, Rajesh Ranganath, Jamie S. Hirsch, David M. Blei, Noémie Elhadad
2015	A	`conf`	Scalable Recommendation with Hierarchical Poisson Factorization. UAI Prem Gopalan, Jake M. Hofman, David M. Blei
2015	A	`conf`	Stochastic Structured Variational Inference. AISTATS Matthew D. Hoffman, David M. Blei
2015	—	`conf`	The Population Posterior and Bayesian Modeling on Streams. NIPS James McInerney, Rajesh Ranganath, David M. Blei
2015	A	`conf`	The Survival Filter: Joint Survival Analysis with a Latent Time Series. UAI Rajesh Ranganath, Adler J. Perotte, Noémie Elhadad, David M. Blei
2015	J	`jnl`	Variational inference with copula augmentation. CoRR Dustin Tran, David M. Blei, Edoardo M. Airoldi
2014	—	`conf`	A Filtering Approach to Stochastic Variational Inference. NIPS Neil Houlsby, David M. Blei
2014	—	`ch.`	Bayesian Nonnegative Matrix Factorization with Stochastic Variational Inference. Handbook of Mixed Membership Models and Their Applications John W. Paisley, David M. Blei, Michael I. Jordan
2014	A	`conf`	Bayesian Nonparametric Poisson Factorization for Recommendation Systems. AISTATS Prem Gopalan, Francisco J. R. Ruiz, Rajesh Ranganath, David M. Blei
2014	A	`conf`	Black Box Variational Inference. AISTATS Rajesh Ranganath, Sean Gerrish, David M. Blei
2014	J	`jnl`	Black Box Variational Inference. CoRR Rajesh Ranganath, Sean Gerrish, David M. Blei
2014	—	`conf`	Content-based recommendations with Poisson factorization. NIPS Prem Gopalan, Laurent Charlin, David M. Blei
2014	J	`jnl`	Decomposing spatiotemporal brain patterns into topographic latent sources. NeuroImage Samuel Gershman, David M. Blei, Kenneth A. Norman, Per B. Sederberg
2014	J	`jnl`	Deep Exponential Families. CoRR Rajesh Ranganath, Linpeng Tang, Laurent Charlin, David M. Blei
2014	J	`jnl`	Deterministic Annealing for Stochastic Variational Inference. CoRR Farhan Abrol, Stephan Mandt, Rajesh Ranganath, David M. Blei
2014	—	`book`	Handbook of Mixed Membership Models and Their Applications. Edoardo M. Airoldi, David M. Blei, Elena A. Erosheva, Stephen E. Fienberg
2014	—	`conf`	Hierarchical topographic factor analysis. PRNI Jeremy R. Manning, Rajesh Ranganath, Waitsang Keung, Nicholas B. Turk-Browne, Jonathan D. Cohen, Kenneth A. Norman, David M. Blei
2014	—	`ch.`	Introduction to Mixed Membership Models and Methods. Handbook of Mixed Membership Models and Their Applications Edoardo M. Airoldi, David M. Blei, Elena A. Erosheva, Stephen E. Fienberg
2014	J	`jnl`	Profile Predictive Inference. CoRR Alp Kucukelbir, David M. Blei
2014	—	`conf`	Smoothed Gradients for Stochastic Variational Inference. NIPS Stephan Mandt, David M. Blei
2014	J	`jnl`	Smoothed Gradients for Stochastic Variational Inference. CoRR Stephan Mandt, David M. Blei
2014	A*	`conf`	The Inverse Regression Topic Model. ICML Maxim Rabinovich, David M. Blei
2013	—	`conf`	A Nested HDP for Hierarchical Topic Models ICLR (Workshop) John W. Paisley, Chong Wang, David M. Blei, Michael I. Jordan
2013	—	`conf`	An Adaptive Learning Rate for Stochastic Variational Inference. ICML (2) Rajesh Ranganath, Chong Wang, David M. Blei, Eric P. Xing
2013	J	`jnl`	Deep Learning with Hierarchical Convolutional Factor Analysis. IEEE Trans. Pattern Anal. Mach. Intell. Bo Chen, Gungor Polatkan, Guillermo Sapiro, David M. Blei, David B. Dunson, Lawrence Carin
2013	—	`conf`	Efficient Online Inference for Bayesian Nonparametric Relational Models. NIPS Dae Il Kim, Prem Gopalan, David M. Blei, Erik B. Sudderth
2013	J	`jnl`	Efficient discovery of overlapping communities in massive networks. Proc. Natl. Acad. Sci. USA Prem Gopalan, David M. Blei
2013	J	`jnl`	Learning with Scope, with Application to Information Extraction and Classification CoRR David M. Blei, J. Andrew Bagnell, Andrew McCallum
2013	—	`conf`	Modeling Overlapping Communities with Node Popularities. NIPS Prem Gopalan, Chong Wang, David M. Blei
2013	J	`jnl`	Scalable Recommendation with Poisson Factorization. CoRR Prem Gopalan, Jake M. Hofman, David M. Blei
2013	J	`jnl`	Stochastic variational inference. J. Mach. Learn. Res. Matthew D. Hoffman, David M. Blei, Chong Wang, John W. Paisley
2013	J	`jnl`	Variational inference in nonconjugate models. J. Mach. Learn. Res. Chong Wang, David M. Blei
2012	J	`jnl`	A Bayesian Nonparametric Approach to Image Super-resolution CoRR Gungor Polatkan, Mingyuan Zhou, Lawrence Carin, David M. Blei, Ingrid Daubechies
2012	J	`jnl`	A Split-Merge MCMC Algorithm for the Hierarchical Dirichlet Process CoRR Chong Wang, David M. Blei
2012	J	`jnl`	Continuous Time Dynamic Topic Models CoRR Chong Wang, David M. Blei, David Heckerman
2012	—	`conf`	How They Vote: Issue-Adjusted Models of Legislative Behavior. NIPS Sean Gerrish, David M. Blei
2012	J	`jnl`	Multilingual Topic Models for Unaligned Text CoRR Jordan L. Boyd-Graber, David M. Blei
2012	J	`jnl`	Nested Hierarchical Dirichlet Processes CoRR John W. Paisley, Chong Wang, David M. Blei, Michael I. Jordan
2012	J	`jnl`	Nonparametric Bayes Pachinko Allocation CoRR Wei Li, David M. Blei, Andrew McCallum
2012	J	`jnl`	Nonparametric variational inference CoRR Samuel Gershman, Matthew D. Hoffman, David M. Blei
2012	A*	`conf`	Nonparametric variational inference. ICML Samuel Gershman, Matthew D. Hoffman, David M. Blei
2012	J	`jnl`	Probabilistic topic models. Commun. ACM David M. Blei
2012	—	`conf`	Scalable Inference of Overlapping Communities. NIPS Prem Gopalan, David M. Mimno, Sean Gerrish, Michael J. Freedman, David M. Blei
2012	A*	`conf`	Sparse stochastic inference for latent Dirichlet allocation. ICML David M. Mimno, Matthew D. Hoffman, David M. Blei
2012	A	`conf`	Stick-Breaking Beta Processes and the Poisson Process. AISTATS John W. Paisley, David M. Blei, Michael I. Jordan
2012	J	`jnl`	Stochastic Variational Inference. CoRR Matthew D. Hoffman, David M. Blei, Chong Wang, John W. Paisley
2012	J	`jnl`	The Issue-Adjusted Ideal Point Model CoRR Sean Gerrish, David M. Blei
2012	—	`conf`	Truncation-free Online Variational Inference for Bayesian Nonparametric Models. NIPS Chong Wang, David M. Blei
2012	A*	`conf`	Variational Bayesian Inference with Stochastic Search. ICML John W. Paisley, David M. Blei, Michael I. Jordan
2012	A	`conf`	Visualizing Topic Models. ICWSM Allison June-Barlow Chaney, David M. Blei
2011	J	`jnl`	A topographic latent source model for fMRI data. NeuroImage Samuel Gershman, David M. Blei, Francisco Pereira, Kenneth A. Norman
2011	A*	`conf`	Bayesian Checking for Topic Models. EMNLP David M. Mimno, David M. Blei
2011	A*	`conf`	Collaborative topic modeling for recommending scientific articles. KDD Chong Wang, David M. Blei
2011	J	`jnl`	Dirichlet Process Mixtures of Generalized Linear Models. J. Mach. Learn. Res. Lauren Hannah, David M. Blei, Warren B. Powell
2011	J	`jnl`	Distance Dependent Chinese Restaurant Processes. J. Mach. Learn. Res. David M. Blei, Peter I. Frazier
2011	A	`conf`	Online Variational Inference for the Hierarchical Dirichlet Process. AISTATS Chong Wang, John W. Paisley, David M. Blei
2011	A*	`conf`	Predicting Legislative Roll Calls from Text. ICML Sean Gerrish, David M. Blei
2011	—	`conf`	Spatial distance dependent Chinese restaurant processes for image segmentation. NIPS Soumya Ghosh, Andrei B. Ungureanu, Erik B. Sudderth, David M. Blei
2011	A	`conf`	The Discrete Infinite Logistic Normal Distribution for Mixed-Membership Modeling. AISTATS John W. Paisley, Chong Wang, David M. Blei
2011	A*	`conf`	Variational Inference for Stick-Breaking Beta Process Priors. ICML John W. Paisley, Lawrence Carin, David M. Blei
2010	A*	`conf`	A Language-based Approach to Measuring Scholarly Impact. ICML Sean Gerrish, David M. Blei
2010	A*	`conf`	Bayesian Nonparametric Matrix Factorization for Recorded Music. ICML Matthew D. Hoffman, David M. Blei, Perry R. Cook
2010	A*	`conf`	Building and using a semantivisual image hierarchy. CVPR Li-Jia Li, Chong Wang, Yongwhan Lim, David M. Blei, Li Fei-Fei
2010	A	`conf`	Dirichlet Process Mixtures of Generalized Linear Models. AISTATS Lauren Hannah, David M. Blei, Warren B. Powell
2010	A*	`conf`	Distance dependent Chinese restaurant processes. ICML David M. Blei, Peter I. Frazier
2010	A	`conf`	Exploiting Covariate Similarity in Sparse Regression via the Pairwise Elastic Net. AISTATS Alexander Lorbert, David J. Eis, Victoria Kostina, David M. Blei, Peter J. Ramadge
2010	—	`conf`	Nonparametric Density Estimation for Stochastic Optimization with an Observable State Variable. NIPS Lauren Hannah, Warren B. Powell, David M. Blei
2010	—	`conf`	Online Learning for Latent Dirichlet Allocation. NIPS Matthew D. Hoffman, David M. Blei, Francis R. Bach
2010	J	`jnl`	Probabilistic Topic Models. IEEE Signal Process. Mag. David M. Blei, Lawrence Carin, David B. Dunson
2010	J	`jnl`	Syntactic Topic Models CoRR Jordan L. Boyd-Graber, David M. Blei
2010	A*	`conf`	The IBP Compound Dirichlet Process and its Application to Focused Topic Modeling. ICML Sinead Williamson, Chong Wang, Katherine A. Heller, David M. Blei
2010	J	`jnl`	The nested chinese restaurant process and bayesian nonparametric inference of topic hierarchies. J. ACM David M. Blei, Thomas L. Griffiths, Michael I. Jordan
2010	—	`conf`	Variational Inference for Adaptor Grammars. HLT-NAACL Shay B. Cohen, David M. Blei, Noah A. Smith
2009	—	`conf`	A Bayesian Analysis of Dynamics in Free Recall. NIPS Richard Socher, Samuel Gershman, Adler J. Perotte, Per B. Sederberg, David M. Blei, Kenneth A. Norman
2009	—	`conf`	Bayesian Spectral Matching: Turning Young MC into MC Hammer via MCMC Sampling. ICMC Matthew D. Hoffman, Perry R. Cook, David M. Blei
2009	A*	`conf`	Connections between the lines: augmenting social networks with text. KDD Jonathan D. Chang, Jordan L. Boyd-Graber, David M. Blei
2009	—	`conf`	Decoupling Sparsity and Smoothness in the Discrete Hierarchical Dirichlet Process. NIPS Chong Wang, David M. Blei
2009	—	`conf`	Easy As CBA: A Simple Probabilistic Model for Tagging Music. ISMIR Matthew D. Hoffman, David M. Blei, Perry R. Cook
2009	A	`conf`	Markov Topic Models. AISTATS Chong Wang, Bo Thiesson, Christopher Meek, David M. Blei
2009	A	`conf`	Multilingual Topic Models for Unaligned Text. UAI Jordan L. Boyd-Graber, David M. Blei
2009	—	`conf`	Reading Tea Leaves: How Humans Interpret Topic Models. NIPS Jonathan D. Chang, Jordan L. Boyd-Graber, Sean Gerrish, Chong Wang, David M. Blei
2009	A	`conf`	Relational Topic Models for Document Networks. AISTATS Jonathan D. Chang, David M. Blei
2009	A*	`conf`	Simultaneous image classification and annotation. CVPR Chong Wang, David M. Blei, Li Fei-Fei
2009	—	`conf`	Variational Inference for the Nested Chinese Restaurant Process. NIPS Chong Wang, David M. Blei
2008	—	`conf`	Content-Based Musical Similarity Computation using the Hierarchical Dirichlet Process. ISMIR Matthew D. Hoffman, David M. Blei, Perry R. Cook
2008	A	`conf`	Continuous Time Dynamic Topic Models. UAI Chong Wang, David M. Blei, David Heckerman
2008	—	`conf`	Data-Driven Recomposition using the Hierarchical Dirichlet Process Hidden Markov Model. ICMC Matthew D. Hoffman, Perry R. Cook, David M. Blei
2008	—	`conf`	Mixed Membership Stochastic Blockmodels. NIPS Edoardo M. Airoldi, David M. Blei, Stephen E. Fienberg, Eric P. Xing
2008	J	`jnl`	Mixed Membership Stochastic Blockmodels. J. Mach. Learn. Res. Edoardo M. Airoldi, David M. Blei, Stephen E. Fienberg, Eric P. Xing
2008	—	`conf`	Relative Performance Guarantees for Approximate Inference in Latent Dirichlet Allocation. NIPS Indraneel Mukherjee, David M. Blei
2008	—	`conf`	Syntactic Topic Models. NIPS Jordan L. Boyd-Graber, David M. Blei
2007	A*	`conf`	A Computational Approach to Style in American Poetry. ICDM David M. Kaplan, David M. Blei
2007	—	`conf`	A Topic Model for Word Sense Disambiguation. EMNLP-CoNLL Jordan L. Boyd-Graber, David M. Blei, Xiaojin Zhu
2007	A*	`conf`	Hierarchical maximum entropy density estimation. ICML Miroslav Dudík, David M. Blei, Robert E. Schapire
2007	J	`jnl`	Mixed membership stochastic blockmodels. CoRR Edoardo M. Airoldi, David M. Blei, Stephen E. Fienberg, Eric P. Xing
2007	A	`conf`	Nonparametric Bayes Pachinko Allocation. UAI Wei Li, David M. Blei, Andrew McCallum
2007	—	`conf`	PU-BCD: Exponential Family Models for the Coarse- and Fine-Grained All-Words Tasks. SemEval@ACL Jonathan D. Chang, Miroslav Dudík, David M. Blei
2007	—	`conf`	PUTOP: Turning Predominant Senses into a Topic Model for Word Sense Disambiguation. SemEval@ACL Jordan L. Boyd-Graber, David M. Blei
2007	—	`ed.`	Statistical Network Analysis: Models, Issues, and New Directions - ICML 2006 Workshop on Statistical Network Analysis, Pittsburgh, PA, USA, June 29, 2006, Revised Selected Papers SNA@ICML Edoardo M. Airoldi, David M. Blei, Stephen E. Fienberg, Anna Goldenberg, Eric P. Xing, Alice X. Zheng
2007	—	`conf`	Supervised Topic Models. NIPS David M. Blei, Jon D. McAuliffe
2006	—	`conf`	Combining Stochastic Block Models and Mixed Membership for Statistical Network Analysis. SNA@ICML Edoardo M. Airoldi, David M. Blei, Stephen E. Fienberg, Eric P. Xing
2006	A*	`conf`	Dynamic topic models. ICML David M. Blei, John D. Lafferty
2006	J	`jnl`	Nonparametric empirical Bayes for the Dirichlet process mixture model. Stat. Comput. Jon D. McAuliffe, David M. Blei, Michael I. Jordan
2006	—	`conf`	Panel Discussion. SNA@ICML David M. Blei
2006	J	`jnl`	Statistical modeling of biomedical corpora: mining the Caenorhabditis Genetic Center Bibliography for genes related to life span. BMC Bioinform. David M. Blei, K. Franks, Michael I. Jordan, I. Saira Mian
2005	—	`conf`	A latent mixed membership model for relational data. LinkKDD Edoardo M. Airoldi, David M. Blei, Eric P. Xing, Stephen E. Fienberg
2005	—	`conf`	Correlated Topic Models. NIPS David M. Blei, John D. Lafferty
2004	—	`conf`	Integrating Topics and Syntax. NIPS Thomas L. Griffiths, Mark Steyvers, David M. Blei, Joshua B. Tenenbaum
2004	—	`conf`	Sharing Clusters among Related Groups: Hierarchical Dirichlet Processes. NIPS Yee Whye Teh, Michael I. Jordan, Matthew J. Beal, David M. Blei
2004	A*	`conf`	Variational methods for the Dirichlet process. ICML David M. Blei, Michael I. Jordan
2003	—	`conf`	Hierarchical Topic Models and the Nested Chinese Restaurant Process. NIPS David M. Blei, Thomas L. Griffiths, Michael I. Jordan, Joshua B. Tenenbaum
2003	J	`jnl`	Latent Dirichlet Allocation. J. Mach. Learn. Res. David M. Blei, Andrew Y. Ng, Michael I. Jordan
2003	J	`jnl`	Matching Words and Pictures. J. Mach. Learn. Res. Kobus Barnard, Pinar Duygulu, David A. Forsyth, Nando de Freitas, David M. Blei, Michael I. Jordan
2003	A*	`conf`	Modeling annotated data. SIGIR David M. Blei, Michael I. Jordan
2002	A	`conf`	Learning with Scope, with Application to Information Extraction and Classification. UAI David M. Blei, J. Andrew Bagnell, Andrew Kachites McCallum
2001	—	`conf`	Latent Dirichlet Allocation. NIPS David M. Blei, Andrew Y. Ng, Michael I. Jordan
2001	A*	`conf`	Topic Segmentation with an Aspect Hidden Markov Model. SIGIR David M. Blei, Pedro J. Moreno