David M. Blei

ORCID

Publication Activity (10 Years)

Years Active: 2001-2024
Publications (10 Years): 159

Top Venues

Publications

Diana Cai, Chirag Modi, Loucas Pillaud-Vivien, Charles C. Margossian, Robert M. Gower, David M. Blei, Lawrence K. Saul
Batch and match: black-box variational inference with a score-based divergence. CoRR (2024)
Bohan Wu, David M. Blei
Extending Mean-Field Variational Inference via Entropic Regularization: Theory and Computation. CoRR (2024)
Caterina De Bacco, Yixin Wang, David M. Blei
A causality-inspired plus-minus model for player evaluation in team sports. CLeaR (2024)
Andrew Jesson, Nicolas Beltran-Velez, Quentin Chu, Sweta Karlekar, Jannik Kossen, Yarin Gal, John P. Cunningham, David M. Blei
Estimating the Hallucination Rate of Generative AI. CoRR (2024)
Keyon Vafa, Emil Palikot, Tianyu Du, Ayush Kanodia, Susan Athey, David M. Blei
CAREER: A Foundation Model for Labor Sequence Data. Trans. Mach. Learn. Res. 2024 (2024)
Achille O. R. Nazaret, Claudia Shi, David M. Blei
On the Misspecification of Linear Assumptions in Synthetic Controls. AISTATS (2024)
Yookoon Park, David M. Blei
Density Uncertainty Layers for Reliable Uncertainty Estimation. AISTATS (2024)
Nicolas Beltran-Velez, Alessandro Antonio Grande, Achille Nazaret, Alp Kucukelbir, David M. Blei
Treeffuser: Probabilistic Predictions via Conditional Diffusions with Gradient-Boosted Trees. CoRR (2024)
Linying Zhang, Lauren R. Richter, Yixin Wang, Anna Ostropolets, Noémie Elhadad, David M. Blei, George Hripcsak
Causal fairness assessment of treatment allocation with electronic health records. J. Biomed. Informatics 155 (2024)
Charles C. Margossian, David M. Blei
Amortized Variational Inference: When and Why? CoRR (2023)
Yixin Wang, David M. Blei, John P. Cunningham
Posterior Collapse and Latent Variable Non-identifiability. CoRR (2023)
Luhuan Wu, Brian L. Trippe, Christian A. Naesseth, David M. Blei, John P. Cunningham
Practical and Asymptotically Exact Conditional Sampling in Diffusion Models. CoRR (2023)
Yookoon Park, David M. Blei
Density Uncertainty Layers for Reliable Uncertainty Estimation. CoRR (2023)
Luhuan Wu, Brian L. Trippe, Christian A. Naesseth, David M. Blei, John P. Cunningham
Practical and Asymptotically Exact Conditional Sampling in Diffusion Models. NeurIPS (2023)
Carolina Zheng, Keyon Vafa, David M. Blei
Revisiting Topic-Guided Language Models. Trans. Mach. Learn. Res. 2023 (2023)
Julius von Kügelgen, Michel Besserve, Wendong Liang, Luigi Gresele, Armin Kekic, Elias Bareinboim, David M. Blei, Bernhard Schölkopf
Nonparametric Identifiability of Causal Representations from Unknown Interventions. NeurIPS (2023)
Liyi Zhang, David M. Blei, Christian A. Naesseth
Transport Score Climbing: Variational Inference Using Forward KL and Adaptive Neural Transport. Trans. Mach. Learn. Res. 2023 (2023)
Yixin Wang, Dhanya Sridhar, David M. Blei
Adjusting Machine Learning Decisions for Equal Opportunity and Counterfactual Fairness. Trans. Mach. Learn. Res. 2023 (2023)
Chirag Modi, Robert Gower, Charles Margossian, Yuling Yao, David M. Blei, Lawrence K. Saul
Variational Inference with Gaussian Score Matching. NeurIPS (2023)
David M. Kaplan, David M. Blei
A Computational Approach to Style in American Poetry. CoRR (2023)
Carolina Zheng, Claudia Shi, Keyon Vafa, Amir Feder, David M. Blei
An Invariant Learning Characterization of Controlled Text Generation. CoRR (2023)
Amir Feder, Yoav Wald, Claudia Shi, Suchi Saria, David M. Blei
Causal-structure Driven Augmentations for Text OOD Generalization. NeurIPS (2023)
Carolina Zheng, Claudia Shi, Keyon Vafa, Amir Feder, David M. Blei
An Invariant Learning Characterization of Controlled Text Generation. ACL (1) (2023)
Zhendong Wang, Ruijiang Gao, Mingzhang Yin, Mingyuan Zhou, David M. Blei
Probabilistic Conformal Prediction Using Conditional Random Samples. AISTATS (2023)
Nino Scherrer, Claudia Shi, Amir Feder, David M. Blei
Evaluating the Moral Beliefs Encoded in LLMs. NeurIPS (2023)
Achille Nazaret, Justin Hong, Elham Azizi, David M. Blei
Stable Differentiable Causal Discovery. CoRR (2023)
Carolina Zheng, Keyon Vafa, David M. Blei
Revisiting Topic-Guided Language Models. CoRR (2023)
Chirag Modi, Charles Margossian, Yuling Yao, Robert Gower, David M. Blei, Lawrence K. Saul
Variational Inference with Gaussian Score Matching. CoRR (2023)
Julius von Kügelgen, Michel Besserve, Wendong Liang, Luigi Gresele, Armin Kekic, Elias Bareinboim, David M. Blei, Bernhard Schölkopf
Nonparametric Identifiability of Causal Representations from Unknown Interventions. CoRR (2023)
Amir Feder, Yoav Wald, Claudia Shi, Suchi Saria, David M. Blei
Causal-structure Driven Augmentations for Text OOD Generalization. CoRR (2023)
Nino Scherrer, Claudia Shi, Amir Feder, David M. Blei
Evaluating the Moral Beliefs Encoded in LLMs. CoRR (2023)
Liyi Zhang, Christian A. Naesseth, David M. Blei
Transport Score Climbing: Variational Inference Using Forward KL and Adaptive Neural Transport. CoRR (2022)
Linying Zhang, Yixin Wang, Martijn J. Schuemie, David M. Blei, George Hripcsak
Adjusting for indirectly measured confounding using large-scale propensity score. J. Biomed. Informatics 134 (2022)
Linying Zhang, Lauren R. Richter, Yixin Wang, Anna Ostropolets, Noemie Elhadad, David M. Blei, George Hripcsak
A Bayesian Causal Inference Approach for Assessing Fairness in Clinical Decision-Making. CoRR (2022)
Keyon Vafa, Emil Palikot, Tianyu Du, Ayush Kanodia, Susan Athey, David M. Blei
Learning Transferrable Representations of Career Trajectories for Economic Prediction. CoRR (2022)
Wesley Tansey, Victor Veitch, Haoran Zhang, Raul Rabadan, David M. Blei
The Holdout Randomization Test for Feature Selection in Black Box Models. J. Comput. Graph. Stat. 31 (1) (2022)
Achille Nazaret, David M. Blei
Variational Inference for Infinitely Deep Neural Networks. CoRR (2022)
Gemma E. Moran, Dhanya Sridhar, Yixin Wang, David M. Blei
Identifiable Deep Generative Models via Sparse Decoding. Trans. Mach. Learn. Res. 2022 (2022)
Achille Nazaret, David M. Blei
Variational Inference for Infinitely Deep Neural Networks. ICML (2022)
Claudia Shi, Dhanya Sridhar, Vishal Misra, David M. Blei
On the Assumptions of Synthetic Control Methods. AISTATS (2022)
Zhendong Wang, Ruijiang Gao, Mingzhang Yin, Mingyuan Zhou, David M. Blei
Probabilistic Conformal Prediction Using Conditional Random Samples. CoRR (2022)
Dhanya Sridhar, Hal Daume III, David M. Blei
Heterogeneous Supervised Topic Models. Trans. Assoc. Comput. Linguistics 10 (2022)
Dhanya Sridhar, Caterina De Bacco, David M. Blei
Estimating Social Influence from Observational Data. CoRR (2022)
Dhanya Sridhar, Caterina De Bacco, David M. Blei
Estimating Social Influence from Observational Data. CLeaR (2022)
Yookoon Park, Sangho Lee, Gunhee Kim, David M. Blei
Unsupervised Representation Learning via Neural Activation Coding. ICML (2021)
Aaron Schein, Keyon Vafa, Dhanya Sridhar, Victor Veitch, Jeffrey Quinn, James Moffet, David M. Blei, Donald P. Green
Assessing the Effects of Friend-to-Friend Texting onTurnout in the 2018 US Midterm Elections. WWW (2021)
Jackson Loper, David M. Blei, John P. Cunningham, Liam Paninski
A general linear-time inference method for Gaussian Processes on one dimension. J. Mach. Learn. Res. 22 (2021)
Luhuan Wu, Andrew Miller, Lauren Anderson, Geoff Pleiss, David M. Blei, John P. Cunningham
Hierarchical Inducing Point Gaussian Process for Inter-domain Observations. CoRR (2021)
Yookoon Park, Sangho Lee, Gunhee Kim, David M. Blei
Unsupervised Representation Learning via Neural Activation Coding. CoRR (2021)
Luhuan Wu, Andrew Miller, Lauren Anderson, Geoff Pleiss, David M. Blei, John P. Cunningham
Hierarchical Inducing Point Gaussian Process for Inter-domian Observations. AISTATS (2021)
Mingzhang Yin, Yixin Wang, David M. Blei
Optimization-based Causal Estimation from Heterogenous Environments. CoRR (2021)
Gemma E. Moran, Dhanya Sridhar, Yixin Wang, David M. Blei
Identifiable Variational Autoencoders via Sparse Decoding. CoRR (2021)
Antonio Khalil Moretti, Liyi Zhang, Christian A. Naesseth, Hadiah Venner, David M. Blei, Itsik Pe'er
Variational Combinatorial Sequential Monte Carlo Methods for Bayesian Phylogenetic Inference. CoRR (2021)
Antonio Khalil Moretti, Liyi Zhang, Christian A. Naesseth, Hadiah Venner, David M. Blei, Itsik Pe'er
variational combinatorial sequential monte carlo methods for bayesian phylogenetic inference. UAI (2021)
Keyon Vafa, Yuntian Deng, David M. Blei, Alexander M. Rush
Rationales for Sequential Predictions. CoRR (2021)
Claudia Shi, Victor Veitch, David M. Blei
Invariant representation learning for treatment effect estimation. UAI (2021)
Yixin Wang, David M. Blei
A Proxy Variable View of Shared Confounding. ICML (2021)
Yixin Wang, David M. Blei, John P. Cunningham
Posterior Collapse and Latent Variable Non-identifiability. NeurIPS (2021)
Keyon Vafa, Yuntian Deng, David M. Blei, Alexander M. Rush
Rationales for Sequential Predictions. EMNLP (1) (2021)
Jackson Loper, David M. Blei, John P. Cunningham, Liam Paninski
General linear-time inference for Gaussian Processes on one dimension. CoRR (2020)
Christian A. Naesseth, Fredrik Lindsten, David M. Blei
Markovian Score Climbing: Variational Inference with KL(p||q). NeurIPS (2020)
Victor Veitch, Dhanya Sridhar, David M. Blei
Adapting Text Embeddings for Causal Inference. UAI (2020)
Keyon Vafa, Suresh Naidu, David M. Blei
Text-Based Ideal Points. CoRR (2020)
Yixin Wang, David M. Blei
Towards Clarifying the Theory of the Deconfounder. CoRR (2020)
Christian A. Naesseth, Fredrik Lindsten, David M. Blei
Markovian Score Climbing: Variational Inference with KL(p||q). CoRR (2020)
Linying Zhang, Yixin Wang, Anna Ostropolets, Ruijun Chen, David M. Blei, George Hripcsak
The Multi-Outcome Medical Deconfounder: Assessing Treatment Effect on Multiple Renal Measures. AMIA (2020)
Claudia Shi, Victor Veitch, David M. Blei
Invariant Representation Learning for Treatment Effect Estimation. CoRR (2020)
Keyon Vafa, Suresh Naidu, David M. Blei
Text-Based Ideal Points. ACL (2020)
Adji Bousso Dieng, Francisco J. R. Ruiz, David M. Blei
Topic Modeling in Embedding Spaces. Trans. Assoc. Comput. Linguistics 8 (2020)
Yixin Wang, Dawen Liang, Laurent Charlin, David M. Blei
Causal Inference for Recommender Systems. RecSys (2020)
George Hripcsak, David M. Blei, Elias Bareinboim, Martijn J. Schuemie, Linying Zhang
Causal Inference from Observational Healthcare Data: Implications, Impacts and Innovations. AMIA (2020)
Aaron Schein, Scott W. Linderman, Mingyuan Zhou, David M. Blei, Hanna M. Wallach
Poisson-Randomized Gamma Dynamical Systems. NeurIPS (2019)
Yixin Wang, Dhanya Sridhar, David M. Blei
Equal Opportunity and Affirmative Action via Counterfactual Predictions. CoRR (2019)
Victor Veitch, Morgane Austern, Wenda Zhou, David M. Blei, Peter Orbanz
Empirical Risk Minimization and Stochastic Gradient Descent for Relational Data. AISTATS (2019)
Yixin Wang, David M. Blei
Variational Bayes under Model Misspecification. CoRR (2019)
Claudia Shi, David M. Blei, Victor Veitch
Adapting Neural Networks for the Estimation of Treatment Effects. NeurIPS (2019)
Linying Zhang, Yixin Wang, Anna Ostropolets, Jami J. Mulgrave, David M. Blei, George Hripcsak
The Medical Deconfounder: Assessing Treatment Effect with Electronic Health Records (EHRs). CoRR (2019)
Adji B. Dieng, Francisco J. R. Ruiz, David M. Blei
The Dynamic Embedded Topic Model. CoRR (2019)
Rajesh Ranganath, David M. Blei
Population Predictive Checks. CoRR (2019)
Claudia Shi, David M. Blei, Victor Veitch
Adapting Neural Networks for the Estimation of Treatment Effects. CoRR (2019)
Wesley Tansey, Christopher Tosh, David M. Blei
Bayesian Tensor Filtering: Smooth, Locally-Adaptive Factorization of Functional Matrices. CoRR (2019)
Adji B. Dieng, Francisco J. R. Ruiz, David M. Blei, Michalis K. Titsias
Prescribed Generative Adversarial Networks. CoRR (2019)
Adji B. Dieng, Yoon Kim, Alexander M. Rush, David M. Blei
Avoiding Latent Variable Collapse with Generative Skip Models. AISTATS (2019)
Yixin Wang, David M. Blei
Variational Bayes under Model Misspecification. NeurIPS (2019)
Aaron Schein, Scott W. Linderman, Mingyuan Zhou, David M. Blei, Hanna M. Wallach
Poisson-Randomized Gamma Dynamical Systems. CoRR (2019)
Linying Zhang, Yixin Wang, Anna Ostropolets, Jami J. Mulgrave, David M. Blei, George Hripcsak
The Medical Deconfounder: Assessing Treatment Effects with Electronic Health Records. MLHC (2019)
Yixin Wang, David M. Blei
Multiple Causes: A Causal Graphical View. CoRR (2019)
Yixin Wang, David M. Blei
The Blessings of Multiple Causes: A Reply to Ogburn et al. (2019). CoRR (2019)
Robert Donnelly, Francisco J. R. Ruiz, David M. Blei, Susan Athey
Counterfactual Inference for Consumer Choice Across Many Product Categories. CoRR (2019)
Adji B. Dieng, Francisco J. R. Ruiz, David M. Blei
Topic Modeling in Embedding Spaces. CoRR (2019)
Victor Veitch, Yixin Wang, David M. Blei
Using Embeddings to Correct for Unobserved Confounding. CoRR (2019)
Victor Veitch, Dhanya Sridhar, David M. Blei
Using Text Embeddings for Causal Inference. CoRR (2019)
Victor Veitch, Yixin Wang, David M. Blei
Using Embeddings to Correct for Unobserved Confounding in Networks. NeurIPS (2019)
Jeremy R. Manning, Xia Zhu, Theodore L. Willke, Rajesh Ranganath, Kimberly L. Stachenfeld, Uri Hasson, David M. Blei, Kenneth A. Norman
A probabilistic approach to discovering dynamic full-brain functional connectivity patterns. NeuroImage 180 (Part) (2018)
Wesley Tansey, Yixin Wang, David M. Blei, Raul Rabadan
Black Box FDR. CoRR (2018)
Jaan Altosaar, Rajesh Ranganath, David M. Blei
Proximity Variational Inference. AISTATS (2018)
Adji B. Dieng, Rajesh Ranganath, Jaan Altosaar, David M. Blei
Noisin: Unbiased Regularization for Recurrent Neural Networks. CoRR (2018)
Yixin Wang, Dawen Liang, Laurent Charlin, David M. Blei
The Deconfounded Recommender: A Causal Inference Approach to Recommendation. CoRR (2018)
Wesley Tansey, Yixin Wang, David M. Blei, Raul Rabadan
Black Box FDR. ICML (2018)
David M. Blei
Technical perspective: Expressive probabilistic models and scalable method of moments. Commun. ACM 61 (4) (2018)

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Publication Activity (10 Years)

Top Topics

Top Venues

David M. Blei