Blei, David

74 publications

ICLRW 2025 Bayesian Invariance Modeling of Multi-Environment Data Luhuan Wu, Mingzhang Yin, Yixin Wang, John Patrick Cunningham, David Blei

ICLR 2025 Can Generative AI Solve Your In-Context Learning Problem? a Martingale Perspective Andrew Jesson, Nicolas Beltran-Velez, David Blei

ICLRW 2025 Distributionally Robust Posterior Sampling - A Variational Bayes Approach Bohan Wu, Bennett Zhu, David Blei

NeurIPS 2025 Fisher Meets Feynman: Score-Based Variational Inference with a Product of Experts Diana Cai, Robert M. Gower, David Blei, Lawrence K. Saul

UAI 2025 HDP-Flow: Generalizable Bayesian Nonparametric Model for Time Series State Discovery Sana Tonekaboni, Tina Behrouzi, Addison Weatherhead, Emily Fox, David Blei, Anna Goldenberg

AISTATS 2025 Posterior Mean Matching: Generative Modeling Through Online Bayesian Inference Sebastian Salazar, Michal Kucer, Yixin Wang, Emily Casleton, David Blei

NeurIPS 2025 Quantifying Uncertainty in the Presence of Distribution Shifts Yuli Slavutsky, David Blei

CLeaR 2024 A Causality-Inspired Plus-Minus Model for Player Evaluation in Team Sports Caterina De Bacco, Yixin Wang, David Blei

ICML 2024 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 Blei, Lawrence K. Saul

TMLR 2024 CAREER: A Foundation Model for Labor Sequence Data Keyon Vafa, Emil Palikot, Tianyu Du, Ayush Kanodia, Susan Athey, David Blei

NeurIPSW 2024 Can Generative AI Solve Your In-Context Learning Problem? a Martingale Perspective Andrew Jesson, Nicolas Beltran-Velez, David Blei

AISTATS 2024 Density Uncertainty Layers for Reliable Uncertainty Estimation Yookoon Park, David Blei

ICMLW 2024 EigenVI: Score-Based Variational Inference with Orthogonal Function Expansions Diana Cai, Chirag Modi, Charles Margossian, Robert M. Gower, David Blei, Lawrence K. Saul

NeurIPS 2024 Estimating the Hallucination Rate of Generative AI Andrew Jesson, Nicolas Beltran-Velez, Quentin Chu, Sweta Karlekar, Jannik Kossen, Yarin Gal, John P. Cunningham, David Blei

UAI 2024 Extremely Greedy Equivalence Search Achille Nazaret, David Blei

ICMLW 2024 Hypothesis Testing the Circuit Hypothesis in LLMs Claudia Shi, Nicolas Beltran-Velez, Achille Nazaret, Carolina Zheng, Adrià Garriga-Alonso, Andrew Jesson, Maggie Makar, David Blei

AISTATS 2024 On the Misspecification of Linear Assumptions in Synthetic Controls Achille O. R. Nazaret, Claudia Shi, David Blei

TMLR 2024 Population Priors for Matrix Factorization Sohrab Salehi, Achille Nazaret, Sohrab P Shah, David Blei

ICML 2024 Stable Differentiable Causal Discovery Achille Nazaret, Justin Hong, Elham Azizi, David Blei

ICMLW 2024 Stable Differentiable Causal Discovery Achille Nazaret, Justin Hong, Elham Azizi, David Blei

NeurIPS 2024 Treeffuser: Probabilistic Prediction via Conditional Diffusions with Gradient-Boosted Trees Nicolas Beltran-Velez, Alessandro Antonio Grande, Achille Nazaret, Alp Kucukelbir, David Blei

TMLR 2023 Adjusting Machine Learning Decisions for Equal Opportunity and Counterfactual Fairness Yixin Wang, Dhanya Sridhar, David Blei

ICMLW 2023 Practical and Asymptotically Exact Conditional Sampling in Diffusion Models Brian L. Trippe, Luhuan Wu, Christian A. Naesseth, David Blei, John Patrick Cunningham

AISTATS 2023 Probabilistic Conformal Prediction Using Conditional Random Samples Zhendong Wang, Ruijiang Gao, Mingzhang Yin, Mingyuan Zhou, David Blei

TMLR 2023 Revisiting Topic-Guided Language Models Carolina Zheng, Keyon Vafa, David Blei

TMLR 2023 Transport Score Climbing: Variational Inference Using Forward KL and Adaptive Neural Transport Liyi Zhang, David Blei, Christian A Naesseth

AISTATS 2022 On the Assumptions of Synthetic Control Methods Claudia Shi, Dhanya Sridhar, Vishal Misra, David Blei

NeurIPSW 2022 An Invariant Learning Characterization of Controlled Text Generation Claudia Shi, Carolina Zheng, Keyon Vafa, Amir Feder, David Blei

NeurIPSW 2022 An Invariant Learning Characterization of Controlled Text Generation Claudia Shi, Carolina Zheng, Keyon Vafa, Amir Feder, David Blei

NeurIPSW 2022 CAREER: Economic Prediction of Labor Sequence Data Under Distribution Shift Keyon Vafa, Emil Palikot, Tianyu Du, Ayush Kanodia, Susan Athey, David Blei

CLeaR 2022 Estimating Social Influence from Observational Data Dhanya Sridhar, Caterina De Bacco, David Blei

UAI 2022 Forget-Me-Not! Contrastive Critics for Mitigating Posterior Collapse Sachit Menon, David Blei, Carl Vondrick

TMLR 2022 Identifiable Deep Generative Models via Sparse Decoding Gemma Elyse Moran, Dhanya Sridhar, Yixin Wang, David Blei

ICMLW 2022 Optimization-Based Causal Estimation from Heterogenous Environments Mingzhang Yin, Yixin Wang, David Blei

ICML 2022 Variational Inference for Infinitely Deep Neural Networks Achille Nazaret, David Blei

AISTATS 2021 Hierarchical Inducing Point Gaussian Process for Inter-Domian Observations Luhuan Wu, Andrew Miller, Lauren Anderson, Geoff Pleiss, David Blei, John Cunningham

JMLR 2021 A General Linear-Time Inference Method for Gaussian Processes on One Dimension Jackson Loper, David Blei, John P. Cunningham, Liam Paninski

ICML 2021 A Proxy Variable View of Shared Confounding Yixin Wang, David Blei

ICML 2021 Unsupervised Representation Learning via Neural Activation Coding Yookoon Park, Sangho Lee, Gunhee Kim, David Blei

UAI 2021 Variational Combinatorial Sequential Monte Carlo Methods for Bayesian Phylogenetic Inference Antonio Khalil Moretti, Liyi Zhang, Christian A. Naesseth, Hadiah Venner, David Blei, Itsik Pe’er

UAI 2020 Adapting Text Embeddings for Causal Inference Victor Veitch, Dhanya Sridhar, David Blei

NeurIPS 2019 Adapting Neural Networks for the Estimation of Treatment Effects Claudia Shi, David Blei, Victor Veitch

NeurIPS 2019 Poisson-Randomized Gamma Dynamical Systems Aaron Schein, Scott Linderman, Mingyuan Zhou, David Blei, Hanna Wallach

NeurIPS 2019 Using Embeddings to Correct for Unobserved Confounding in Networks Victor Veitch, Yixin Wang, David Blei

NeurIPS 2019 Variational Bayes Under Model Misspecification Yixin Wang, David Blei

ICML 2018 Augment and Reduce: Stochastic Inference for Large Categorical Distributions Francisco Ruiz, Michalis Titsias, Adji Bousso Dieng, David Blei

ICML 2018 Black Box FDR Wesley Tansey, Yixin Wang, David Blei, Raul Rabadan

ICML 2018 Noisin: Unbiased Regularization for Recurrent Neural Networks Adji Bousso Dieng, Rajesh Ranganath, Jaan Altosaar, David Blei

NeurIPS 2017 Context Selection for Embedding Models Liping Liu, Francisco Ruiz, Susan Athey, David Blei

NeurIPS 2017 Hierarchical Implicit Models and Likelihood-Free Variational Inference Dustin Tran, Rajesh Ranganath, David Blei

NeurIPS 2017 Structured Embedding Models for Grouped Data Maja Rudolph, Francisco Ruiz, Susan Athey, David Blei

NeurIPS 2017 Variational Inference via $\chi$ Upper Bound Minimization Adji Bousso Dieng, Dustin Tran, Rajesh Ranganath, John Paisley, David Blei

ICML 2016 A Variational Analysis of Stochastic Gradient Algorithms Stephan Mandt, Matthew Hoffman, David Blei

ICML 2016 Bayesian Poisson Tucker Decomposition for Learning the Structure of International Relations Aaron Schein, Mingyuan Zhou, David Blei, Hanna Wallach

MLHC 2016 Deep Survival Analysis Rajesh Ranganath, Adler Perotte, Noémie Elhadad, David Blei

NeurIPS 2016 Exponential Family Embeddings Maja Rudolph, Francisco Ruiz, Stephan Mandt, David Blei

ICML 2016 Hierarchical Variational Models Rajesh Ranganath, Dustin Tran, David Blei

NeurIPS 2016 Operator Variational Inference Rajesh Ranganath, Dustin Tran, Jaan Altosaar, David Blei

NeurIPS 2016 The Generalized Reparameterization Gradient Francisco R Ruiz, Michalis Titsias RC Aueb, David Blei

NeurIPS 2015 Automatic Variational Inference in Stan Alp Kucukelbir, Rajesh Ranganath, Andrew Gelman, David Blei

NeurIPS 2015 Copula Variational Inference Dustin Tran, David Blei, Edoardo M. Airoldi

NeurIPS 2015 The Population Posterior and Bayesian Modeling on Streams James McInerney, Rajesh Ranganath, David Blei

NeurIPS 2014 A Filtering Approach to Stochastic Variational Inference Neil Houlsby, David Blei

NeurIPS 2014 Content-Based Recommendations with Poisson Factorization Prem Gopalan, Laurent Charlin, David Blei

NeurIPS 2014 Smoothed Gradients for Stochastic Variational Inference Stephan Mandt, David Blei

ICML 2014 The Inverse Regression Topic Model Maxim Rabinovich, David Blei

NeurIPS 2013 Efficient Online Inference for Bayesian Nonparametric Relational Models Dae Il Kim, Prem Gopalan, David Blei, Erik Sudderth

NeurIPS 2013 Modeling Overlapping Communities with Node Popularities Prem Gopalan, Chong Wang, David Blei

AISTATS 2012 Stick-Breaking Beta Processes and the Poisson Process John Paisley, David Blei, Michael Jordan

AISTATS 2011 The Discrete Infinite Logistic Normal Distribution for Mixed-Membership Modeling John Paisley, Chong Wang, David Blei

AISTATS 2010 Dirichlet Process Mixtures of Generalized Linear Models Lauren Hannah, David Blei, Warren Powell

AISTATS 2010 Exploiting Covariate Similarity in Sparse Regression via the Pairwise Elastic Net Alexander Lorbert, David Eis, Victoria Kostina, David Blei, Peter Ramadge

AISTATS 2009 Markov Topic Models Chong Wang, Bo Thiesson, Chris Meek, David Blei

AISTATS 2009 Relational Topic Models for Document Networks Jonathan Chang, David Blei