David Blei

Can Generative AI Solve Your In-Context Learning Problem? A Martingale Perspective ICLR 2025 HDP-Flow: Generalizable Bayesian Nonparametric Model for Time Series State Discovery UAI 2025 Posterior Mean Matching: Generative Modeling through Online Bayesian Inference AISTATS 2025 A causality-inspired plus-minus model for player evaluation in team sports CLeaR 2024 Batch and match: black-box variational inference with a score-based divergence ICML 2024 Density Uncertainty Layers for Reliable Uncertainty Estimation AISTATS 2024 Estimating the Hallucination Rate of Generative AI NeurIPS 2024 Extremely Greedy Equivalence Search UAI 2024 On the Misspecification of Linear Assumptions in Synthetic Controls AISTATS 2024 Stable Differentiable Causal Discovery ICML 2024 Treeffuser: probabilistic prediction via conditional diffusions with gradient-boosted trees NeurIPS 2024 An Invariant Learning Characterization of Controlled Text Generation ACL 2023 Probabilistic Conformal Prediction Using Conditional Random Samples AISTATS 2023 Estimating Social Influence from Observational Data CLeaR 2022 Forget-me-not! Contrastive critics for mitigating posterior collapse UAI 2022 On the Assumptions of Synthetic Control Methods AISTATS 2022 Variational Inference for Infinitely Deep Neural Networks ICML 2022 A general linear-time inference method for Gaussian Processes on one dimension JMLR 2021 A Proxy Variable View of Shared Confounding ICML 2021 Hierarchical Inducing Point Gaussian Process for Inter-domian Observations AISTATS 2021 Rationales for Sequential Predictions EMNLP 2021 Unsupervised Representation Learning via Neural Activation Coding ICML 2021 variational combinatorial sequential monte carlo methods for bayesian phylogenetic inference UAI 2021 Adapting Text Embeddings for Causal Inference UAI 2020 Text-Based Ideal Points ACL 2020 Adapting Neural Networks for the Estimation of Treatment Effects NeurIPS 2019 Poisson-Randomized Gamma Dynamical Systems NeurIPS 2019 Using Embeddings to Correct for Unobserved Confounding in Networks NeurIPS 2019 Variational Bayes under Model Misspecification NeurIPS 2019 Augment and Reduce: Stochastic Inference for Large Categorical Distributions ICML 2018 Black Box FDR ICML 2018 Noisin: Unbiased Regularization for Recurrent Neural Networks ICML 2018 Proximity Variational Inference AISTATS 2018 Variational Sequential Monte Carlo AISTATS 2018 Bayesian Learning and Inference in Recurrent Switching Linear Dynamical Systems AISTATS 2017 Context Selection for Embedding Models NeurIPS 2017 Hierarchical Implicit Models and Likelihood-Free Variational Inference NeurIPS 2017 Reparameterization Gradients through Acceptance-Rejection Sampling Algorithms AISTATS 2017 Structured Embedding Models for Grouped Data NeurIPS 2017 Variational Inference via χ Upper Bound Minimization NeurIPS 2017 A Variational Analysis of Stochastic Gradient Algorithms ICML 2016 Bayesian Poisson Tucker Decomposition for Learning the Structure of International Relations ICML 2016 Deep Survival Analysis MLHC 2016 Detecting and Characterizing Events EMNLP 2016 Exponential Family Embeddings NeurIPS 2016 Hierarchical Variational Models ICML 2016 Operator Variational Inference NeurIPS 2016 The Generalized Reparameterization Gradient NeurIPS 2016 Variational Tempering AISTATS 2016 Automatic Variational Inference in Stan NeurIPS 2015 Copula variational inference NeurIPS 2015 Deep Exponential Families AISTATS 2015 Stochastic Structured Variational Inference AISTATS 2015 The Population Posterior and Bayesian Modeling on Streams NeurIPS 2015 A Filtering Approach to Stochastic Variational Inference NeurIPS 2014 Bayesian Nonparametric Poisson Factorization for Recommendation Systems AISTATS 2014 Black Box Variational Inference AISTATS 2014 Content-based recommendations with Poisson factorization NeurIPS 2014 Smoothed Gradients for Stochastic Variational Inference NeurIPS 2014 The Inverse Regression Topic Model ICML 2014 Efficient Online Inference for Bayesian Nonparametric Relational Models NeurIPS 2013 Modeling Overlapping Communities with Node Popularities NeurIPS 2013 Stick-Breaking Beta Processes and the Poisson Process AISTATS 2012 Bayesian Checking for Topic Models EMNLP 2011 The Discrete Infinite Logistic Normal Distribution for Mixed-Membership Modeling AISTATS 2011 Dirichlet Process Mixtures of Generalized Linear Models AISTATS 2010 Exploiting Covariate Similarity in Sparse Regression via the Pairwise Elastic Net AISTATS 2010 A Topic Model for Word Sense Disambiguation EMNLP 2007 PU-BCD: Exponential Family Models for the Coarse- and Fine-Grained All-Words Tasks SemEval 2007 PUTOP: Turning Predominant Senses into a Topic Model for Word Sense Disambiguation SemEval 2007 Markov Topic Models AISTATS 2005 Relational Topic Models for Document Networks AISTATS 2005