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David Blei - On the Assumptions of Synthetic Control Methods
David Blei
Author Information
David Blei (Columbia University)
David Blei is a Professor of Statistics and Computer Science at Columbia University, and a member of the Columbia Data Science Institute. His research is in statistical machine learning, involving probabilistic topic models, Bayesian nonparametric methods, and approximate posterior inference algorithms for massive data. He works on a variety of applications, including text, images, music, social networks, user behavior, and scientific data. David has received several awards for his research, including a Sloan Fellowship (2010), Office of Naval Research Young Investigator Award (2011), Presidential Early Career Award for Scientists and Engineers (2011), Blavatnik Faculty Award (2013), and ACM-Infosys Foundation Award (2013). He is a fellow of the ACM.
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2022 : A Bayesian Causal Inference Approach for Assessing Fairness in Clinical Decision-Making »
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2022 : CAREER: Economic Prediction of Labor Sequence Data Under Distribution Shift »
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2023 Poster: Twisting Towards Perfection: Asymptotically Exact Conditional Sampling in Diffusion Models »
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2023 Poster: Causal-structure Driven Augmentations for Text OOD Generalization »
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2023 Poster: Nonparametric Identifiability of Causal Representations from Unknown Interventions »
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2023 Poster: Evaluating the Moral Beliefs Encoded in LLMs »
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2023 Poster: Variational Inference with Gaussian Score Matching »
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2022 : CAREER: Economic Prediction of Labor Sequence Data Under Distribution Shift »
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2021 Test Of Time: Online Learning for Latent Dirichlet Allocation »
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2021 Poster: Posterior Collapse and Latent Variable Non-identifiability »
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2020 Workshop: I Can’t Believe It’s Not Better! Bridging the gap between theory and empiricism in probabilistic machine learning »
Jessica Forde · Francisco Ruiz · Melanie Fernandez Pradier · Aaron Schein · Finale Doshi-Velez · Isabel Valera · David Blei · Hanna Wallach -
2020 Poster: Markovian Score Climbing: Variational Inference with KL(p||q) »
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2019 Poster: Poisson-Randomized Gamma Dynamical Systems »
Aaron Schein · Scott Linderman · Mingyuan Zhou · David Blei · Hanna Wallach -
2019 Poster: Variational Bayes under Model Misspecification »
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2019 Poster: Using Embeddings to Correct for Unobserved Confounding in Networks »
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2019 Poster: Adapting Neural Networks for the Estimation of Treatment Effects »
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2018 : Datasets and Benchmarks for Causal Learning »
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2018 : The Blessings of Multiple Causes »
David Blei -
2017 : Panel: On the Foundations and Future of Approximate Inference »
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2017 Workshop: Advances in Approximate Bayesian Inference »
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2017 Poster: Hierarchical Implicit Models and Likelihood-Free Variational Inference »
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2017 Poster: Structured Embedding Models for Grouped Data »
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2017 Poster: Variational Inference via $\chi$ Upper Bound Minimization »
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2017 Poster: Context Selection for Embedding Models »
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2016 : Causal Inference for Recommendation Systems »
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2016 : Panel Discussion »
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2016 : Deep exponential families »
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2016 Workshop: Advances in Approximate Bayesian Inference »
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2016 Poster: Operator Variational Inference »
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2016 Poster: The Generalized Reparameterization Gradient »
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2016 Poster: Exponential Family Embeddings »
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2016 Tutorial: Variational Inference: Foundations and Modern Methods »
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2015 Workshop: Advances in Approximate Bayesian Inference »
Dustin Tran · Tamara Broderick · Stephan Mandt · James McInerney · Shakir Mohamed · Alp Kucukelbir · Matthew D. Hoffman · Neil Lawrence · David Blei -
2015 Poster: The Population Posterior and Bayesian Modeling on Streams »
James McInerney · Rajesh Ranganath · David Blei -
2015 Poster: Automatic Variational Inference in Stan »
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2015 Spotlight: Automatic Variational Inference in Stan »
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2015 Poster: Copula variational inference »
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