
TextBased Ideal Points
Ideal point models analyze lawmakers' votes to quantify their political ...
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Towards Clarifying the Theory of the Deconfounder
Wang and Blei (2019) studies multiple causal inference and proposes the ...
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PoissonRandomized Gamma Dynamical Systems
This paper presents the Poissonrandomized gamma dynamical system (PRGDS...
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The Blessings of Multiple Causes: A Reply to Ogburn et al. (2019)
Ogburn et al. (2019, arXiv:1910.05438) discuss "The Blessings of Multipl...
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Prescribed Generative Adversarial Networks
Generative adversarial networks (GANs) are a powerful approach to unsupe...
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Population Predictive Checks
Bayesian modeling has become a staple for researchers analyzing data. Th...
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The Dynamic Embedded Topic Model
Topic modeling analyzes documents to learn meaningful patterns of words....
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Topic Modeling in Embedding Spaces
Topic modeling analyzes documents to learn meaningful patterns of words....
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Bayesian Tensor Filtering: Smooth, LocallyAdaptive Factorization of Functional Matrices
We consider the problem of functional matrix factorization, finding low...
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Adapting Neural Networks for the Estimation of Treatment Effects
This paper addresses the use of neural networks for the estimation of tr...
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Multiple Causes: A Causal Graphical View
Unobserved confounding is a major hurdle for causal inference from obser...
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Using Text Embeddings for Causal Inference
We address causal inference with text documents. For example, does addin...
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Equal Opportunity and Affirmative Action via Counterfactual Predictions
Machine learning (ML) can automate decisionmaking by learning to predic...
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Variational Bayes under Model Misspecification
Variational Bayes (VB) is a scalable alternative to Markov chain Monte C...
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The Medical Deconfounder: Assessing Treatment Effect with Electronic Health Records (EHRs)
Causal estimation of treatment effect has an important role in guiding p...
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Using Embeddings to Correct for Unobserved Confounding
We consider causal inference in the presence of unobserved confounding. ...
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Doseresponse modeling in highthroughput cancer drug screenings: A case study with recommendations for practitioners
Personalized cancer treatments based on the molecular profile of a patie...
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A Probabilistic Model of Cardiac Physiology and Electrocardiograms
An electrocardiogram (EKG) is a common, noninvasive test that measures ...
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The Holdout Randomization Test: Principled and Easy Black Box Feature Selection
We consider the problem of feature selection using black box predictive ...
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The Deconfounded Recommender: A Causal Inference Approach to Recommendation
The goal of a recommender system is to show its users items that they wi...
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Avoiding Latent Variable Collapse With Generative Skip Models
Variational autoencoders (VAEs) learn distributions of highdimensional ...
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Empirical Risk Minimization and Stochastic Gradient Descent for Relational Data
Empirical risk minimization is the principal tool for prediction problem...
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Black Box FDR
Analyzing largescale, multiexperiment studies requires scientists to t...
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The Blessings of Multiple Causes
Causal inference from observation data often assumes "strong ignorabilit...
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Noisin: Unbiased Regularization for Recurrent Neural Networks
Recurrent neural networks (RNNs) are powerful models of sequential data....
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Equation Embeddings
We present an unsupervised approach for discovering semantic representat...
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Augment and Reduce: Stochastic Inference for Large Categorical Distributions
Categorical distributions are ubiquitous in machine learning, e.g., in c...
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SHOPPER: A Probabilistic Model of Consumer Choice with Substitutes and Complements
We develop SHOPPER, a sequential probabilistic model of market baskets. ...
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Implicit Causal Models for Genomewide Association Studies
Progress in probabilistic generative models has accelerated, developing ...
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Variational Sequential Monte Carlo
Variational inference underlies many recent advances in large scale prob...
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Proximity Variational Inference
Variational inference is a powerful approach for approximate posterior i...
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Frequentist Consistency of Variational Bayes
A key challenge for modern Bayesian statistics is how to perform scalabl...
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Stochastic Gradient Descent as Approximate Bayesian Inference
Stochastic Gradient Descent with a constant learning rate (constant SGD)...
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Hierarchical Implicit Models and LikelihoodFree Variational Inference
Implicit probabilistic models are a flexible class of models defined by ...
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Deep Probabilistic Programming
We propose Edward, a Turingcomplete probabilistic programming language....
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Variational Inference via χUpper Bound Minimization
Variational inference (VI) is widely used as an efficient alternative to...
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Edward: A library for probabilistic modeling, inference, and criticism
Probabilistic modeling is a powerful approach for analyzing empirical in...
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Operator Variational Inference
Variational inference is an umbrella term for algorithms which cast Baye...
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Recurrent switching linear dynamical systems
Many natural systems, such as neurons firing in the brain or basketball ...
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Reparameterization Gradients through AcceptanceRejection Sampling Algorithms
Variational inference using the reparameterization trick has enabled lar...
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The Generalized Reparameterization Gradient
The reparameterization gradient has become a widely used method to obtai...
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Exponential Family Embeddings
Word embeddings are a powerful approach for capturing semantic similarit...
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Robust Probabilistic Modeling with Bayesian Data Reweighting
Probabilistic models analyze data by relying on a set of assumptions. Da...
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Bayesian Poisson Tucker Decomposition for Learning the Structure of International Relations
We introduce Bayesian Poisson Tucker decomposition (BPTD) for modeling c...
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Posterior Dispersion Indices
Probabilistic modeling is cyclical: we specify a model, infer its poster...
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Overdispersed BlackBox Variational Inference
We introduce overdispersed blackbox variational inference, a method to ...
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Automatic Differentiation Variational Inference
Probabilistic modeling is iterative. A scientist posits a simple model, ...
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A Variational Analysis of Stochastic Gradient Algorithms
Stochastic Gradient Descent (SGD) is an important algorithm in machine l...
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Variational Inference: A Review for Statisticians
One of the core problems of modern statistics is to approximate difficul...
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The Variational Gaussian Process
Variational inference is a powerful tool for approximate inference, and ...
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David M. Blei
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David M. Blei is a professor in Columbia University’s Departments of Statistics and Computer Science. Before autumn 2014, he was Associate Professor at Princeton University’s Department of Computer Science. His work is mainly in machine education.
He was one of the original developers of the latent Dirichlet assignment. his research interests include subject models. His publications were cited 50,850 times as of October 25, 2017, giving him an Hindex of 64.
He was named Fellow of the ACM “For contributions to probabilistic subject modeling and Bayesian machine learning theory and practice” in 2015.