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Text-Based Ideal Points


May 08, 2020
Keyon Vafa, Suresh Naidu, David M. Blei

* To appear in the Proceedings of the 2020 Conference of the Association for Computational Linguistics (ACL 2020) 

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Towards Clarifying the Theory of the Deconfounder


Mar 10, 2020
Yixin Wang, David M. Blei


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Poisson-Randomized Gamma Dynamical Systems


Oct 28, 2019
Aaron Schein, Scott W. Linderman, Mingyuan Zhou, David M. Blei, Hanna Wallach

* To appear in the Proceedings of the 32nd Advances in Neural Information Processing Systems (NeurIPS 2019) 

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The Blessings of Multiple Causes: A Reply to Ogburn et al. (2019)


Oct 17, 2019
Yixin Wang, David M. Blei


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Prescribed Generative Adversarial Networks


Oct 09, 2019
Adji B. Dieng, Francisco J. R. Ruiz, David M. Blei, Michalis K. Titsias

* Code for this paper can be found at https://github.com/adjidieng/PresGANs 

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Population Predictive Checks


Aug 08, 2019
Rajesh Ranganath, David M. Blei


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The Dynamic Embedded Topic Model


Jul 12, 2019
Adji B. Dieng, Francisco J. R. Ruiz, David M. Blei


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Topic Modeling in Embedding Spaces


Jul 08, 2019
Adji B. Dieng, Francisco J. R. Ruiz, David M. Blei

* Code can be found at https://github.com/adjidieng/ETM 

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Bayesian Tensor Filtering: Smooth, Locally-Adaptive Factorization of Functional Matrices


Jun 10, 2019
Wesley Tansey, Christopher Tosh, David M. Blei


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Adapting Neural Networks for the Estimation of Treatment Effects


Jun 05, 2019
Claudia Shi, David M. Blei, Victor Veitch


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Multiple Causes: A Causal Graphical View


May 30, 2019
Yixin Wang, David M. Blei

* 23 pages 

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Using Text Embeddings for Causal Inference


May 29, 2019
Victor Veitch, Dhanya Sridhar, David M. Blei


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Equal Opportunity and Affirmative Action via Counterfactual Predictions


May 29, 2019
Yixin Wang, Dhanya Sridhar, David M. Blei

* 18 pages 

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Variational Bayes under Model Misspecification


May 26, 2019
Yixin Wang, David M. Blei

* 27 pages 

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The Medical Deconfounder: Assessing Treatment Effect with Electronic Health Records (EHRs)


Apr 03, 2019
Linying Zhang, Yixin Wang, Anna Ostropolets, Jami J. Mulgrave, David M. Blei, George Hripcsak


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Using Embeddings to Correct for Unobserved Confounding


Feb 11, 2019
Victor Veitch, Yixin Wang, David M. Blei


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A Probabilistic Model of Cardiac Physiology and Electrocardiograms


Dec 01, 2018
Andrew C. Miller, Ziad Obermeyer, David M. Blei, John P. Cunningham, Sendhil Mullainathan

* Machine Learning for Health (ML4H) Workshop at NeurIPS 2018 arXiv:cs/0101200 

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The Deconfounded Recommender: A Causal Inference Approach to Recommendation


Aug 20, 2018
Yixin Wang, Dawen Liang, Laurent Charlin, David M. Blei

* 14 pages, 3 figures 

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Noisin: Unbiased Regularization for Recurrent Neural Networks


Jul 13, 2018
Adji B. Dieng, Rajesh Ranganath, Jaan Altosaar, David M. Blei

* In Proceedings of the International Conference on Machine Learning, 2018 

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Avoiding Latent Variable Collapse With Generative Skip Models


Jul 12, 2018
Adji B. Dieng, Yoon Kim, Alexander M. Rush, David M. Blei

* Presented at Workshop on Theoretical Foundations and Applications of Deep Generative Models, ICML, 2018 

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SHOPPER: A Probabilistic Model of Consumer Choice with Substitutes and Complements


Jul 02, 2018
Francisco J. R. Ruiz, Susan Athey, David M. Blei

* 27 pages, 4 figures, submitted to AOAS 

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Empirical Risk Minimization and Stochastic Gradient Descent for Relational Data


Jun 27, 2018
Victor Veitch, Morgane Austern, Wenda Zhou, David M. Blei, Peter Orbanz

* 23 pages, 1 figure 

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Robust Probabilistic Modeling with Bayesian Data Reweighting


Jun 19, 2018
Yixin Wang, Alp Kucukelbir, David M. Blei

* In ICML 2017. Updated related work 

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The Blessings of Multiple Causes


Jun 19, 2018
Yixin Wang, David M. Blei

* 59 pages 

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Black Box FDR


Jun 08, 2018
Wesley Tansey, Yixin Wang, David M. Blei, Raul Rabadan

* To appear at ICML'18; code available at https://github.com/tansey/bb-fdr 

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Augment and Reduce: Stochastic Inference for Large Categorical Distributions


Jun 07, 2018
Francisco J. R. Ruiz, Michalis K. Titsias, Adji B. Dieng, David M. Blei

* Francisco J. R. Ruiz, Michalis K. Titsias, Adji B. Dieng, and David M. Blei. Augment and Reduce: Stochastic Inference for Large Categorical Distributions. International Conference on Machine Learning. Stockholm (Sweden), July 2018 
* 11 pages, 2 figures 

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Frequentist Consistency of Variational Bayes


May 24, 2018
Yixin Wang, David M. Blei

* 58 pages 

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Variational Inference: A Review for Statisticians


May 09, 2018
David M. Blei, Alp Kucukelbir, Jon D. McAuliffe

* Journal of the American Statistical Association, Vol. 112 , Iss. 518, 2017 

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Equation Embeddings


Mar 24, 2018
Kriste Krstovski, David M. Blei

* 12 pages, 2 figures 

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