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Learning Correlated Latent Representations with Adaptive Priors


Jul 16, 2019
Da Tang, Dawen Liang, Nicholas Ruozzi, Tony Jebara

* 12pages, 2 figures 

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Correlated Variational Auto-Encoders


May 16, 2019
Da Tang, Dawen Liang, Tony Jebara, Nicholas Ruozzi

* International Conference on Machine Learning (ICML), 2019 

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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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Variational Autoencoders for Collaborative Filtering


Feb 16, 2018
Dawen Liang, Rahul G. Krishnan, Matthew D. Hoffman, Tony Jebara

* 10 pages, 3 figures. WWW 2018 

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On the challenges of learning with inference networks on sparse, high-dimensional data


Oct 17, 2017
Rahul G. Krishnan, Dawen Liang, Matthew Hoffman

* 14 pages, 3 tables, 11 figures 

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Edward: A library for probabilistic modeling, inference, and criticism


Feb 01, 2017
Dustin Tran, Alp Kucukelbir, Adji B. Dieng, Maja Rudolph, Dawen Liang, David M. Blei


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Modeling User Exposure in Recommendation


Feb 04, 2016
Dawen Liang, Laurent Charlin, James McInerney, David M. Blei

* 11 pages, 4 figures. WWW'16 

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Beta Process Non-negative Matrix Factorization with Stochastic Structured Mean-Field Variational Inference


Dec 02, 2014
Dawen Liang, Matthew D. Hoffman

* 6 pages, 1 figure 

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