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Francisco J. R. Ruiz

Order Matters: Probabilistic Modeling of Node Sequence for Graph Generation


Jun 11, 2021
Xiaohui Chen, Xu Han, Jiajing Hu, Francisco J. R. Ruiz, Liping Liu

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* ICML 2021 
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VarGrad: A Low-Variance Gradient Estimator for Variational Inference


Oct 29, 2020
Lorenz Richter, Ayman Boustati, Nikolas Nüsken, Francisco J. R. Ruiz, Ömer Deniz Akyildiz

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Unbiased Gradient Estimation for Variational Auto-Encoders using Coupled Markov Chains


Oct 05, 2020
Francisco J. R. Ruiz, Michalis K. Titsias, Taylan Cemgil, Arnaud Doucet

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* 16 pages, 4 figures 
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Prescribed Generative Adversarial Networks


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

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

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* Code can be found at https://github.com/adjidieng/ETM 
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A Contrastive Divergence for Combining Variational Inference and MCMC


May 28, 2019
Francisco J. R. Ruiz, Michalis K. Titsias

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* International Conference on Machine Learning (ICML 2019). 12 pages, 3 figures 
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Poisson Multi-Bernoulli Mapping Using Gibbs Sampling


Nov 07, 2018
Maryam Fatemi, Karl Granström, Lennart Svensson, Francisco J. R. Ruiz, Lars Hammarstrand

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* IEEE Transactions on Signal Processing, Vol. 65, Issue 11, June 2017 
 * 14 pages, 6 figures 
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Infinite Factorial Finite State Machine for Blind Multiuser Channel Estimation


Oct 18, 2018
Francisco J. R. Ruiz, Isabel Valera, Lennart Svensson, Fernando Perez-Cruz

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* IEEE Transactions on Cognitive Communications and Networking, June 2018, Vol 2, Issue 2, pages 177-191 
 * 15 pages, 15 figures 
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Unbiased Implicit Variational Inference


Oct 10, 2018
Michalis K. Titsias, Francisco J. R. Ruiz

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* 9 pages, 3 figures 
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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

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* 27 pages, 4 figures, submitted to AOAS 
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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

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* 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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Reparameterization Gradients through Acceptance-Rejection Sampling Algorithms


Mar 10, 2017
Christian A. Naesseth, Francisco J. R. Ruiz, Scott W. Linderman, David M. Blei

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Exponential Family Embeddings


Nov 21, 2016
Maja R. Rudolph, Francisco J. R. Ruiz, Stephan Mandt, David M. Blei

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The Generalized Reparameterization Gradient


Oct 19, 2016
Francisco J. R. Ruiz, Michalis K. Titsias, David M. Blei

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* 16 pages, 15 figures, NIPS version 
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Overdispersed Black-Box Variational Inference


Mar 03, 2016
Francisco J. R. Ruiz, Michalis K. Titsias, David M. Blei

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* 10 pages, 6 figures 
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Bayesian nonparametric comorbidity analysis of psychiatric disorders


Jan 29, 2014
Francisco J. R. Ruiz, Isabel Valera, Carlos Blanco, Fernando Perez-Cruz

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* Submitted to Journal of Machine Learning Research 
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