76 citations · 348 across the 19 of their papers we have counts for
13 papers · 1 filter
Order Matters: Probabilistic Modeling of Node Sequence for Graph Generation
Xiaohui Chen, Xu Han, Jiajing Hu +2
A graph generative model defines a distribution over graphs. One type of generative model is constructed by autoregressive neural networks, which sequentially add nodes and edges t…
VarGrad: A Low-Variance Gradient Estimator for Variational Inference
Lorenz Richter, Ayman Boustati, Nikolas Nüsken +2
We analyse the properties of an unbiased gradient estimator of the ELBO for variational inference, based on the score function method with leave-one-out control variates. We show t…
Prescribed Generative Adversarial Networks
Adji B. Dieng, Francisco J. R. Ruiz, David M. Blei +1
Generative adversarial networks (GANs) are a powerful approach to unsupervised learning. They have achieved state-of-the-art performance in the image domain. However, GANs are limi…
A Contrastive Divergence for Combining Variational Inference and MCMC
Francisco J. R. Ruiz, Michalis K. Titsias
We develop a method to combine Markov chain Monte Carlo (MCMC) and variational inference (VI), leveraging the advantages of both inference approaches. Specifically, we improve the…
Poisson Multi-Bernoulli Mapping Using Gibbs Sampling
Maryam Fatemi, Karl Granström, Lennart Svensson +2
This paper addresses the mapping problem. Using a conjugate prior form, we derive the exact theoretical batch multi-object posterior density of the map given a set of measurements.…
Unbiased Implicit Variational Inference
Michalis K. Titsias, Francisco J. R. Ruiz
We develop unbiased implicit variational inference (UIVI), a method that expands the applicability of variational inference by defining an expressive variational family. UIVI consi…
Augment and Reduce: Stochastic Inference for Large Categorical Distributions
Francisco J. R. Ruiz, Michalis K. Titsias, Adji B. Dieng +1
Categorical distributions are ubiquitous in machine learning, e.g., in classification, language models, and recommendation systems. However, when the number of possible outcomes is…
SHOPPER: A Probabilistic Model of Consumer Choice with Substitutes and Complements
Francisco J. R. Ruiz, Susan Athey, David M. Blei
We develop SHOPPER, a sequential probabilistic model of shopping data. SHOPPER uses interpretable components to model the forces that drive how a customer chooses products; in part…
The Generalized Reparameterization Gradient
Francisco J. R. Ruiz, Michalis K. Titsias, David M. Blei
The reparameterization gradient has become a widely used method to obtain Monte Carlo gradients to optimize the variational objective. However, this technique does not easily apply…
Reparameterization Gradients through Acceptance-Rejection Sampling Algorithms
Christian A. Naesseth, Francisco J. R. Ruiz, Scott W. Linderman +1
Variational inference using the reparameterization trick has enabled large-scale approximate Bayesian inference in complex probabilistic models, leveraging stochastic optimization…
Exponential Family Embeddings
Maja R. Rudolph, Francisco J. R. Ruiz, Stephan Mandt +1
Word embeddings are a powerful approach for capturing semantic similarity among terms in a vocabulary. In this paper, we develop exponential family embeddings, a class of methods t…
Overdispersed Black-Box Variational Inference
Francisco J. R. Ruiz, Michalis K. Titsias, David M. Blei
We introduce overdispersed black-box variational inference, a method to reduce the variance of the Monte Carlo estimator of the gradient in black-box variational inference. Instead…
Bayesian nonparametric comorbidity analysis of psychiatric disorders
Francisco J. R. Ruiz, Isabel Valera, Carlos Blanco +1
The analysis of comorbidity is an open and complex research field in the branch of psychiatry, where clinical experience and several studies suggest that the relation among the psy…