76 citations · 348 across the 19 of their papers we have counts for
21 papers
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…
Unbiased Gradient Estimation for Variational Auto-Encoders using Coupled Markov Chains
Francisco J. R. Ruiz, Michalis K. Titsias, Taylan Cemgil +1
The variational auto-encoder (VAE) is a deep latent variable model that has two neural networks in an autoencoder-like architecture; one of them parameterizes the model's likelihoo…
Information Theoretic Meta Learning with Gaussian Processes
Michalis K. Titsias, Francisco J. R. Ruiz, Sotirios Nikoloutsopoulos +1
We formulate meta learning using information theoretic concepts; namely, mutual information and the information bottleneck. The idea is to learn a stochastic representation or enco…
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…
Topic Modeling in Embedding Spaces
Adji B. Dieng, Francisco J. R. Ruiz, David M. Blei
Topic modeling analyzes documents to learn meaningful patterns of words. However, existing topic models fail to learn interpretable topics when working with large and heavy-tailed…
The Dynamic Embedded Topic Model
Adji B. Dieng, Francisco J. R. Ruiz, David M. Blei
Topic modeling analyzes documents to learn meaningful patterns of words. For documents collected in sequence, dynamic topic models capture how these patterns vary over time. We dev…
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.…
Infinite Factorial Finite State Machine for Blind Multiuser Channel Estimation
Francisco J. R. Ruiz, Isabel Valera, Lennart Svensson +1
New communication standards need to deal with machine-to-machine communications, in which users may start or stop transmitting at any time in an asynchronous manner. Thus, the numb…
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…
Estimating Heterogeneous Consumer Preferences for Restaurants and Travel Time Using Mobile Location Data
Susan Athey, David Blei, Robert Donnelly +2
This paper analyzes consumer choices over lunchtime restaurants using data from a sample of several thousand anonymous mobile phone users in the San Francisco Bay Area. The data 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…
Structured Embedding Models for Grouped Data
Maja Rudolph, Francisco Ruiz, Susan Athey +1
Word embeddings are a powerful approach for analyzing language, and exponential family embeddings (EFE) extend them to other types of data. Here we develop structured exponential f…
Model Criticism for Bayesian Causal Inference
Dustin Tran, Francisco J. R. Ruiz, Susan Athey +1
The goal of causal inference is to understand the outcome of alternative courses of action. However, all causal inference requires assumptions. Such assumptions can be more influen…
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…