108 citations · 112 across the 3 of their papers we have counts for
6 papers · 1 filter
VACA: Design of Variational Graph Autoencoders for Interventional and Counterfactual Queries
Pablo Sanchez-Martin, Miriam Rateike, Isabel Valera
In this paper, we introduce VACA, a novel class of variational graph autoencoders for causal inference in the absence of hidden confounders, when only observational data and the ca…
Automatic Bayesian Density Analysis
Antonio Vergari, Alejandro Molina, Robert Peharz +3
Making sense of a dataset in an automatic and unsupervised fashion is a challenging problem in statistics and AI. Classical approaches for {exploratory data analysis} are usually n…
Boosting Black Box Variational Inference
Francesco Locatello, Gideon Dresdner, Rajiv Khanna +2
Approximating a probability density in a tractable manner is a central task in Bayesian statistics. Variational Inference (VI) is a popular technique that achieves tractability by…
Enhancing the Accuracy and Fairness of Human Decision Making
Isabel Valera, Adish Singla, Manuel Gomez Rodriguez
Societies often rely on human experts to take a wide variety of decisions affecting their members, from jail-or-release decisions taken by judges and stop-and-frisk decisions taken…
General Latent Feature Modeling for Data Exploration Tasks
Isabel Valera, Melanie F. Pradier, Zoubin Ghahramani
This paper introduces a general Bayesian non- parametric latent feature model suitable to per- form automatic exploratory analysis of heterogeneous datasets, where the attributes d…
From Parity to Preference-based Notions of Fairness in Classification
Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez Rodriguez +2
The adoption of automated, data-driven decision making in an ever expanding range of applications has raised concerns about its potential unfairness towards certain social groups.…