626 citations · 1.7k across the 31 of their papers we have counts for
35 papers
Probabilistic Neural Transfer Function Estimation with Bayesian System Identification
Nan Wu, Isabel Valera, Fabian Sinz +3
Neural population responses in sensory systems are driven by external physical stimuli. This stimulus-response relationship is typically characterized by receptive fields, which ha…
Causal normalizing flows: from theory to practice
Adrián Javaloy, Pablo Sánchez-Martín, Isabel Valera
In this work, we deepen on the use of normalizing flows for causal reasoning. Specifically, we first leverage recent results on non-linear ICA to show that causal models are identi…
Variational Mixture of HyperGenerators for Learning Distributions Over Functions
Batuhan Koyuncu, Pablo Sanchez-Martin, Ignacio Peis +2
Recent approaches build on implicit neural representations (INRs) to propose generative models over function spaces. However, they are computationally costly when dealing with infe…
Learnable Graph Convolutional Attention Networks
Adrián Javaloy, Pablo Sanchez-Martin, Amit Levi +1
Existing Graph Neural Networks (GNNs) compute the message exchange between nodes by either aggregating uniformly (convolving) the features of all the neighboring nodes, or by apply…
Mitigating Modality Collapse in Multimodal VAEs via Impartial Optimization
Adrián Javaloy, Maryam Meghdadi, Isabel Valera
A number of variational autoencoders (VAEs) have recently emerged with the aim of modeling multimodal data, e.g., to jointly model images and their corresponding captions. Still, m…
Don't Throw it Away! The Utility of Unlabeled Data in Fair Decision Making
Miriam Rateike, Ayan Majumdar, Olga Mineeva +2
Decision making algorithms, in practice, are often trained on data that exhibits a variety of biases. Decision-makers often aim to take decisions based on some ground-truth target…
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…
RotoGrad: Gradient Homogenization in Multitask Learning
Adrián Javaloy, Isabel Valera
Multitask learning is being increasingly adopted in applications domains like computer vision and reinforcement learning. However, optimally exploiting its advantages remains a maj…
A Ranking Approach to Fair Classification
Jakob Schoeffer, Niklas Kuehl, Isabel Valera
Algorithmic decision systems are increasingly used in areas such as hiring, school admission, or loan approval. Typically, these systems rely on labeled data for training a classif…
On the Fairness of Causal Algorithmic Recourse
Julius von Kügelgen, Amir-Hossein Karimi, Umang Bhatt +3
Algorithmic fairness is typically studied from the perspective of predictions. Instead, here we investigate fairness from the perspective of recourse actions suggested to individua…
Scaling Guarantees for Nearest Counterfactual Explanations
Kiarash Mohammadi, Amir-Hossein Karimi, Gilles Barthe +1
Counterfactual explanations (CFE) are being widely used to explain algorithmic decisions, especially in consequential decision-making contexts (e.g., loan approval or pretrial bail…
A survey of algorithmic recourse: definitions, formulations, solutions, and prospects
Amir-Hossein Karimi, Gilles Barthe, Bernhard Schölkopf +1
Machine learning is increasingly used to inform decision-making in sensitive situations where decisions have consequential effects on individuals' lives. In these settings, in addi…
Algorithmic recourse under imperfect causal knowledge: a probabilistic approach
Amir-Hossein Karimi, Julius von Kügelgen, Bernhard Schölkopf +1
Recent work has discussed the limitations of counterfactual explanations to recommend actions for algorithmic recourse, and argued for the need of taking causal relationships betwe…
Lipschitz standardization for multivariate learning
Adrián Javaloy, Isabel Valera
Probabilistic learning is increasingly being tackled as an optimization problem, with gradient-based approaches as predominant methods. When modelling multivariate likelihoods, a u…
Algorithmic Recourse: from Counterfactual Explanations to Interventions
Amir-Hossein Karimi, Bernhard Schölkopf, Isabel Valera
As machine learning is increasingly used to inform consequential decision-making (e.g., pre-trial bail and loan approval), it becomes important to explain how the system arrived at…
Model-Agnostic Counterfactual Explanations for Consequential Decisions
Amir-Hossein Karimi, Gilles Barthe, Borja Balle +1
Predictive models are being increasingly used to support consequential decision making at the individual level in contexts such as pretrial bail and loan approval. As a result, the…
Fair Decisions Despite Imperfect Predictions
Niki Kilbertus, Manuel Gomez-Rodriguez, Bernhard Schölkopf +2
Consequential decisions are increasingly informed by sophisticated data-driven predictive models. However, to consistently learn accurate predictive models, one needs access to gro…
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…
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…
Handling Incomplete Heterogeneous Data using VAEs
Alfredo Nazabal, Pablo M. Olmos, Zoubin Ghahramani +1
Variational autoencoders (VAEs), as well as other generative models, have been shown to be efficient and accurate for capturing the latent structure of vast amounts of complex high…
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.…