Search Results for author: Isabel Valera

Found 33 papers, 17 papers with code

VACA: Design of Variational Graph Autoencoders for Interventional and Counterfactual Queries

1 code implementation27 Oct 2021 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 causal graph are available.

Causal Inference Fairness

Variational Causal Autoencoder for Interventional and Counterfactual Queries

no code implementations NeurIPS 2021 Pablo Sanchez Martin, Miriam Rateike, Isabel Valera

We propose the Variational Causal Autoencoder (VCAUSE), a novel class of variational graph autoencoders for causal inference in the absence of hidden confounders, when only observational data and the causal graph are available.

Causal Inference Fairness

RotoGrad: Gradient Homogenization in Multi-Task Learning

no code implementations NeurIPS 2021 Adrián Javaloy, Isabel Valera

Multi-task learning is being increasingly adopted in applications domains like computer vision and reinforcement learning.

Multi-Label Classification Multi-Task Learning

RotoGrad: Gradient Homogenization in Multitask Learning

1 code implementation3 Mar 2021 Adrián Javaloy, Isabel Valera

Multitask learning is being increasingly adopted in applications domains like computer vision and reinforcement learning.

Multi-Label Classification Multi-Task Learning

A Ranking Approach to Fair Classification

no code implementations8 Feb 2021 Jakob Schoeffer, Niklas Kuehl, Isabel Valera

In this paper, we focus on scenarios where only imperfect labels are available and propose a new fair ranking-based decision system based on monotonic relationships between legitimate features and the outcome.

Classification Decision Making +2

Rotograd: Dynamic Gradient Homogenization for Multitask Learning

no code implementations1 Jan 2021 Adrián Javaloy, Isabel Valera

GradNorm eases the fitting of all individual tasks by dynamically equalizing the contribution of each task to the overall gradient magnitude.

Scaling Guarantees for Nearest Counterfactual Explanations

no code implementations10 Oct 2020 Kiarash Mohammadi, Amir-Hossein Karimi, Gilles Barthe, Isabel Valera

Counterfactual explanations (CFE) are being widely used to explain algorithmic decisions, especially in consequential decision-making contexts (e. g., loan approval or pretrial bail).

Decision Making

A survey of algorithmic recourse: definitions, formulations, solutions, and prospects

no code implementations8 Oct 2020 Amir-Hossein Karimi, Gilles Barthe, Bernhard Schölkopf, Isabel Valera

Machine learning is increasingly used to inform decision-making in sensitive situations where decisions have consequential effects on individuals' lives.

Decision Making Fairness

Algorithmic recourse under imperfect causal knowledge: a probabilistic approach

1 code implementation NeurIPS 2020 Amir-Hossein Karimi, Julius von Kügelgen, Bernhard Schölkopf, Isabel Valera

Recent work has discussed the limitations of counterfactual explanations to recommend actions for algorithmic recourse, and argued for the need of taking causal relationships between features into consideration.

Lipschitz standardization for multivariate learning

2 code implementations26 Feb 2020 Adrián Javaloy, Isabel Valera

While MTL solutions do not directly apply in the probabilistic setting (as they cannot handle the likelihood constraints) we show that similar ideas may be leveraged during data preprocessing.

Imputation Multi-Task Learning

Algorithmic Recourse: from Counterfactual Explanations to Interventions

1 code implementation14 Feb 2020 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 its decision, and also suggest actions to achieve a favorable decision.

Decision Making

Model-Agnostic Counterfactual Explanations for Consequential Decisions

1 code implementation27 May 2019 Amir-Hossein Karimi, Gilles Barthe, Borja Balle, Isabel Valera

Predictive models are being increasingly used to support consequential decision making at the individual level in contexts such as pretrial bail and loan approval.

Decision Making

Fair Decisions Despite Imperfect Predictions

1 code implementation8 Feb 2019 Niki Kilbertus, Manuel Gomez-Rodriguez, Bernhard Schölkopf, Krikamol Muandet, Isabel Valera

In this paper, we show that in this selective labels setting, learning a predictor directly only from available labeled data is suboptimal in terms of both fairness and utility.

Causal Inference Decision Making +1

Infinite Factorial Finite State Machine for Blind Multiuser Channel Estimation

no code implementations18 Oct 2018 Francisco J. R. Ruiz, Isabel Valera, Lennart Svensson, Fernando Perez-Cruz

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.

Automatic Bayesian Density Analysis

no code implementations24 Jul 2018 Antonio Vergari, Alejandro Molina, Robert Peharz, Zoubin Ghahramani, Kristian Kersting, Isabel Valera

Classical approaches for {exploratory data analysis} are usually not flexible enough to deal with the uncertainty inherent to real-world data: they are often restricted to fixed latent interaction models and homogeneous likelihoods; they are sensitive to missing, corrupt and anomalous data; moreover, their expressiveness generally comes at the price of intractable inference.

Anomaly Detection Bayesian Inference +1

Handling Incomplete Heterogeneous Data using VAEs

2 code implementations10 Jul 2018 Alfredo Nazabal, Pablo M. Olmos, Zoubin Ghahramani, Isabel Valera

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-dimensional data.

Imputation

Boosting Black Box Variational Inference

1 code implementation NeurIPS 2018 Francesco Locatello, Gideon Dresdner, Rajiv Khanna, Isabel Valera, Gunnar Rätsch

Finally, we present a stopping criterion drawn from the duality gap in the classic FW analyses and exhaustive experiments to illustrate the usefulness of our theoretical and algorithmic contributions.

Variational Inference

Enhancing the Accuracy and Fairness of Human Decision Making

1 code implementation NeurIPS 2018 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 by police officers to accept-or-reject decisions taken by academics.

Decision Making Fairness

Automatic Discovery of the Statistical Types of Variables in a Dataset

1 code implementation ICML 2017 Isabel Valera, Zoubin Ghahramani

A common practice in statistics and machine learning is to assume that the statistical data types (e. g., ordinal, categorical or real-valued) of variables, and usually also the likelihood model, is known.

General Latent Feature Modeling for Data Exploration Tasks

no code implementations26 Jul 2017 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 describing each object can be either discrete, continuous or mixed variables.

From Parity to Preference-based Notions of Fairness in Classification

1 code implementation NeurIPS 2017 Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez Rodriguez, Krishna P. Gummadi, Adrian Weller

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.

Classification Decision Making +2

General Latent Feature Models for Heterogeneous Datasets

1 code implementation12 Jun 2017 Isabel Valera, Melanie F. Pradier, Maria Lomeli, Zoubin Ghahramani

Second, its Bayesian nonparametric nature allows us to automatically infer the model complexity from the data, i. e., the number of features necessary to capture the latent structure in the data.

Uncovering the Dynamics of Crowdlearning and the Value of Knowledge

no code implementations14 Dec 2016 Utkarsh Upadhyay, Isabel Valera, Manuel Gomez-Rodriguez

In this paper, we present a probabilistic modeling framework of crowdlearning, which uncovers the evolution of a user's expertise over time by leveraging other users' assessments of her contributions.

Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatment

3 code implementations26 Oct 2016 Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez Rodriguez, Krishna P. Gummadi

To account for and avoid such unfairness, in this paper, we introduce a new notion of unfairness, disparate mistreatment, which is defined in terms of misclassification rates.

Decision Making Fairness +1

Distilling Information Reliability and Source Trustworthiness from Digital Traces

no code implementations24 Oct 2016 Behzad Tabibian, Isabel Valera, Mehrdad Farajtabar, Le Song, Bernhard Schölkopf, Manuel Gomez-Rodriguez

Then, we propose a temporal point process modeling framework that links these temporal traces to robust, unbiased and interpretable notions of information reliability and source trustworthiness.

Modeling the Dynamics of Online Learning Activity

1 code implementation18 Oct 2016 Charalampos Mavroforakis, Isabel Valera, Manuel Gomez Rodriguez

People are increasingly relying on the Web and social media to find solutions to their problems in a wide range of domains.

Infinite Factorial Dynamical Model

1 code implementation NeurIPS 2015 Isabel Valera, Francisco Ruiz, Lennart Svensson, Fernando Perez-Cruz

We propose the infinite factorial dynamic model (iFDM), a general Bayesian nonparametric model for source separation.

Fairness Constraints: Mechanisms for Fair Classification

2 code implementations19 Jul 2015 Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez Rodriguez, Krishna P. Gummadi

Algorithmic decision making systems are ubiquitous across a wide variety of online as well as offline services.

Classification Decision Making +2

General Table Completion using a Bayesian Nonparametric Model

no code implementations NeurIPS 2014 Isabel Valera, Zoubin Ghahramani

Even though heterogeneous databases can be found in a broad variety of applications, there exists a lack of tools for estimating missing data in such databases.

Shaping Social Activity by Incentivizing Users

no code implementations NeurIPS 2014 Mehrdad Farajtabar, Nan Du, Manuel Gomez Rodriguez, Isabel Valera, Hongyuan Zha, Le Song

Events in an online social network can be categorized roughly into endogenous events, where users just respond to the actions of their neighbors within the network, or exogenous events, where users take actions due to drives external to the network.

Bayesian nonparametric comorbidity analysis of psychiatric disorders

no code implementations29 Jan 2014 Francisco J. R. Ruiz, Isabel Valera, Carlos Blanco, Fernando Perez-Cruz

To this end, we use the large amount of information collected in the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC) database and propose to model these data using a nonparametric latent model based on the Indian Buffet Process (IBP).

Variational Inference

Bayesian Nonparametric Modeling of Suicide Attempts

no code implementations NeurIPS 2012 Francisco Ruiz, Isabel Valera, Carlos Blanco, Fernando Pérez-Cruz

In the present paper, we are interested in seeking the hidden causes behind the suicide attempts, for which we propose to model the subjects using a nonparametric latent model based on the Indian Buffet Process (IBP).

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