44 citations · 93 across the 8 of their papers we have counts for
8 papers
Outlier detection in regression: conic quadratic formulations
Andrés Gómez, José Neto
In many applications, when building linear regression models, it is important to account for the presence of outliers, i.e., corrupted input data points. Such problems can be formu…
Efficient Cross-Validation for Sparse Linear Regression
Ryan Cory-Wright, Andrés Gómez
Given a high-dimensional covariate matrix and a response vector, ridge-regularized sparse linear regression selects a subset of features that explains the relationship between cova…
Blueprint of a Molecular Spin Quantum Processor
A. Chiesa, S. Roca, S. Chicco +6
The implementation of a universal quantum processor still poses fundamental issues related to error mitigation and correction, which demand to investigate also platforms and comput…
Using Differential Evolution to avoid local minima in Variational Quantum Algorithms
Daniel Faílde, José Daniel Viqueira, Mariamo Mussa Juane +1
Variational Quantum Algorithms (VQAs) are among the most promising NISQ-era algorithms for harnessing quantum computing in diverse fields. However, the underlying optimization proc…
Real Option Pricing using Quantum Computers
Alberto Manzano, Gonzalo Ferro, Álvaro Leitao +2
In this work we present an alternative methodology to the standard Quantum Accelerated Monte Carlo (QAMC) applied to derivatives pricing. Our pipeline benefits from the combination…
Convex Submodular Minimization with Indicator Variables
Shaoning Han, Andrés Gómez
We study a general class of convex submodular optimization problems with indicator variables. Many applications such as the problem of inferring Markov random fields (MRFs) with a…
Generating and Detecting True Ambiguity: A Forgotten Danger in DNN Supervision Testing
Michael Weiss, André García Gómez, Paolo Tonella
Deep Neural Networks (DNNs) are becoming a crucial component of modern software systems, but they are prone to fail under conditions that are different from the ones observed durin…
Learning Optimal Fair Classification Trees: Trade-offs Between Interpretability, Fairness, and Accuracy
Nathanael Jo, Sina Aghaei, Andrés Gómez +1
The increasing use of machine learning in high-stakes domains -- where people's livelihoods are impacted -- creates an urgent need for interpretable, fair, and highly accurate algo…