activity
20172022
most citedFeature Selection based on the Local Lift Dependence Scale

4 citations · 6 across the 3 of their papers we have counts for

collaborators

5 papers

stat.ML20211 cited

Learning the hypotheses space from data through a U-curve algorithm

Diego Marcondes, Adilson Simonis, Junior Barrera

This paper proposes a data-driven systematic, consistent and non-exhaustive approach to Model Selection, that is an extension of the classical agnostic PAC learning model. In this…

stat.ME2020

Parameter estimation in dynamical systems via Statistical Learning: a reinterpretation of Approximate Bayesian Computation applied to COVID-19 spread

Diego Marcondes

We propose a robust parameter estimation method for dynamical systems based on Statistical Learning techniques which aims to estimate a set of parameters that well fit the dynamics…

math.PR2019

Local Lift Dependence Scale

Diego Marcondes, Adilson Simonis

We propose a local and general dependence quantifier between two random variables and , which we call Local Lift Dependence Scale, that does not assume any form of dependenc…

stat.AP2018

Assessing randomness in case assignment: the case study of the Brazilian Supreme Court

Diego Marcondes, Cláudia Peixoto, Julio Michael Stern

Sortition, i.e., random appointment for public duty, has been employed by societies throughout the years, especially for duties related to the judicial system, as a firewall design…

stat.CO20174 cited

Feature Selection based on the Local Lift Dependence Scale

Diego Marcondes, Adilson Simonis, Junior Barrera

This paper uses a classical approach to feature selection: minimization of a cost function applied on estimated joint distributions. However, the search space in which such minimiz…