• Scalable and interpretable kernel methods based on random Fourier features 

      Otto, Mateus Piovezan (Universidade Federal de São Carlos, UFSCar, Programa Interinstitucional de Pós-Graduação em Estatística - PIPGEs, Câmpus São Carlos, 29/03/2023)
      Kernel methods are a class of statistical machine learning models based on positive semidefinite kernels, which serve as a measure of similarity between data features. Examples of kernel methods include kernel ridge ...
    • Quantificação em problemas com mudança de domínio 

      Vaz, Afonso Fernandes (Universidade Federal de São Carlos, UFSCar, Programa Interinstitucional de Pós-Graduação em Estatística - PIPGEs, Câmpus São Carlos, 17/05/2018)
      Several machine learning applications use classifiers as a way of quantifying the prevalence of positive class labels in a target dataset, a task named quantification. For instance, a naive way of determining what proportion ...
    • Conditional independence testing, two sample comparison and density estimation using neural networks 

      Inácio, Marco Henrique de Almeida (Universidade Federal de São Carlos, UFSCar, Programa Interinstitucional de Pós-Graduação em Estatística - PIPGEs, Câmpus São Carlos, 03/08/2020)
      Given the vast amount of data available nowadays and the rapid increase of computational processing power, the field of machine learning and the so called algorithmic modeling have seen a recent surge in its popularity and ...
    • Redes neurais para grafos e suas aplicações aos sistemas complexos 

      Carvalho, Guilherme Michel Lima de (Universidade Federal de São Carlos, UFSCar, Programa Interinstitucional de Pós-Graduação em Estatística - PIPGEs, Câmpus São Carlos, 08/04/2022)
      Complex systems are composed of several components that interact with each other. A natural approach for these types of systems is to use mathematical graph abstraction. In different contexts in the real world, it is ...