Probabilidade, Processos Estocásticos, e suas Aplicados:
Modelagem, Controle e Simulação de Sistemas Dinâmicos Estocásticos;
Métodos Numéricos para Problemas de Filtragem e Controle Estocástico;
Machine Learning
Métodos Kernel;
Online Learning.
Projetos
Devido a problemas de caracterização de incertezas ou complexidade de diversos sistemas, os modelos estocásticos têm tido um papel fundamental no estudo de sistemas reais. Este projeto de pesquisa tem como objetivo utilizar métodos estocásticos avançados e robustos para solucionar problemas relevantes de modelagem, estimação e controle associados a sistemas sujeitos à incertezas. O projeto contempla diversas questões teóricas e aplicações no tema que denominaremos de sistemas dinâmicos multi-modelo estocásticos, que inclui a classe dos processos de Markov determinísticos por partes (PMDP) e os sistemas com saltos Markovianos (SSM).
Probabilidade, Processos Estocásticos e suas Aplicações
Numerical Method for Ergodic Optimal Control Problems of Switching
Stochastic Differential Equations with Reflection
SC Leite, MD Fragoso
59th IEEE Conference on Decision and Control, 2020
Switching Diffusion Approximations for Optimal Power Management in
Parallel Processing Systems
SC Leite, MD Fragoso, RS Teixeira
To Appear in Stochastic Models 2021
preprint
A constrained Langevin approximation for chemical reaction
networks
SC Leite, RJ Williams
The Annals of Applied Probability 29 (3), 1541-1608, 8 2019
preprint
On Constrained Langevin Equations and (Bio) Chemical Reaction
Networks
DF Anderson, DJ Higham, SC Leite, RJ Williams
Multiscale Modeling & Simulation 17 (1), 1-30 2019
preprint
On the control of power consumption in server farms via heavy traffic
approximation
SC Leite, MD Fragoso
53rd IEEE Conference on Decision and Control, 3683-3688 2014
link
Reducing Response Time in Fork-Join Systems under Heavy Traffic Via
Imbalance Control
SC Leite, MD Fragoso
Advances in Applied Probability 45 (4), 1137-1156 2013
link
Diffusion approximation for signaling stochastic networks
SC Leite, MD Fragoso
Stochastic Processes and their Applications 123 (8), 2957-2982
2013
link
Machine Learning
S.M. Villela, S.C. Leite, A.E. Xavier, R. Fonseca Neto
An ordered search with a large margin classifier for feature
selection
Applied Soft Computing, 2020, 106930,
link
A kernel based learning method for non-stationary two-player repeated
games
R. Motta, SC Leite, RF Neto
Knowledge-Based Systems Volume 196, 2020
link
Online Orthogonal Regression Based on a Regularized Squared
Loss
R Souza, SC Leite, W Meira, E Hruschka
17th IEEE International Conference on Machine Learning and Applications
2018
link
Incremental p-margin algorithm for classification with arbitrary
norm
SM Villela, SC Leite, RF Neto
Pattern Recognition 55, 261-272 2016
link
Feature selection from microarray data via an ordered search with
projected margin
SM Villela, SC Leite, RF Neto
Twenty-Fourth International Joint Conference on Artificial Intelligence
2015
link
Online algorithm based on support vectors for orthogonal
regression
RC Souza, SC Leite, CCH Borges, RF Neto
Pattern Recognition Letters 34 (12), 1394-1404 2013
link