Invited Paper Session on Statistical Learning
Uncertainty Quantification in Machine Learning
Machine learning methods have an increasing ability to create models with good predictive power. However, even the best models make mistakes. To mitigate the effect of these errors on subsequent decision-making, it is essential to be able to quantify the uncertainty associated with each prediction. In this seminar, I will discuss recent methods of uncertainty quantification that I have recently developed.
Bio:
Rafael is an Assistant Professor at the Department of Statistics of the Federal University of São Carlos (UFSCar), Brazil. He obtained his PhD degree in the Department of Statistics & Data Science at Carnegie Mellon University (CMU), USA. Prior to that, he graduated and received Master’s degree at the University of São Paulo. He is a CNPq Research Fellow and is interested in theory, methodology, applications, and foundations of statistics and machine learning.
Active learning: A way to cope with large unlabelled data sets
In an age where people produce large amounts of data, often times labeling such data becomes extremely costly. Such annotation problem occurs mainly in fields of knowledge where it is required the performance of specialists who are difficult to access or even with little time dedicated to curating a data set. One of the strategies to get around this problem is to use active learning, which uses machine learning to learn with little annotated data and then be able to annotate large volumes of unlabelled data with the help of an oracle. In this talk, I will discuss the related issues and how the active learning field helps to solve such issues.
Bio:
Luciano Rebouças holds a Ph.D. in Electrical and Computer Engineering, from the Institute of Systems and Robotics University of Coimbra, a master's degree in Mechatronics, and a bachelor’s in computer science at the Federal University of Bahia (UFBA). He is an Associate Professor at the Dept. of Computer Science, at Institute of Computing, UFBA, and head of the Intelligent Vision Research Lab (http://ivisionlab.ufba.br). He is a specialist in the field of Computer Vision and Machine Learning while his applied research is focused mainly on robotics, smart cities, biometric systems, and biomedicine.
Convolutional Support Vector Model: prediction of coronavirus disease using chest x-rays
The disease caused by the coronavirus (COVID-19) has been plaguing the world for the last two years. In this paper, a complete and applied study of convolutional support machines will be presented to classify patients infected with COVID-19 using X-ray data and comparing them with traditional convolutional neural networks (CNN). Based on the fitted models, it was possible to observe that the proposed convolutional support vector machine with the polynomial kernel has a better predictive performance. In addition to the results obtained based on real images, the behavior of the models studied was observed through simulated images, where it was possible to observe the advantages of support vector machine (SVM) models.
Bio:
Bachelor degree (2009) and Master degree (2011) in Statistics, titles obtained at Federal University of São Carlos (UFSCar). PhD in Statistics (2016) through the Graduate Programs in Statistics (PPGEst-UFSCar) and Graduate Studies in Computer Science (PPG-CC-UFSCar). Lecturer in the Specialization in Data Science & Big Data (DSBD-UFPR), MBA in Financial Analytics (DAAGE-UTFPR) and in Specialization in Data Science and Big Data (ECD-UFBA). Since August 2021, Assistant Professor at Department of Statistics, Federal University of Paraná (DEST-UFPR), Curitiba-PR, Brazil. Assistant Professor at Department of Statistics, Federal University of Bahia (DEST-UFBA), Salvador-BA, Brazil (2017-2021). Lecturer at Faculty of Technology SENAI-SP, São Carlos-SP, Brazil (2009-2015). His research areas include statistical machine learning, statistical inference, computational methods and big data analytics.