machine learning

Covid-19 Einstein Analysis

In this post I analyse the covid-19 data from https://www.kaggle.com/einsteindata4u/covid19, which contains information about patients from Albert Einstein’s Hospital, in São Paulo (Brazil). My main assumptions in the following analysis are that:

Confidence Sets and Hypothesis Testing in a Likelihood-Free Inference Setting

Flexible distribution-free conditional predictive bands using density estimators

The NN-Stacking: Feature weighted linear stacking through neural networks

Distance assessment and analysis of high-dimensional samples using variational autoencoders

Combinando métodos de aprendizado supervisionado para a melhoria da previsão do redshift de galáxia

Local Interpretation Methods to Machine Learning Using the Domain of the Feature Space

Quantification under prior probability shift: the ratio estimator and its extensions

The quantification problem consists of determining the prevalence of a given label in a target population. However, one often has access to the labels in a sample from the training population but not in the target population. A common assumption in …

Conditional density estimation using Fourier series and neural networks

Most machine learning tools aim at creating good predictions for new samples. However, obtaining 100% is not feasible in most problems, and therefore modeling the uncertainty over such predictions becomes necessary in several applications. This can …

Monitoramento online da dengue: usando o Google para predizer epidemias.