Rafael Izbicki | PhD
Rafael Izbicki | PhD
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machine learning
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 this situation is that of prior probability shift, that is, once the labels are known, the distribution of the features is the same in the training and target populations. In this paper, we derive a new lower bound for the risk of the quantification problem under the prior shift assumption. Using a weaker version of the prior shift assumption, which can be tested, we show that ratio estimators can be used to build confidence intervals for the quantification problem.
Afonso F. Vaz
,
Rafael Izbicki
,
Rafael B. Stern
May, 2019
Journal of Machine Learning Research
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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, …
M. H. de A. Inácio
,
Rafael Izbicki
May, 2018
KDMiLe - Symposium on Knowledge Discovery, Mining and Learning - Algorithms Track
PDF
Monitoramento online da dengue: usando o Google para predizer epidemias.
L. O. Cruz
,
Rafael Izbicki
January, 2018
Revista Brasileira de Biometria
Classificação morfológica de galáxias em conjuntos de dados desbalanceados.
P. Ianishi
,
Rafael Izbicki
January, 2017
TEMA – Tendências em Matemática Aplicada e Computacional
PDF
High-Dimensional density ratio estimation with extensions to approximate likelihood computation
The ratio between two probability density functions is an important component of various tasks, including selection bias correction, …
Rafael Izbicki
,
A. B. Lee
,
C. M. Schafer
December, 2014
Journal of Machine Learning Research (AISTATS Track)
PDF
New image statistics for detecting disturbed galaxy morphologies at high redshift
P. E. Freeman
,
Rafael Izbicki
,
A. B. Lee
,
J.A. Newman
,
C. J. Conselice
,
A.M. Koekemoer
,
J.M. Lotz
,
M. Mozena
December, 2013
Statistical Analysis and Data Mining
Preprint
PDF
Code
Learning with many experts: Model selection and sparsity
Experts classifying data are often imprecise. Recently, several models have been proposed to train classifiers using the noisy labels …
Rafael Izbicki
,
Rafael B. Stern
June, 2013
In
SAM
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DOI
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