Rafael Izbicki | PhD
Rafael Izbicki | PhD
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machine learning
Confidence Sets and Hypothesis Testing in a Likelihood-Free Inference Setting
Parameter estimation, statistical tests and confidence sets are the cornerstones of classical statistics that allow scientists to make inferences about the underlying process that generated the observed data. A key question is whether one can still construct hypothesis tests and confidence sets with proper coverage and high power in a so-called likelihood-free inference (LFI) setting; that is, a setting where the likelihood is not explicitly known but one can forward-simulate observable data according to a stochastic model. We present ACORE, a frequentist approach to LFI that first formulates the classical likelihood ratio test (LRT) as a parametrized classification problem, and then uses the equivalence of tests and confidence sets to build confidence regions for parameters of interest. We also present a goodness-of-fit procedure for checking whether the constructed tests and confidence regions are valid.
Niccolò Dalmasso
,
Rafael Izbicki
,
Ann B. Lee
February, 2020
Proceedings of Machine Learning Research (ICML Track)
Preprint
PDF
The NN-Stacking: Feature weighted linear stacking through neural networks
Stacking methods improve the prediction performance of regression models. A simple way to stack base regressions estimators is by …
V. A. Coscrato
,
M. H. de A. Inácio
,
Rafael Izbicki
January, 2020
Neurocomputing
Preprint
PDF
Combinando métodos de aprendizado supervisionado para a melhoria da previsão do redshift de galáxia
M. Musetti
,
Rafael Izbicki
January, 2020
TEMA – Tendências em Matemática Aplicada e Computacional
PDF
Local Interpretation Methods to Machine Learning Using the Domain of the Feature Space
As machine learning becomes an important part of many real world applications affecting human lives, new requirements, besides high …
T. Botari
,
Rafael Izbicki
,
A. C. P. L. F. Carvalho
January, 2020
Communications in Computer and Information Science
Preprint
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
PDF
Cite
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
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