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Neural networks in statistical inference problems

Grant number: 19/11321-9
Support type:Regular Research Grants
Duration: October 01, 2019 - September 30, 2021
Field of knowledge:Physical Sciences and Mathematics - Probability and Statistics - Statistics
Principal researcher:Rafael Izbicki
Grantee:Rafael Izbicki
Home Institution: Centro de Ciências Exatas e de Tecnologia (CCET). Universidade Federal de São Carlos (UFSCAR). São Carlos , SP, Brazil
Assoc. researchers:Rafael Bassi Stern


In the last decade, computational advancements have made neural networks reemerged as a powerful tool for performing supervised learning tasks such as classification and regression. Nonetheless, this tool has been subutilized as a way of performing statistical inference. For instance, solutions given by neural networks are typically black-box and therefore hard to interpret. In this work we will explore the power of neural networks for solving three challenges in statistical inference: (i) fitting interpretable nonparametric local linear regression estimators for large datasets (ii) measuring uncertainties in predictions made by supervised models via conditional density estimation for high-dimensional data, and (iii) testing conditional independence. (AU)

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Scientific publications (8)
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
SCHMIDT, S. J.; MALZ, I, A.; SOO, J. Y. H.; ALMOSALLAM, I. A.; BRESCIA, M.; CAVUOTI, S.; COHEN-TANUGI, J.; CONNOLLY, A. J.; DEROSE, J.; FREEMAN, P. E.; GRAHAM, M. L.; IYER, K. G.; JARVIS, M. J.; KALMBACH, J. B.; KOVACS, E.; LEE, A. B.; LONGO, G.; MORRISON, C. B.; NEWMAN, J. A.; NOURBAKHSH, E.; NUSS, E.; POSPISIL, T.; TRANIN, H.; WECHSLER, R. H.; ZHOU, R.; IZBICKI, R.; COLLABORATION, LSST DARK ENERGY SCI. Evaluation of probabilistic photometric redshift estimation approaches for The Rubin Observatory Legacy Survey of Space and Time (LSST). Monthly Notices of the Royal Astronomical Society, v. 499, n. 2, p. 1587-1606, DEC 2020. Web of Science Citations: 1.
BORGES, LEONARDO M.; REIS, VICTOR CANDIDO; IZBICKI, RAFAEL. Schrodinger's phenotypes: Herbarium specimens show two-dimensional images are both good and (not so) bad sources of morphological data. METHODS IN ECOLOGY AND EVOLUTION, AUG 2020. Web of Science Citations: 0.
COSCRATO, VICTOR; DE ALMEIDA INACIO, MARCO HENRIQUE; IZBICKI, RAFAEL. The NN-Stacking: Feature weighted linear stacking through neural networks. Neurocomputing, v. 399, p. 141-152, JUL 25 2020. Web of Science Citations: 0.
CEREGATTI, RAFAEL DE CARVALHO; IZBICKI, RAFAEL; BUENO SALASAR, LUIS ERNESTO. WIKS: a general Bayesian nonparametric index for quantifying differences between two populations. TEST, MAY 2020. Web of Science Citations: 0.
COSCRATO, VICTOR; IZBICKI, RAFAEL; STERN, RAFAEL BASSI. Agnostic tests can control the type I and type II errors simultaneously. BRAZILIAN JOURNAL OF PROBABILITY AND STATISTICS, v. 34, n. 2, p. 230-250, MAY 2020. Web of Science Citations: 0.
DALMASSO, N.; POSPISIL, T.; LEE, A. B.; IZBICKI, R.; FREEMAN, P. E.; MALZ, A. I. Conditional density estimation tools in python and R with applications to photometric redshifts and likelihood-free cosmological inference. ASTRONOMY AND COMPUTING, v. 30, JAN 2020. Web of Science Citations: 0.
M. MUSETTI; R. IZBICKI. Combinando Métodos de Aprendizado Supervisionado para a Melhoria da Previsão do Redshift de Galáxias. TEMA (São Carlos), v. 21, n. 1, p. 117-131, Abr. 2020.
ESTEVES, LUIS GUSTAVO; IZBICKI, RAFAEL; STERN, JULIO MICHAEL; STERN, RAFAEL BASSI. Pragmatic Hypotheses in the Evolution of Science. Entropy, v. 21, n. 9 SEP 2019. Web of Science Citations: 0.

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