Advanced search
Start date
Betweenand

Neural networks in statistical inference problems

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

Abstract

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)

Articles published in Agência FAPESP Newsletter about the research grant:
More itemsLess items
Articles published in other media outlets ( ):
More itemsLess items
VEICULO: TITULO (DATA)
VEICULO: TITULO (DATA)

Scientific publications (25)
(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)
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, . (19/11321-9)
ESTEVES, LUIS GUSTAVO; IZBICKI, RAFAEL; STERN, JULIO MICHAEL; STERN, RAFAEL BASSI. Pragmatic Hypotheses in the Evolution of Science. Entropy, v. 21, n. 9, . (14/50279-4, 19/11321-9, 13/07375-0, 17/03363-8, 14/25302-2)
M. INÁCIO; R. IZBICKI; D. LOPES; M. A. DINIZ; L. E. SALASAR; J. C. P. FERREIRA. What If the Forecaster Knew? Assessing Forecast Reliability via Simulation. Trends in Computational and Applied Mathematics, v. 23, n. 1, p. 175-192, . (19/11321-9)
DALMASSO, NICCOLO; LEE, ANN B.; IZBICKI, RAFAEL; POSPISIL, TAYLOR; KIM, ILMUN; LIN, CHIEH-AN; CHIAPPA, S; CALANDRA, R. Validation of Approximate Likelihood and Emulator Models for Computationally Intensive Simulations. INTERNATIONAL CONFERENCE ON MACHINE LEARNING, VOL 162, v. 108, p. 12-pg., . (19/11321-9, 17/03363-8)
DALMASSO, NICCOLO; IZBICKI, RAFAEL; LEE, ANN B.; DAUME, H; SINGH, A. Confidence Sets and Hypothesis Testing in a Likelihood-Free Inference Setting. INTERNATIONAL CONFERENCE ON MACHINE LEARNING, VOL 162, v. 119, p. 12-pg., . (19/11321-9)
DALMASSO, NICCOLO; IZBICKI, RAFAEL; LEE, ANN B.; ASSOC INFORMAT SYST. Confidence Sets and Hypothesis Testing in a Likelihood-Free Inference Setting. 25TH AMERICAS CONFERENCE ON INFORMATION SYSTEMS (AMCIS 2019), v. N/A, p. 12-pg., . (19/11321-9)
CABEZAS, LUBEN M. C.; IZBICKI, RAFAEL; STERN, RAFAEL B.. Hierarchical clustering: Visualization, feature importance and model selection. APPLIED SOFT COMPUTING, v. 141, p. 12-pg., . (20/10861-7, 13/07699-0, 19/11321-9)
MCNEELY, TREY; VINCENT, GALEN; WOOD, KIMBERLY M.; IZBICKI, RAFAEL; LEE, ANN B.. DETECTING DISTRIBUTIONAL DIFFERENCES IN LABELED SEQUENCE DATA WITH APPLICATION TO TROPICAL CYCLONE SATELLITE IMAGERY. Annals of Applied Statistics, v. 17, n. 2, p. 25-pg., . (19/11321-9)
VALLE, DENIS; IZBICKI, RAFAEL; LEITE, RODRIGO VIEIRA. Quantifying uncertainty in land-use land-cover classification using conformal statistics. REMOTE SENSING OF ENVIRONMENT, v. 295, p. 9-pg., . (19/11321-9)
CEREGATTI, RAFAEL DE CARVALHO; IZBICKI, RAFAEL; BUENO SALASAR, LUIS ERNESTO. WIKS: a general Bayesian nonparametric index for quantifying differences between two populations. TEST, v. 30, n. 1, p. 18-pg., . (19/11321-9, 17/03363-8)
COSCRATO, VICTOR; INACIO, MARCO H. A.; BOTARI, TIAGO; IZBICKI, RAFAEL. NLS: An accurate and yet easy-to-interpret prediction method. NEURAL NETWORKS, v. 162, p. 14-pg., . (19/11321-9, 17/06161-7, 17/03363-8)
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, . (19/11321-9, 17/03363-8)
FERONATO, SOFIA GALVAO; MATOS SILVA, MARIA LUIZA; IZBICKI, RAFAEL; FARIAS, TICIANA D. J.; SHIGUNOV, PATRICIA; DALLAGIOVANNA, BRUNO; PASSETTI, FABIO; DOS SANTOS, HELLEN GEREMIAS. Selecting Genetic Variants and Interactions Associated with Amyotrophic Lateral Sclerosis: A Group LASSO Approach. JOURNAL OF PERSONALIZED MEDICINE, v. 12, n. 8, p. 18-pg., . (19/11321-9)
CEREGATTI, RAFAEL DE CARVALHO; IZBICKI, RAFAEL; BUENO SALASAR, LUIS ERNESTO. WIKS: a general Bayesian nonparametric index for quantifying differences between two populations. TEST, . (19/11321-9, 17/03363-8)
IZBICKI, RAFAEL; SHIMIZU, GILSON Y.; STERN, RAFAEL B.; CHIAPPA, S; CALANDRA, R. Flexible distribution-free conditional predictive bands using density estimators. INTERNATIONAL CONFERENCE ON MACHINE LEARNING, VOL 162, v. 108, p. 9-pg., . (19/11321-9)
BOTARI, TIAGO; IZBICKI, RAFAEL; DE CARVALHO, ANDRE C. P. L. F.; CELLIER, P; DRIESSENS, K. Local Interpretation Methods to Machine Learning Using the Domain of the Feature Space. MACHINE LEARNING AND KNOWLEDGE DISCOVERY IN DATABASES, ECML PKDD 2019, PT I, v. 1167, p. 12-pg., . (13/07375-0, 19/11321-9)
VALLE, DENIS; SHIMIZU, GILSON; IZBICKI, RAFAEL; MARACAHIPES, LEANDRO; SILVERIO, DIVINO VICENTE; PAOLUCCI, LUCAS N.; JAMEEL, YUSUF; BRANDO, PAULO. The Latent Dirichlet Allocation model with covariates (LDAcov): A case study on the effect of fire on species composition in Amazonian forests. ECOLOGY AND EVOLUTION, v. 11, n. 12, p. 7970-7979, . (19/11321-9)
INACIO, MARCO; IZBICKI, RAFAEL; GYIRES-TOTH, BALINT. Distance assessment and analysis of high-dimensional samples using variational autoencoders. INFORMATION SCIENCES, v. 557, p. 407-420, . (19/11321-9, 17/03363-8)
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, . (19/11321-9, 17/03363-8)
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.; et al. 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, . (19/11321-9)
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, . (17/03363-8, 19/11321-9)
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, . (19/11321-9, 17/03363-8)
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, v. 11, n. 10, p. 1296-1308, . (19/11321-9)
SHIMIZU, GILSON Y.; IZBICKI, RAFAEL; VALLE, DENIS. A new LDA formulation with covariates. COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION, v. N/A, p. 18-pg., . (19/11321-9)
IZBICKI, RAFAEL; SHIMIZU, GILSON; STERN, RAFAEL B.. CD-split and HPD-split: Efficient Conformal Regions in High Dimensions. JOURNAL OF MACHINE LEARNING RESEARCH, v. 23, p. 32-pg., . (13/07699-0, 19/11321-9)