| 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)
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