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Selective Inference in Machine Learning: theory, algorithms and applications

Grant number: 09/17773-7
Support type:Scholarships in Brazil - Doctorate
Effective date (Start): May 01, 2010
Effective date (End): May 31, 2014
Field of knowledge:Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
Principal researcher:Maria Carolina Monard
Grantee:Ígor Assis Braga
Home Institution: Instituto de Ciências Matemáticas e de Computação (ICMC). Universidade de São Paulo (USP). São Carlos , SP, Brazil

Abstract

Selective inference is a problem that can be spotted in several machine learning tasks. Although some solutions to this problem have already been proposed, these solutions consider the use of algorithms which have been developed to solve more general problems of inference. However, recent work in machine learning shows that it is important to solve a specific inference problem, such as selective inference, directly, without relying on solutions to more general problems. To this end, this work proposes researching and developing algorithms to specifically solve the selective inference problem.

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Scientific publications
(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)
BRAGA, IGOR; MONARD, MARIA CAROLINA. Improving the kernel regularized least squares method for small-sample regression. Neurocomputing, v. 163, n. SI, p. 106-114, SEP 2 2015. Web of Science Citations: 3.

Please report errors in scientific publications list by writing to: cdi@fapesp.br.