Selective Inference in Machine Learning: theory, algorithms and applications

• Grant number: 09/17773-7
• Support Opportunities: Scholarships in Brazil - Doctorate
• Start date: May 01, 2010
• End date: May 31, 2014
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
• Principal Investigator: Maria Carolina Monard
• Grantee: Ígor Assis Braga
• Host 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.

Scientific publications

(The scientific publications listed on this page originate from the Web of Science or SciELO databases. Their authors have cited FAPESP grant or fellowship project numbers awarded to Principal Investigators or Fellowship Recipients, whether or not they are among the authors. This information is collected automatically and retrieved directly from those bibliometric databases.)

BRAGA, IGOR; YANG, Q; WOOLDRIDGE, M. Stochastic Density Ratio Estimation and Its Application to Feature Selection. PROCEEDINGS OF THE TWENTY-FOURTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE (IJCAI), v. N/A, p. 2-pg., 2015-01-01. (09/17773-7)

BRAGA, IGOR; MONARD, MARIA CAROLINA. Improving the kernel regularized least squares method for small-sample regression. Neurocomputing, v. 163, p. 9-pg., 2015-09-02. (09/17773-7)

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. (09/17773-7)

Academic Publications

(References retrieved automatically from State of São Paulo Research Institutions)

BRAGA, Ígor Assis. Stochastic density ratio estimation and its application to feature selection. 2014. Doctoral Thesis - Universidade de São Paulo (USP). Instituto de Ciências Matemáticas e de Computação (ICMC/SB) São Carlos.