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pro vyhledávání: '"Fonseca, Yuri"'
Popular debiased causal estimation methods, e.g. for the average treatment effect -- such as one-step estimation (e.g., augmented inverse propensity weighting) and targeted maximum likelihood estimation -- enjoy desirable asymptotic properties such a
Externí odkaz:
http://arxiv.org/abs/2405.09493
Instrumental variables (IVs) provide a powerful strategy for identifying causal effects in the presence of unobservable confounders. Within the nonparametric setting (NPIV), recent methods have been based on nonlinear generalizations of Two-Stage Lea
Externí odkaz:
http://arxiv.org/abs/2402.05639
Autor:
Fonseca, Yuri R., Saporito, Yuri F.
Inverse problems are paramount in Science and Engineering. In this paper, we consider the setup of Statistical Inverse Problem (SIP) and demonstrate how Stochastic Gradient Descent (SGD) algorithms can be used in the linear SIP setting. We provide co
Externí odkaz:
http://arxiv.org/abs/2209.14967
We study the problems of offline and online contextual optimization with feedback information, where instead of observing the loss, we observe, after-the-fact, the optimal action an oracle with full knowledge of the objective function would have take
Externí odkaz:
http://arxiv.org/abs/2106.14015
Autor:
FONSECA, Yuri Ikeda
Publikováno v:
Repositório Institucional da UFPAUniversidade Federal do ParáUFPA.
Submitted by Rosana Moreira (rosanapsm@outlook.com) on 2018-08-20T18:46:31Z No. of bitstreams: 2 license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Dissertacao_FormalismoDireitoEtica.pdf: 1371206 bytes, checksum: 7835acfcddee84795
Externí odkaz:
http://repositorio.ufpa.br/jspui/handle/2011/10153
In this paper, we introduce a new machine learning (ML) model for nonlinear regression called the Boosted Smooth Transition Regression Trees (BooST), which is a combination of boosting algorithms with smooth transition regression trees. The main adva
Externí odkaz:
http://arxiv.org/abs/1808.03698
Autor:
Resende Fonseca, Yuri
This thesis focuses on learning from revealed preferences and their implications across operations management problems through an Inverse Problem perspective. For the first part of the thesis, we focus on decentralized platforms facilitating many-to-
Publikováno v:
In Journal of the Mechanical Behavior of Biomedical Materials December 2017 76:104-109
Akademický článek
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Autor:
Fonseca, Yuri R.1 yuriresendefonseca@outlook.com, Masini, Ricardo P.2 ricardo.masini@fgv.br, Medeiros, Marcelo C.3 mcm@econ.puc-rio.br, Vasconcelos, Gabriel F. R.4 gabrielrvsc@yahoo.com.br
Publikováno v:
R Journal. Jul2018, Vol. 10 Issue 1, p91-108. 18p.