Text

High-Dimensional Multi-Task Learning using Multivariate Regression and Generalized Fiducial Inference

Over the past decades, the Multi-Task Learning (MTL) problem has attracted much attention in the artificial intelligence and machine learning communities. However, most published work in this area focuses on point estimation; that is, estimating model parameters and/or making predictions. This article studies another important aspect of the MTL problem: uncertainty quantification for model choices and predictions. To be more specific, this article approaches the MTL problem with multivariate regression and develops a novel method for deriving a probability density function on the space of all potential regression models. With this density function, point estimates, as well as confidence and prediction ellipsoids, can be obtained for quantities of interest, such as future observations. The proposed method, termed GMTask, is based on the generalized fiducial inference (GFI) framework and is shown to enjoy desirable theoretical properties. Its promising empirical properties are illustrated via a sequence of numerical experiments and applications to two real datasets. Supplementary materials for this article are available online.

Ketersediaan

Informasi Detail

Judul Seri

JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS; Vol.32 No.1 March 2023

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 226-240

Bahasa

English

ISBN/ISSN

-

Klasifikasi

NONE

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

UNCERTAINTY QUANTIFICATION
LARGE P SMALL N
CONFIDENCE ELLIPSOIDS
GMTASK
PREDICTION ELLIPSOIDS

Info Detail Spesifik

https://doi.org/10.1080/10618600.2022.2090946

Pernyataan Tanggungjawab

Zhenyu Wei, Thomas C. M. Lee

Versi lain/terkait

Lampiran Berkas

Komentar