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André R. Gonçalves

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IJCAI Conference 2019 Conference Paper

Group LASSO with Asymmetric Structure Estimation for Multi-Task Learning

Saullo H. G. Oliveira
André R. Gonçalves
Fernando J. Von Zuben

Group LASSO is a widely used regularization that imposes sparsity considering groups of covariates. When used in Multi-Task Learning (MTL) formulations, it makes an underlying assumption that if one group of covariates is not relevant for one or a few tasks, it is also not relevant for all tasks, thus implicitly assuming that all tasks are related. This implication can easily lead to negative transfer if this assumption does not hold for all tasks. Since for most practical applications we hardly know a priori how the tasks are related, several approaches have been conceived in the literature to (i) properly capture the transference structure, (ii) improve interpretability of the tasks interplay, and (iii) penalize potential negative transfer. Recently, the automatic estimation of asymmetric structures inside the learning process was capable of effectively avoiding negative transfer. Our proposal is the first attempt in the literature to conceive a Group LASSO with asymmetric transference formulation, looking for the best of both worlds in a framework that admits the overlap of groups. The resulting optimization problem is solved by an alternating procedure with fast methods. We performed experiments using synthetic and real datasets to compare our proposal with state-of-the-art approaches, evidencing the promising predictive performance and distinguished interpretability of our proposal. The real case study involves the prediction of cognitive scores for Alzheimer's disease progression assessment. The source codes are available at GitHub.

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JMLR Journal 2016 Journal Article

Multi-task Sparse Structure Learning with Gaussian Copula Models

André R. Gonçalves
Fernando J. Von Zuben
Arindam Banerjee

Multi-task learning (MTL) aims to improve generalization performance by learning multiple related tasks simultaneously. While sometimes the underlying task relationship structure is known, often the structure needs to be estimated from data at hand. In this paper, we present a novel family of models for MTL, applicable to regression and classification problems, capable of learning the structure of tasks relationship. In particular, we consider a joint estimation problem of the tasks relationship structure and the individual task parameters, which is solved using alternating minimization. The task relationship revealed by structure learning is founded on recent advances in Gaussian graphical models endowed with sparse estimators of the precision (inverse covariance) matrix. An extension to include flexible Gaussian copula models that relaxes the Gaussian marginal assumption is also proposed. We illustrate the effectiveness of the proposed model on a variety of synthetic and benchmark data sets for regression and classification. We also consider the problem of combining Earth System Model (ESM) outputs for better projections of future climate, with focus on projections of temperature by combining ESMs in South and North America, and show that the proposed model outperforms several existing methods for the problem. [abs] [ pdf ][ bib ] &copy JMLR 2016. ( edit, beta )

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