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The documents below are introductory materials for a variety of machine learning sub-fields. These materials are actually chapters of my undergraduate thesis written back in 2008. Even though these are fast-moving research areas, these materials can serve as a first read for the newcomers in the machine learning realm. All the documents are in Brazilian Portuguese.
During my PhD, I spent a lot of time searching, collecting, and preparing datasets for multitask learning. To help new researchers in the field, I’m sharing the datasets that I’ve been collecting for the last few years. All the Global Climate Models (GCMs) combination datasets were created by our research group at University of Minnesota - Twin Cities. I refer to one of our papers for a detailed explanation of the problem and the datasets.
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Published in CIKM'14, 2014
Authors: AR Goncalves, P Das, S Chatterjee, V Sivakumar, FJ Von Zuben, A Banerjee
In this paper, we present a novel family of models for MTL, applicable to regression and classification problems, capable of learning the structure of task relationships. In particular, we consider a joint estimation problem of the task relationship structure and the individual task parameters, which is solved using alternating minimization. The task relationship structure learning component builds on recent advances in structure learning of Gaussian graphical models based on sparse estimators of the precision (inverse covariance) matrix.
Published in IJCAI'15, 2015
Authors: AR Goncalves, FJ Von Zuben, A Banerjee
Built on recent advances in structure learning in Ising Markov Random Fields (I-MRF), we propose a multi-label classification algorithm that explicitly estimate and incorporate label dependence into the classifiers learning process by means of a sparse convex multitask learning formulation.
Published in JMLR, 2016
Authors: AR Goncalves, FJ Von Zuben
This paper presents a novel family of models for MTL, applicable to regression and classification problems, capable of learning the structure of tasks relationship via Copula distributions.
Published in AAAI'17, 2017
Authors: AR Goncalves, A Banerjee, FJ Von Zuben
This paper proposes a new Hierarchical Multitask Learning framework for the joint prediction of multiple climate variables. Each task in this multitask learning set up is actually another multitask learning problem.
Published in Large-Scale Machine Learning in the Earth Sciences (Book Chapter), 2017
Authors: S Chatterjee, V Sivakumar, AR Goncalves, A Banerjee
In this book chapter, we discuss structured sparse regression and multitask models for data analysis in climate science.
Published in 16th International Conference of the Biometrics Special Interest Group, 2017
Authors: AR Goncalves, P Korshunov, RPV Violato, FO Simoes, S Marcel
This paper describes presentation attack detection systems developed for the Automatic Speaker Verification Spoofing and Countermeasures Challenge (ASVspoof 2017). The submitted systems, using calibration and score fusion techniques, combine different sub-systems (up to 18), which are based on eight state of the art features and rely on Gaussian mixture models and feed-forward neural network classifiers.
Published in Computerized Medical Imaging and Graphics (Journal), 2018
Authors: X Liu, AR Goncalves, P Cao, D Zhao, A Banerjee
We consider the problem of simultaneously predicting several different cognitive scores associated with categorizing subjects as normal, mild cognitive impairment (MCI), or Alzheimer's disease (AD) in a multi-task learning framework using features extracted from brain images obtained from ADNI (Alzheimer's Disease Neuroimaging Initiative).