activity
20152018
most citedSpatial Projection of Multiple Climate Variables using Hierarchical Multitask Learning

7 citations · 12 across the 4 of their papers we have counts for

collaborators

5 papers

cs.LG2018

RIn-Close_CVC2: an even more efficient enumerative algorithm for biclustering of numerical datasets

Rosana Veroneze, Fernando J. Von Zuben

RIn-Close_CVC is an efficient (take polynomial time per bicluster), complete (find all maximal biclusters), correct (all biclusters attend the user-defined level of consistency) an…

cs.DB20174 cited

Efficient mining of maximal biclusters in mixed-attribute datasets

Rosana Veroneze, Fernando J. Von Zuben

This paper presents a novel enumerative biclustering algorithm to directly mine all maximal biclusters in mixed-attribute datasets (containing both numerical and categorical attrib…

cs.LG20177 cited

Spatial Projection of Multiple Climate Variables using Hierarchical Multitask Learning

André R. Gonçalves, Arindam Banerjee, Fernando J. Von Zuben

Future projection of climate is typically obtained by combining outputs from multiple Earth System Models (ESMs) for several climate variables such as temperature and precipitation…

cs.NE2015

Hybrid Algorithm for Multi-Objective Optimization by Greedy Hypervolume Maximization

Conrado Silva Miranda, Fernando José Von Zuben

This paper introduces a high-performance hybrid algorithm, called Hybrid Hypervolume Maximization Algorithm (H2MA), for multi-objective optimization that alternates between explori…

stat.ML20151 cited

Asymmetric Distributions from Constrained Mixtures

Conrado S. Miranda, Fernando J. Von Zuben

This paper introduces constrained mixtures for continuous distributions, characterized by a mixture of distributions where each distribution has a shape similar to the base distrib…