We study the behavior of block 1/ 2 regularization for multivariate regression, where a K-dimensional response vector is regressed upon a fixed set of p covariates. The problem of...
Guillaume Obozinski, Martin J. Wainwright, Michael...
Local ratio is a well-known paradigm for designing approximation algorithms for combinatorial optimization problems. At a very high level, a local-ratio algorithm first decomposes ...
In distributed data mining models, adopting a flat node distribution model can affect scalability. To address the problem of modularity, flexibility and scalability, we propose...
In the recent years, CSP's have come to be seen as the core problem in many applications. We propose here a hybrid algorithm (MC-FC) that combines two different search method...
A Markov Decision Process (MDP) is a general model for solving planning problems under uncertainty. It has been extended to multiobjective MDP to address multicriteria or multiagen...