@techreport{TR-IC-PFG-22-43,
   number = {IC-PFG-22-43},
   author = {Giovana Kerche Bonás and Marcelo da Silva Reis and Marcos
                   Medeiros Raimundo},
   title   =   {{An   algorithm   for  multi-task  learning  based  on 
                   combinatorial optimization of U-curves}},
   month = {December},
   year = {2022},
   institution = {Institute of Computing, University of Campinas},
   note = {In English, 19 pages.
    \par\selectlanguage{English}\textbf{Abstract}
       Here  we  report  the development of an algorithm that explores
       properties of U-shaped curves (“U-curves”) in cost functions of
       multi-task   transfer   learning  (MTL)  models.  The  proposed 
       algorithm works even with the insertion of different tasks that
       may  or may not be related to the original task of learning. To
       find a global minimum of the described curve, a Boolean lattice
       is  organized based on the enumeration of the search space with
       the  weights  of the group of different tasks used in training.
       This  traversal  of  that  lattice  is  carried  out  through a
       branch-and-bound  procedure,  in which the pruning criterion is
       the  increase  of  the  cost  in  a  chain  of that lattice. To
       benchmark   the  proposed  algorithm  against  established  MTL 
       methods,  we  carried  out  computational experiments with both
       synthetic  and  real  datasets.  We  expect  that this proposed
       algorithm  will represent a relevant alternative for multi-task
       learning models.
  }
}