---
res:
  bibo_abstract:
  - "We consider an extension of the contextual multi-armed bandit problem, in\r\nwhich,
    instead of selecting a single alternative (arm), a learner is supposed\r\nto make
    a preselection in the form of a subset of alternatives. More\r\nspecifically,
    in each iteration, the learner is presented a set of arms and a\r\ncontext, both
    described in terms of feature vectors. The task of the learner is\r\nto preselect
    $k$ of these arms, among which a final choice is made in a second\r\nstep. In
    our setup, we assume that each arm has a latent (context-dependent)\r\nutility,
    and that feedback on a preselection is produced according to a\r\nPlackett-Luce
    model. We propose the CPPL algorithm, which is inspired by the\r\nwell-known UCB
    algorithm, and evaluate this algorithm on synthetic and real\r\ndata. In particular,
    we consider an online algorithm selection scenario, which\r\nserved as a main
    motivation of our problem setting. Here, an instance (which\r\ndefines the context)
    from a certain problem class (such as SAT) can be solved\r\nby different algorithms
    (the arms), but only $k$ of these algorithms can\r\nactually be run.@eng"
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Adil
      foaf_name: El Mesaoudi-Paul, Adil
      foaf_surname: El Mesaoudi-Paul
  - foaf_Person:
      foaf_givenName: Viktor
      foaf_name: Bengs, Viktor
      foaf_surname: Bengs
      foaf_workInfoHomepage: http://www.librecat.org/personId=76599
  - foaf_Person:
      foaf_givenName: Eyke
      foaf_name: Hüllermeier, Eyke
      foaf_surname: Hüllermeier
      foaf_workInfoHomepage: http://www.librecat.org/personId=48129
  dct_date: 2020^xs_gYear
  dct_language: eng
  dct_title: Online Preselection with Context Information under the Plackett-Luce  Model@
...