Now showing items 1-5 of 5

    • Contextual multi-armed bandits with structured payoffs 

      Qureshi, Muhammad Anjum (Bilkent University, 2020-09)
      Multi-Armed Bandit (MAB) problems model sequential decision making under uncertainty. In traditional MAB, the learner selects an arm in each round, and then, observes a random reward from the arm’s unknown reward ...
    • Fully distributed bandit algorithm for the joint channel and rate selection problem in heterogeneous cognitive radio networks 

      Javanmardi, Alireza (Bilkent University, 2020-12)
      We consider the problem of the distributed sequential channel and rate selection in cognitive radio networks where multiple users choose channels from the same set of available wireless channels and pick modulation and ...
    • Multi-objective contextual bandits with a dominant objective 

      Tekin, Cem; Turgay, Eralp (IEEE, 2017)
      In this paper, we propose a new contextual bandit problem with two objectives, where one of the objectives dominates the other objective. Unlike single-objective bandit problems in which the learner obtains a random scalar ...
    • Online Contextual Influence Maximization in social networks 

      Sarıtaç, Ömer; Karakurt, Altuğ; Tekin, Cem (Institute of Electrical and Electronics Engineers Inc., 2017)
      In this paper, we propose the Online Contextual Influence Maximization Problem (OCIMP). In OCIMP, the learner faces a series of epochs in each of which a different influence campaign is run to promote a certain product in ...
    • Online contextual influence maximization with costly observations 

      Sarıtaç, Anıl Ömer; Karakurt, Altuğ; Tekin, Cem (IEEE, 2019-06)
      In the online contextual influence maximization problem with costly observations, the learner faces a series of epochs in each of which a different influence spread process takes place over a network. At the beginning of ...