Now showing items 1-4 of 4

    • Generalized global bandit and its application in cellular coverage optimization 

      Shen, C.; Zhou, R.; Tekin, Cem; Schaar, M. V. D. (Institute of Electrical and Electronics Engineers, 2018)
      Motivated by the engineering problem of cellular coverage optimization, we propose a novel multiarmed bandit model called generalized global bandit. We develop a series of greedy algorithms that have the capability to ...
    • Global bandits 

      Atan, O.; Tekin, Cem; Schaar, M. V. D. (Institute of Electrical and Electronics Engineers, 2018)
      Multiarmed bandits (MABs) model sequential decision-making problems, in which a learner sequentially chooses arms with unknown reward distributions in order to maximize its cumulative reward. Most of the prior works on MAB ...
    • Online anomaly detection with bandwidth optimized hierarchical kernel density estimators 

      Kerpicci, M.; Ozkan, H.; Kozat, Süleyman Serdar (IEEE, 2020)
      We propose a novel unsupervised anomaly detection algorithm that can work for sequential data from any complex distribution in a truly online framework with mathematically proven strong performance guarantees. First, a ...
    • Online anomaly detection with kernel density estimators 

      Kerpiççi, Mine (Bilkent University, 2019-07)
      We study online anomaly detection in an unsupervised framework and introduce an algorithm to detect the anomalies in sequential data. We first sequentially learn the density for the observed data with a novel kernel based ...