Now showing items 1-7 of 7

    • Adaptive ensemble learning with confidence bounds 

      Tekin, C.; Yoon, J.; Schaar, M. V. D. (Institute of Electrical and Electronics Engineers Inc., 2017)
      Extracting actionable intelligence from distributed, heterogeneous, correlated, and high-dimensional data sources requires run-time processing and learning both locally and globally. In the last decade, a large number of ...
    • Jamming bandits-a novel learning method for optimal jamming 

      Amuru, S.; Tekin, C.; Van Der Schaar, M.; Buehrer, R.M. (Institute of Electrical and Electronics Engineers Inc., 2016)
      Can an intelligent jammer learn and adapt to unknown environments in an electronic warfare-type scenario? In this paper, we answer this question in the positive, by developing a cognitive jammer that adaptively and optimally ...
    • Logarithmic regret bound over diffusion based distributed estimation 

      Sayın, Muhammed O.; Vanlı, Nuri Denizcan; Kozat, Süleyman Serdar (IEEE, 2014)
      We provide a logarithmic upper-bound on the regret function of the diffusion implementation for the distributed estimation. For certain learning rates, the bound shows guaranteed performance convergence of the distributed ...
    • Multiagent systems: learning, strategic behavior, cooperation, and network formation 

      Tekin, Cem; Zhang, S.; Xu, J.; Schaar, M. van der (Elsevier, 2018)
      Many applications ranging from crowdsourcing to recommender systems involve informationally decentralized agents repeatedly interacting with each other in order to reach their goals. These networked agents base their ...
    • Online cross-layer learning in heterogeneous cognitive radio networks without CSI 

      Qureshi, Muhammad Anjum; Tekin, Cem (IEEE, 2018)
      We propose a contextual multi-armed bandit (CMAB) model for cross-layer learning in heterogeneous cognitive radio networks (CRNs). We consider the scenario where application adaptive modulation (AAM) is implemented in the ...
    • RELEAF: an algorithm for learning and exploiting relevance 

      Tekin, C.; Schaar, Mihaela van der (Cornell University, 2015-02)
      Recommender systems, medical diagnosis, network security, etc., require on-going learning and decision-making in real time. These -- and many others -- represent perfect examples of the opportunities and difficulties ...
    • Robust least squares methods under bounded data uncertainties 

      Vanli, N. D.; Donmez, M. A.; Kozat, S. S. (Academic Press, 2015)
      We study the problem of estimating an unknown deterministic signal that is observed through an unknown deterministic data matrix under additive noise. In particular, we present a minimax optimization framework to the least ...