Now showing items 1-20 of 29

    • Accelerating the HyperLogLog cardinality estimation algorithm 

      Bozkus, C.; Fraguela, B. B. (Hindawi Limited, 2017)
      In recent years, vast amounts of data of different kinds, from pictures and videos from our cameras to software logs from sensor networks and Internet routers operating day and night, are being generated. This has led to ...
    • Adaptive ensemble learning with confidence bounds for personalized diagnosis 

      Tekin, C.; Yoon, J.; Schaar, M. V. D. (AI Access Foundation, 2016)
      With the advances in the field of medical informatics, automated clinical decision support systems are becoming the de facto standard in personalized diagnosis. In order to establish high accuracy and confidence in ...
    • Asymptotically optimal contextual bandit algorithm using hierarchical structures 

      Neyshabouri, M. M.; Gokcesu, K.; Gokcesu, H.; Ozkan, H.; Kozat, S. S. (Institute of Electrical and Electronics Engineers, 2018)
      We propose an online algorithm for sequential learning in the contextual multiarmed bandit setting. Our approach is to partition the context space and, then, optimally combine all of the possible mappings between the ...
    • Big Data Signal Processing Using Boosted RLS Algorithm 

      Civek, B. C.; Kari, D.; Delibalta, I.; Kozat, S. S. (Institute of Electrical and Electronics Engineers Inc., 2016)
      We propose an efficient method for the high dimensional data regression. To this end, we use a least mean squares (LMS) filter followed by a recursive least squares (RLS) filter and combine them via boosting notion extensively ...
    • Big-data streaming applications scheduling based on staged multi-armed bandits 

      Kanoun, K.; Tekin, C.; Atienza, D.; Van Der Schaar, M. (Institute of Electrical and Electronics Engineers, 2016)
      Several techniques have been recently proposed to adapt Big-Data streaming applications to existing many core platforms. Among these techniques, online reinforcement learning methods have been proposed that learn how to ...
    • C-Stream: A coroutine-based elastic stream processing engine 

      Şahin, Semih (Bilkent University, 2015)
      Stream processing is a computational paradigm for on-the-fly processing of live data. This paradigm lends itself to implementations that can provide high throughput and low latency, by taking advantage of various forms ...
    • Computationally highly efficient mixture of adaptive filters 

      Kilic, O. F.; Sayin, M. O.; Delibalta, I.; Kozat, S. S. (Springer London, 2017)
      We introduce a new combination approach for the mixture of adaptive filters based on the set-membership filtering (SMF) framework. We perform SMF to combine the outputs of several parallel running adaptive algorithms and ...
    • Efficient community identification and maintenance at multiple resolutions on distributed datastores 

      Aksu, H.; Canim, M.; Chang, Yuan-Chi; Korpeoglu, I.; Ulusoy, Ö. (Elsevier BV, 2015)
      The topic of network community identification at multiple resolutions is of great interest in practice to learn high cohesive subnetworks about different subjects in a network. For instance, one might examine the ...
    • Efficient implementation of Newton-raphson methods for sequential data prediction 

      Civek, B. C.; Kozat, S. S. (IEEE Computer Society, 2017)
      We investigate the problem of sequential linear data prediction for real life big data applications. The second order algorithms, i.e., Newton-Raphson Methods, asymptotically achieve the performance of the 'best' possible ...
    • Foreword: 1st International Workshop on High Performance Computing for Big Data 

      Kaya, K.; Gedik, B.; Çatalyürek, U.V. (Institute of Electrical and Electronics Engineers Inc., 2015)
      The 1st International Workshop on High Performance Computing for Big Data (HPC4BD) is held on September 10, 2014 in concordance with 43rd International Conference on Parallel Processing (ICPP-2014). The workshop aimed to ...
    • Graph aware caching policy for distributed graph stores 

      Aksu H.; Canim, M.; Chang, Y.-C.; Korpeoglu I.; Ulusoy Ö. (Institute of Electrical and Electronics Engineers Inc., 2015)
      Graph stores are becoming increasingly popular among NOSQL applications seeking flexibility and heterogeneity in managing linked data. Conceptually and in practice, applications ranging from social networks, knowledge ...
    • Improving performance of sparse matrix dense matrix multiplication on large-scale parallel systems 

      Acer, S.; Selvitopi, O.; Aykanat, C. (Elsevier BV, 2016)
      We propose a comprehensive and generic framework to minimize multiple and different volume-based communication cost metrics for sparse matrix dense matrix multiplication (SpMM). SpMM is an important kernel that finds ...
    • L1 norm based multiplication-free cosine similarity measures for big data analysis 

      Akbas, C.E.; Bozkurt, A.; Arslan, M.T.; Aslanoglu H.; Cetin, A.E. (Institute of Electrical and Electronics Engineers Inc., 2014)
      The cosine similarity measure is widely used in big data analysis to compare vectors. In this article a new set of vector similarity measures are proposed. New vector similarity measures are based on a multiplication-free ...
    • Mixture of Set Membership Filters Approach for Big Data Signal Processing 

      Kilic, O. F.; Sayin, M. O.; Delibalta, I.; Kozat, S. S. (Institute of Electrical and Electronics Engineers Inc., 2016)
      In this work, we propose a new approach for mixture of adaptive filters based on set-membership filters (SMF) which is specifically designated for big data signal processing applications. By using this approach, we achieve ...
    • Online churn detection on high dimensional cellular data using adaptive hierarchical trees 

      Khan, F.; Delibalta, I.; Kozat, S. S. (European Signal Processing Conference, EUSIPCO, 2016)
      We study online sequential logistic regression for churn detection in cellular networks when the feature vectors lie in a high dimensional space on a time varying manifold. We escape the curse of dimensionality by tracking ...
    • Online density estimation of nonstationary sources using exponential family of distributions 

      Gokcesu, K.; Kozat, S. S. (Institute of Electrical and Electronics Engineers Inc., 2017)
      We investigate online probability density estimation (or learning) of nonstationary (and memoryless) sources using exponential family of distributions. To this end, we introduce a truly sequential algorithm that achieves ...
    • Online learning over distributed networks 

      Sayın, Muhammed Ömer (Bilkent University, 2015)
      We study online learning strategies over distributed networks. Here, we have a distributed collection of agents with learning and cooperation capabilities. These agents observe a noisy version of a desired state of the ...
    • Online learning under adverse settings 

      Özkan, Hüseyin (Bilkent University, 2015-05)
      We present novel solutions for contemporary real life applications that generate data at unforeseen rates in unpredictable forms including non-stationarity, corruptions, missing/mixed attributes and high dimensionality. ...
    • An online minimax optimal algorithm for adversarial multiarmed bandit problem 

      Gokcesu, K.; Kozat, S. S. (Institute of Electrical and Electronics Engineers, 2018)
      We investigate the adversarial multiarmed bandit problem and introduce an online algorithm that asymptotically achieves the performance of the best switching bandit arm selection strategy. Our algorithms are truly online ...
    • Online nonlinear modeling for big data applications 

      Khan, Farhan (Bilkent University, 2017-12)
      We investigate online nonlinear learning for several real life, adaptive signal processing and machine learning applications involving big data, and introduce algorithms that are both e cient and e ective. We present ...