Blar i AURA på forfatter "Money, Rohan Thekkemarickal"

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    • Online Edge Flow Imputation on Networks 

      Money, Rohan Thekkemarickal; Krishnan, Joshin Parakkulangarayil; Beferull-Lozano, Baltasar; Isufi, Elvin (Peer reviewed; Journal article, 2022)
      An online algorithm for missing data imputation for networks with signals defined on the edges is presented. Leveraging the prior knowledge intrinsic to real-world networks, we propose a bi-level optimization scheme that ...

    • Online Joint Nonlinear Topology Identification and Missing Data Imputation over Dynamic Graphs 

      Money, Rohan Thekkemarickal; Krishnan, Joshin Parakkulangarayil; Beferull-Lozano, Baltasar (Journal article; Peer reviewed, 2022)
      Extracting causal graph structures from multivariate time series, termed topology identification, is a fundamental problem in network science with several important applications. Topology identification is a challenging ...

    • Online Machine Learning for Inference from Multivariate Time-series 

      Money, Rohan Thekkemarickal (Doctoral Dissertations at the University of Agder; no: 429, Doctoral thesis, 2023)
      Inference and data analysis over networks have become significant areas of research due to the increasing prevalence of interconnected systems and the growing volume of data they produce. Many of these systems generate ...

    • Scalable and Privacy-Aware Online Learning of Nonlinear Structural Equation Models 

      Money, Rohan Thekkemarickal; Krishnan, Joshin Parakkulangarayil; Beferull-Lozano, Baltasar; Isufi, Elvin (Peer reviewed; Journal article, 2023)
      An online topology estimation algorithm for nonlinear structural equation models (SEM) is proposed in this paper, addressing the nonlinearity and the non-stationarity of real-world systems. The nonlinearity is modeled using ...

    • Sparse Online Learning with Kernels using Random Features for Estimating Nonlinear Dynamic Graphs 

      Money, Rohan Thekkemarickal; Krishnan, Joshin P.; Beferull-Lozano, Baltasar (Peer reviewed; Journal article, 2023)
      Online topology estimation of graph-connected time series is challenging in practice, particularly because the dependencies between the time series in many real-world scenarios are nonlinear. To address this challenge, we ...