Browsing NTNU Open by Author "Gratton, Cristiano"

Now showing items 1-7 of 7

    • Consensus-based Distributed Total Least-squares Estimation Using Parametric Semidefinite Programming 

      Gratton, Cristiano; Dasanadoddi Venkategowda, Naveen Kumar; Arablouei, Reza; Werner, Stefan (Chapter, 2019)
      We propose a new distributed algorithm to solve the total least-squares (TLS) problem when data are distributed over a multi-agent network. To develop the proposed algorithm, named distributed ADMM TLS (DA-TLS), we reformulate ...

    • Distributed Learning over Networks with Non-Smooth Regularizers and Feature Partitioning 

      Gratton, Cristiano; Kumar Dasanadoddi Venkategowda, Naveen; Arablouei, Reza; Werner, Stefan (Chapter, 2021)
      We develop a new algorithm for distributed learning with non-smooth regularizers and feature partitioning. To this end, we transform the underlying optimization problem into a suitable dual form and solve it using the ...

    • Distributed Learning with Non-Smooth Objective Functions 

      Gratton, Cristiano; Dasanadoddi Venkategowda, Naveen Kumar; Arablouei, Reza; Werner, Stefan (Chapter, 2020)
      We develop a new distributed algorithm to solve a learning problem with non-smooth objective functions when data are distributed over a multi-agent network. We employ a zeroth-order method to minimize the associated augmented ...

    • Distributed Ridge Regression with Feature Partitioning 

      Gratton, Cristiano; Dasanadoddi Venkategowda, Naveen Kumar; Arablouei, Reza; Werner, Stefan (Chapter, 2019)
      We develop a new distributed algorithm to solve the ridge regression problem with feature partitioning of the observation matrix. The proposed algorithm, named D-Ridge, is based on the alternating direction method of ...

    • Privacy-Preserved Distributed Learning With Zeroth-Order Optimization 

      Gratton, Cristiano; Kumar Dasanadoddi Venkategowda, Naveen; Arablouei, Reza; Werner, Stefan (Peer reviewed; Journal article, 2022)
      We develop a privacy-preserving distributed algorithm to minimize a regularized empirical risk function when the first-order information is not available and data is distributed over a multi-agent network. We employ a ...

    • Privacy-Preserving Distributed Learning with Nonsmooth Objective Functions 

      Gauthier, Francois; Gratton, Cristiano; Dasanadoddi Venkategowda, Naveen Kumar; Werner, Stefan (Chapter, 2021)
      This paper develops a fully distributed differentially-private learning algorithm based on the alternating direction method of multipliers (ADMM) to solve nonsmooth optimization problems. We employ an approximation of the ...

    • Privacy-preserving distributed machine learning for artificial intelligence of things 

      Gratton, Cristiano (Doctoral theses at NTNU;2023:12, Doctoral thesis, 2023)
      This thesis proposes machine learning algorithms that can be fully distributed over ad-hoc networks of machines/agents. Developing distributed algorithms for artificial intelligence is necessary since running machine-lea ...