• Adaptive Graph Filters in Reproducing Kernel Hilbert Spaces: Design and Performance Analysis 

      Elias, Vitor; Gogineni, Vinay Chakravarthi; Martins, Wallace; Werner, Stefan (Peer reviewed; Journal article, 2021)
      This paper develops adaptive graph filters that operate in reproducing kernel Hilbert spaces. We consider both centralized and fully distributed implementations. We first define nonlinear graph filters that operate on ...
    • ADMM for Sparse-Penalized Quantile Regression with Non-Convex Penalties 

      Werner, Stefan; Gogineni, Vinay Chakravarthi; Dasanadoddi Venkategowda, Naveen Kumar (Chapter, 2022)
      This paper studies quantile regression with non-convex and non-smooth sparse-penalties, such as minimax concave penalty (MCP) and smoothly clipped absolute deviation (SCAD). Although iterative coordinate descent and local ...
    • Asynchronous online federated learning with reduced communication requirements 

      Gauthier, Francois Jean Rene; Gogineni, Vinay Chakravarthi; Werner, Anders Stefan; Huang, Yih-Fang; Kuh, Anthony (Peer reviewed; Journal article, 2023)
      Online federated learning (FL) enables geographically distributed devices to learn a global shared model from locally available streaming data. Most online FL literature considers a best-case scenario regarding the ...
    • Autoencoder-based hyperspectral anomaly detection using kernel principal component pre-processing 

      Müller, Katinka; Gogineni, Vinay Chakravarthi; Orlandic, Milica; Werner, Anders Stefan (Chapter, 2023)
      Anomaly detection in hyperspectral remote sensing applications has attracted colossal attention due to its ability to uncover small distinctive objects dispersed across large geographical areas in an unsupervised manner. ...
    • Communication-efficient and privacy-aware distributed learning 

      Gogineni, Vinay Chakravarthi; Moradi, Ashkan; Dasanadoddi Venkategowda, Naveen Kumar; Werner, Anders Stefan (Peer reviewed; Journal article, 2023)
      Communication efficiency and privacy are two key concerns in modern distributed computing systems. Towards this goal, this article proposes partial sharing private distributed learning (PPDL) algorithms that offer communication ...
    • Communication-efficient and privacy-aware distributed LMS algorithm 

      Gogineni, Vinay Chakravarthi; Moradi, Ashkan; Kumar Dasanadoddi Venkategowda, Naveen; Werner, Stefan (Chapter, 2022)
      This paper presents a private-partial distributed least mean square (PP-DLMS) algorithm that offers energy efficiency while preserving privacy and is suitable for applications with limited resources and strict security ...
    • Communication-Efficient Online Federated Learning Framework for Nonlinear Regression 

      Gogineni, Vinay Chakravarthi; Werner, Stefan; Huang, Yih-Fang; Kuh, Anthony (Peer reviewed; Journal article, 2022)
      Federated learning (FL) literature typically assumes that each client has a fixed amount of data, which is unrealistic in many practical applications. Some recent works introduced a framework for online FL (Online-Fed) ...
    • Communication-Efficient Online Federated Learning Strategies for Kernel Regression 

      Gogineni, Vinay Chakravarthi; Werner, Stefan; Huang, Yih-Fang; Kuh, Anthony (Journal article; Peer reviewed, 2022)
    • Continual local updates for federated learning with enhanced robustness to link noise 

      Lari, Ehsan; Gogineni, Vinay Chakravarthi; Arablouei, Reza; Werner, Anders Stefan (Chapter, 2023)
      Communication errors caused by noisy links can negatively impact the accuracy of federated learning (FL) algorithms. To address this issue, we introduce an FL algorithm that is robust to communication errors while concurrently ...
    • Data-Driven Personalized Cervical Cancer Risk Prediction: A Graph-Perspective 

      Gogineni, Vinay Chakravarthi; Langberg, Geir Severin Rakh Elvatun; Naumova, Valeriya; Nygård, Jan; Nygård, Marie; Grasmair, Markus; Werner, Stefan (Chapter, 2021)
      Routine cervical cancer screening at regular periodic intervals leads to either over-screening or too infrequent screening of patients. For this purpose, personalized screening intervals are desirable that account for ...
    • Decentralized Graph Federated Multitask Learning for Streaming Data 

      Gogineni, Vinay Chakravarthi; Werner, Stefan (Chapter, 2022)
      In federated learning (FL), multiple clients connected to a single server train a global model based on locally stored data without revealing their data to the server or other clients. Nonetheless, the current FL architecture ...
    • Decentralized Graph Federated Multitask Learning for Streaming Data 

      Gogineni, Vinay Chakravarthi; Werner, Anders Stefan; Huang, Yih-Fang; Kuh, Anthony (Annual Conference on Information Sciences and Systems (CISS);56, Chapter, 2022)
      In federated learning (FL), multiple clients connected to a single server train a global model based on locally stored data without revealing their data to the server or other clients. Nonetheless, the current FL architecture ...
    • Distributed quantile regression with non-convex sparse penalties 

      Mirzaeifard, Reza; Gogineni, Vinay Chakravarthi; Kumar Dasanadoddi Venkategowda, Naveen; Werner, Anders Stefan (Peer reviewed; Journal article, 2023)
      The surge in data generated by IoT sensors has increased the need for scalable and efficient data analysis methods, particularly for robust algorithms like quantile regression, which can be tailored to meet a variety of ...
    • Dynamic Graph Topology Learning with Non-Convex Penalties 

      Gogineni, Vinay Chakravarthi; Dasanadoddi Venkategowda, Naveen Kumar; Werner, Stefan (Chapter, 2022)
      This paper presents a majorization-minimization-based framework for learning time-varying graphs from spatial-temporal measurements with non-convex penalties. The proposed approach infers time-varying graphs by using the ...
    • Enhanced Graph Autoencoders for Hyperspectral Anomaly Detection 

      Samsonsen, Brage Gaasø (Master thesis, 2023)
      Denne masteroppgaven presenterer flere hyperspektrale anomali detektorer som alle baserer seg på en toppmoderne detektor ved navnet Robust Graph Autoencoder (RGAE). Alle foreslåtte detektorer i denne masteroppgaven vil ha ...
    • Fractional-order correntropy adaptive filters for distributed processing of alpha-stable signals 

      Gogineni, Vinay Chakravarthi; Talebi, Sayed Pouria; Werner, Stefan; Mandic, Danilo (Peer reviewed; Journal article, 2020)
      This work revisits the problem of distributed adaptive filtering in multi-agent sensor networks. In contrast to classical approaches, the formulation relaxes the Gaussian assumption on the signal and noise to the generalized ...
    • Fractional-Order Correntropy Adaptive Filters for Distributed Processing of α -Stable Signals 

      Gogineni, Vinay Chakravarthi; Werner, Stefan (Journal article; Peer reviewed, 2020)
      This work revisits the problem of distributed adaptive filtering in multi-agent sensor networks. In contrast to classical approaches, the formulation relaxes the Gaussian assumption on the signal and noise to the generalized ...
    • Fractional-order correntropy filters for tracking dynamic systems in alpha-stable environments 

      Gogineni, Vinay Chakravarthi; Talebi, Sayed Pouria; Werner, Stefan; Mandic, Danilo (Peer reviewed; Journal article, 2020)
      In an increasing number of modern filtering applications, the encountered signals consist of frequent sharp spikes, that cannot be accurately modeled using Gaussian random processes. Modeling the behavior of such signals ...
    • Graph diffusion kernel LMS using random Fourier features 

      Gogineni, Vinay Chakravarthi; Elias, Vitor; Martins, Wallace; Werner, Stefan (Chapter, 2021)
      This work introduces kernel adaptive graph filters that operate in the reproducing kernel Hilbert space. We propose a centralized graph kernel least mean squares (GKLMS) approach for identifying the nonlinear graph filters. ...
    • Graph Kernel Recursive Least-Squares Algorithms 

      Gogineni, Vinay Chakravarthi; Naumova, Valeriya; Werner, Stefan; Huang, Yih-Fang (Chapter, 2022)
      This paper presents graph kernel adaptive filters that model nonlinear input-output relationships of streaming graph signals. To this end, we propose centralized and distributed graph kernel recursive least-squares (GKRLS) ...