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Sergios Theodoridis

Also published under: S. Theodoridis

Publications
188
Citations
3,409
Publications by Year
19872025
Co-Authors:
Show All Co-Authors (160)
Image of author Sergios Theodoridis

Sergios Theodoridis

Also published under: S. Theodoridis

Affiliation

Department of Informatics and Telecommunications
National and Kapodistrian University of Athens
Athina, Greece

Publication Topics

Biography

Sergios Theodoridis is a Professor Emeritus of Signal Processing and Machine Learning with the Department of Informatics and Telecommunications, National and Kapodistrian University of Athens, Greece and with the Shenzhen Research Institute of Big Data (SRIBD), the Chinese University of Hong Kong, Shenzhen, China.,He is the author of the book “Machine Learning: A Bayesian and Optimization Perspective” Academic Press, 2nd Ed., 2020, the co-author of the best-selling book “Pattern Recognition”, Academic Press, 4th ed. 2009, the co-author of the book “Introduction to Pattern Recognition: A MATLAB Approach”, Academic Press, 2010, the Co-Editor of the book “Efficient Algorithms for Signal Processing and System Identification”, Prentice Hall 1993...
Publications
188
Citations
3,409
Publications by Year
19872025
Co-Authors:
Show All Co-Authors (160)
Author's Published Works

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Sparsity-Aware Distributed Learning for Gaussian Processes With Linear Multiple Kernel

Richard Cornelius Suwandi;Zhidi Lin;Feng Yin;Zhiguo Wang;Sergios Theodoridis

IEEE Transactions on Neural Networks and Learning Systems
Year: 2025 | Early Access Article |
Gaussian processes (GPs) stand as crucial tools in machine learning and signal processing, with their effectiveness hinging on kernel design and hyperparameter optimization. This article presents a novel GP linear multiple kernel (LMK) and a generic sparsity-aware distributed learning framework to optimize the hyperparameters. The newly proposed grid spectral mixture product (GSMP) kernel is tailo...Show More

Sparsity-Aware Distributed Learning for Gaussian Processes With Linear Multiple Kernel

Richard Cornelius Suwandi;Zhidi Lin;Feng Yin;Zhiguo Wang;Sergios Theodoridis

Year: 2025 | Early Access Article |

A Matrix-Factorization-Error-Ratio Approach to Cooperative Sensing in Non-Ideal Communication Environment

Rui Zhou;Wenqiang Pu;Licheng Zhao;Ming-Yi You;Qingjiang Shi;Sergios Theodoridis

IEEE Transactions on Signal Processing
Year: 2024 | Volume: 72 | Journal Article |
A fundamental challenge in cognitive radio is the detection of primary users in a licensed spectrum. Cooperative sensing, which utilizes multiple receivers distributed across different locations, offers the advantage of utilizing multiple antennas and achieving spatial diversity gain. However, successful implementation of cooperative sensing relies on the ideal exchange of information among cooper...Show More

A Matrix-Factorization-Error-Ratio Approach to Cooperative Sensing in Non-Ideal Communication Environment

Rui Zhou;Wenqiang Pu;Licheng Zhao;Ming-Yi You;Qingjiang Shi;Sergios Theodoridis

Year: 2024 | Volume: 72 | Journal Article |

Cooperative Sensing Via Matrix Factorization of the Partially Received Sample Covariance Matrix

Rui Zhou;Wenqiang Pu;Licheng Zhao;Ming-Yi You;Qingjiang Shi;Sergios Theodoridis

ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2024 | Conference Paper |
A fundamental problem in cognitive radio is spectrum sensing, which detects the presence of the primary users in a licensed spectrum. To boost the detection performance and robustness, the multiantenna detector has been investigated and various related methods have been developed, e.g., the energy detector, the eigenvalue arithmetic-to-geometric mean detector, and the generalized likelihood ratio ...Show More

Cooperative Sensing Via Matrix Factorization of the Partially Received Sample Covariance Matrix

Rui Zhou;Wenqiang Pu;Licheng Zhao;Ming-Yi You;Qingjiang Shi;Sergios Theodoridis

Year: 2024 | Conference Paper |

Towards Efficient Modeling and Inference in Multi-Dimensional Gaussian Process State-Space Models

Zhidi Lin;Juan Maroñas;Ying Li;Feng Yin;Sergios Theodoridis

ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2024 | Conference Paper |
Cited by: Papers (2)
The Gaussian process state-space model (GPSSM) has attracted extensive attention for modeling complex nonlinear dynamical systems. However, the existing GPSSM employs separate Gaussian processes (GPs) for each latent state dimension, leading to escalating computational complexity and parameter proliferation, thus posing challenges for modeling dynamical systems with high-dimensional latent states....Show More

Towards Efficient Modeling and Inference in Multi-Dimensional Gaussian Process State-Space Models

Zhidi Lin;Juan Maroñas;Ying Li;Feng Yin;Sergios Theodoridis

Year: 2024 | Conference Paper |

Interpretable Nonnegative Incoherent Deep Dictionary Learning for FMRI Data Analysis

Manuel Morante;Jan Østergaard;Sergios Theodoridis

ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2023 | Conference Paper |
Extracting information from fMRI data constitutes a broad active area of research. Current techniques still present several limitations; some ignore relevant aspects regarding the brain functioning or lack of interpretability. In an effort to overcome such limitations, we introduce an extension of the sparse matrix factorization approach to a multilinear decomposition. The proposed model is built ...Show More

Interpretable Nonnegative Incoherent Deep Dictionary Learning for FMRI Data Analysis

Manuel Morante;Jan Østergaard;Sergios Theodoridis

Year: 2023 | Conference Paper |

Overcoming Posterior Collapse in Variational Autoencoders Via EM-Type Training

Ying Li;Lei Cheng;Feng Yin;Michael Minyi Zhang;Sergios Theodoridis

ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2023 | Conference Paper |
Variational autoencoders (VAE) are one of the most prominent deep generative models for learning the underlying statistical distribution of high-dimensional data. However, training VAEs suffers from a severe issue called posterior collapse; that is, the learned posterior distribution collapses to the assumed/pre-selected prior distribution. This issue limits the capacity of the learned posterior d...Show More

Overcoming Posterior Collapse in Variational Autoencoders Via EM-Type Training

Ying Li;Lei Cheng;Feng Yin;Michael Minyi Zhang;Sergios Theodoridis

Year: 2023 | Conference Paper |

Rethinking Bayesian Learning for Data Analysis: The art of prior and inference in sparsity-aware modeling

Lei Cheng;Feng Yin;Sergios Theodoridis;Sotirios Chatzis;Tsung-Hui Chang

IEEE Signal Processing Magazine
Year: 2022 | Volume: 39, Issue: 6 | Magazine Article |
Cited by: Papers (79)
Sparse modeling for signal processing and machine learning, in general, has been at the focus of scientific research for over two decades. Among others, supervised sparsity-aware learning (SAL) consists of two major paths paved by 1) discriminative methods that establish direct input–output mapping based on a regularized cost function optimization and 2) generative methods that learn the underlyin...Show More

Rethinking Bayesian Learning for Data Analysis: The art of prior and inference in sparsity-aware modeling

Lei Cheng;Feng Yin;Sergios Theodoridis;Sotirios Chatzis;Tsung-Hui Chang

Year: 2022 | Volume: 39, Issue: 6 | Magazine Article |

Bayesian Training in Photonic Neural Meshes

Charis Mesaritakis;George Sarantoglou;Sergios Theodoridis;Adonis Bogris

2022 IEEE Workshop on Complexity in Engineering (COMPENG)
Year: 2022 | Conference Paper |
Neural networks based on reconfigurable photonic integrated chips (RPICs) can offer zero-latency processing, marginal power consumption and operational flexibility. On the other hand, they are subject to, performance affecting, operational/fabrication deviations in their building blocks. Here, we present a Bayesian learning framework that when combined with device characterization, can dramaticall...Show More

Bayesian Training in Photonic Neural Meshes

Charis Mesaritakis;George Sarantoglou;Sergios Theodoridis;Adonis Bogris

Year: 2022 | Conference Paper |

Bayesian Photonic Accelerators for Energy Efficient and Noise Robust Neural Processing

George Sarantoglou;Adonis Bogris;Charis Mesaritakis;Sergios Theodoridis

IEEE Journal of Selected Topics in Quantum Electronics
Year: 2022 | Volume: 28, Issue: 6: High Density Integr. Multipurpose Photon. Circ. | Journal Article |
Cited by: Papers (6)
Artificial neural networks are efficient computing platforms inspired by the brain. Such platforms can tackle a vast area of real-life tasks ranging from image processing to language translation. Silicon photonic integrated chips (PICs), by employing coherent interactions in Mach-Zehnder interferometers, are promising accelerators offering record low power consumption and ultra-fast matrix multipl...Show More

Bayesian Photonic Accelerators for Energy Efficient and Noise Robust Neural Processing

George Sarantoglou;Adonis Bogris;Charis Mesaritakis;Sergios Theodoridis

Year: 2022 | Volume: 28, Issue: 6: High Density Integr. Multipurpose Photon. Circ. | Journal Article |

A Stimuli-Relevant Directed Dependency Index for Time Series

Payam Shahsavari Baboukani;Sergios Theodoridis;Jan Østergaard

ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2022 | Conference Paper |
Cited by: Papers (1)
Transfer entropy can to a certain degree assess the direction in addition to the strength of the couplings within dynamic time series. The greater the transfer entropy, the greater the strength of the dependency between time series. In this work, we are interested in quantifying the effect that a given time series (e.g., an external stimuli) has upon the coupling strength between other time series...Show More

A Stimuli-Relevant Directed Dependency Index for Time Series

Payam Shahsavari Baboukani;Sergios Theodoridis;Jan Østergaard

Year: 2022 | Conference Paper |

Towards Flexible Sparsity-Aware Modeling: Automatic Tensor Rank Learning Using the Generalized Hyperbolic Prior

Lei Cheng;Zhongtao Chen;Qingjiang Shi;Yik-Chung Wu;Sergios Theodoridis

IEEE Transactions on Signal Processing
Year: 2022 | Volume: 70 | Journal Article |
Cited by: Papers (27)
Tensor rank learning for canonical polyadic decomposition (CPD) has long been deemed as an essential yet challenging problem. In particular, since thetensor rank controls the complexity of the CPD model, its inaccurate learning would cause overfitting to noise or underfitting to the signal sources, and even destroy the interpretability of model parameters. However, the optimal determination of a t...Show More

Towards Flexible Sparsity-Aware Modeling: Automatic Tensor Rank Learning Using the Generalized Hyperbolic Prior

Lei Cheng;Zhongtao Chen;Qingjiang Shi;Yik-Chung Wu;Sergios Theodoridis

Year: 2022 | Volume: 70 | Journal Article |

FedLoc: Federated Learning Framework for Data-Driven Cooperative Localization and Location Data Processing

Feng Yin;Zhidi Lin;Qinglei Kong;Yue Xu;Deshi Li;Sergios Theodoridis;Shuguang Robert Cui

IEEE Open Journal of Signal Processing
Year: 2020 | Volume: 1 | Journal Article |
Cited by: Papers (168)
In this overview paper, data-driven learning model-based cooperative localization and location data processing are considered, in line with the emerging machine learning and big data methods. We first review (1) state-of-the-art algorithms in the context of federated learning, (2) two widely used learning models, namely the deep neural network model and the Gaussian process model, and (3) various ...Show More

FedLoc: Federated Learning Framework for Data-Driven Cooperative Localization and Location Data Processing

Feng Yin;Zhidi Lin;Qinglei Kong;Yue Xu;Deshi Li;Sergios Theodoridis;Shuguang Robert Cui

Year: 2020 | Volume: 1 | Journal Article |

Linear Multiple Low-Rank Kernel Based Stationary Gaussian Processes Regression for Time Series

Feng Yin;Lishuo Pan;Tianshi Chen;Sergios Theodoridis;Zhi-Quan Tom Luo;Abdelhak M. Zoubir

IEEE Transactions on Signal Processing
Year: 2020 | Volume: 68 | Journal Article |
Cited by: Papers (28)
Gaussian processes (GPs) for machine learning have been studied systematically over the past two decades. However, kernel design for GPs and the associated hyper-parameters optimization are still difficult, and to a large extent open problems. We consider GP regression for time series modeling and analysis. The underlying stationary kernel is approximated closely by a new grid spectral mixture (GS...Show More

Linear Multiple Low-Rank Kernel Based Stationary Gaussian Processes Regression for Time Series

Feng Yin;Lishuo Pan;Tianshi Chen;Sergios Theodoridis;Zhi-Quan Tom Luo;Abdelhak M. Zoubir

Year: 2020 | Volume: 68 | Journal Article |

Information Assisted Dictionary Learning for fMRI Data Analysis

Manuel Morante;Yannis Kopsinis;Sergios Theodoridis;Athanassios Protopapas

IEEE Access
Year: 2020 | Volume: 8 | Journal Article |
Cited by: Papers (6)
In this paper, the task-related fMRI problem is treated in its matrix factorization form, focusing on the Dictionary Learning (DL) approach. The proposed method allows the incorporation of a priori knowledge that is associated with both the experimental design and available brain atlases. Moreover, it can cope efficiently with uncertainties in the modeling of the hemodynamic response function. In ...Show More

Information Assisted Dictionary Learning for fMRI Data Analysis

Manuel Morante;Yannis Kopsinis;Sergios Theodoridis;Athanassios Protopapas

Year: 2020 | Volume: 8 | Journal Article |

Toward the 6G Network Era: Opportunities and Challenges

Ioannis Tomkos;Dimitrios Klonidis;Evangelos Pikasis;Sergios Theodoridis

IT Professional
Year: 2020 | Volume: 22, Issue: 1 | Magazine Article |
Cited by: Papers (176)
The next generation of telecommunication networks will integrate the latest developments and emerging advancements in telecommunications connectivity infrastructures. In this article, we discuss the transformation and convergence of the fifth-generation (5G) mobile network and the internet of things technologies, toward the emergence of the smart sixth-generation (6G) networks which will employ AI...Show More

Toward the 6G Network Era: Opportunities and Challenges

Ioannis Tomkos;Dimitrios Klonidis;Evangelos Pikasis;Sergios Theodoridis

Year: 2020 | Volume: 22, Issue: 1 | Magazine Article |

Tensor-based Blind fMRI Source Separation Without the Gaussian Noise Assumption — A β-Divergence Approach

Christos Chatzichristos;Michiel Vandecapelle;Eleftherios Kofidis;Sergios Theodoridis;Lieven De Lathauwer;Sabine Van Huffel

2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP)
Year: 2019 | Conference Paper |
Cited by: Papers (2)
The advantages of tensor- over matrix-based methods have been recently demonstrated in the context of functional magnetic resonance imaging (fMRI) blind source unmixing. However, these methods rely on the assumption of a Gaussian distribution for the noise, which suggests a least squares criterion for the tensor decomposition. One can instead argue that a Rician model for the fMRI noise is much mo...Show More

Tensor-based Blind fMRI Source Separation Without the Gaussian Noise Assumption — A β-Divergence Approach

Christos Chatzichristos;Michiel Vandecapelle;Eleftherios Kofidis;Sergios Theodoridis;Lieven De Lathauwer;Sabine Van Huffel

Year: 2019 | Conference Paper |

Fusion of EEG and fMRI via Soft Coupled Tensor Decompositions

Christos Chatzichristos;Mike Davies;Javier Escudero;Eleftherios Kofidis;Sergios Theodoridis

2018 26th European Signal Processing Conference (EUSIPCO)
Year: 2018 | Conference Paper |
Cited by: Papers (16)
Data fusion refers to the joint analysis of multiple datasets which provide complementary views of the same task. In this paper, the problem of jointly analyzing electroencephalography (EEG) and functional Magnetic Resonance Imaging (fMRI) data is considered. Analyzing both EEG and fMRI measurements is highly beneficial for studying brain function because these modalities have complementary spatio...Show More

Fusion of EEG and fMRI via Soft Coupled Tensor Decompositions

Christos Chatzichristos;Mike Davies;Javier Escudero;Eleftherios Kofidis;Sergios Theodoridis

Year: 2018 | Conference Paper |

Sparse Structure Enabled Grid Spectral Mixture Kernel for Temporal Gaussian Process Regression

Feng Yin;Xinwei He;Lishuo Pan;Tianshi Chen;Zhi-Quan Tom Luo;Sergios Theodoridis

2018 21st International Conference on Information Fusion (FUSION)
Year: 2018 | Conference Paper |
Cited by: Papers (6)
We propose a modified spectral mixture (SM) kernel that serves as a universal stationary kernel for temporal Gaussian process regression (GPR). The kernel is named grid spectral mixture (GSM) kernel as we fix the frequency and variance parameters in the original SM kernel to a set of pre-selected grid points. The hyper-parameters are the non-negative weights of all sub-kernel functions and the res...Show More

Sparse Structure Enabled Grid Spectral Mixture Kernel for Temporal Gaussian Process Regression

Feng Yin;Xinwei He;Lishuo Pan;Tianshi Chen;Zhi-Quan Tom Luo;Sergios Theodoridis

Year: 2018 | Conference Paper |

Distributed Set-Theoretic Parameter Estimation in Networks with Ambiguous Measurements

Dimitris Ampeliotis;Christos Mavrokefalidis;Kostas Berberidis;Sergios Theodoridis

2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)
Year: 2018 | Conference Paper |
Cited by: Papers (1)
Distributed estimation of a parameter vector in a network of sensor nodes with ambiguous measurements is considered. These ambiguities may be due to interference, poor calibration, high noise levels or any other cause. Cooperation among the nodes is required to resolve them. In such a setting, non-convex constraint sets may be required at the nodes, in order to accurately model the local ambiguiti...Show More

Distributed Set-Theoretic Parameter Estimation in Networks with Ambiguous Measurements

Dimitris Ampeliotis;Christos Mavrokefalidis;Kostas Berberidis;Sergios Theodoridis

Year: 2018 | Conference Paper |

Online Distributed Learning Over Networks in RKH Spaces Using Random Fourier Features

Pantelis Bouboulis;Symeon Chouvardas;Sergios Theodoridis

IEEE Transactions on Signal Processing
Year: 2018 | Volume: 66, Issue: 7 | Journal Article |
Cited by: Papers (54)
We present a novel diffusion scheme for online kernel-based learning over networks. So far, a major drawback of any online learning algorithm, operating in a reproducing kernel Hilbert space (RKHS), is the need for updating a growing number of parameters as time iterations evolve. Besides complexity, this leads to an increased need of communication resources in a distributed setting. In contrast, ...Show More

Online Distributed Learning Over Networks in RKH Spaces Using Random Fourier Features

Pantelis Bouboulis;Symeon Chouvardas;Sergios Theodoridis

Year: 2018 | Volume: 66, Issue: 7 | Journal Article |

PARAFAC2 and its block term decomposition analog for blind fMRI source unmixing

Christos Chatzichristos;Eleftherios Kofidis;Sergios Theodoridis

2017 25th European Signal Processing Conference (EUSIPCO)
Year: 2017 | Conference Paper |
Cited by: Papers (6)
Tensor-based analysis of brain imaging data, in particular functional Magnetic Resonance Imaging (fMRI), has proved to be quite effective in exploiting their inherently multidimensional nature. It commonly relies on a trilinear model generating the analyzed data. This assumption, however, may prove to be quite strict in practice; for example, due to the natural intra-subject and inter-subject vari...Show More

PARAFAC2 and its block term decomposition analog for blind fMRI source unmixing

Christos Chatzichristos;Eleftherios Kofidis;Sergios Theodoridis

Year: 2017 | Conference Paper |

Assisted dictionary learning for FMRI data analysis

Manuel Morante Moreno;Yannis Kopsinis;Eleftherios Kofidis;Christos Chatzichristos;Sergios Theodoridis

2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2017 | Conference Paper |
Cited by: Papers (2)
Extracting information from functional magnetic resonance images (fMRI) has been a major area of research for more than two decades. The goal of this work is to present a new method for the analysis of fMRI data sets, that is capable to incorporate a priori available information, via an efficient optimization framework. Tests on synthetic data sets demonstrate significant performance gains over ex...Show More

Assisted dictionary learning for FMRI data analysis

Manuel Morante Moreno;Yannis Kopsinis;Eleftherios Kofidis;Christos Chatzichristos;Sergios Theodoridis

Year: 2017 | Conference Paper |

Robust Nonlinear Regression: A Greedy Approach Employing Kernels With Application to Image Denoising

George Papageorgiou;Pantelis Bouboulis;Sergios Theodoridis

IEEE Transactions on Signal Processing
Year: 2017 | Volume: 65, Issue: 16 | Journal Article |
Cited by: Papers (14)
We consider the task of robust nonlinear regression in the presence of both inlier noise and outliers. Assuming that the unknown nonlinear function belongs to a reproducing kernel Hilbert space, our goal is to estimate the set of the associated unknown parameters. Due to the presence of outliers, common techniques such as the kernel ridge regression (KRR) or the support vector regression turn out ...Show More

Robust Nonlinear Regression: A Greedy Approach Employing Kernels With Application to Image Denoising

George Papageorgiou;Pantelis Bouboulis;Sergios Theodoridis

Year: 2017 | Volume: 65, Issue: 16 | Journal Article |

Heterogeneous and Multitask Wireless Sensor Networks—Algorithms, Applications, and Challenges

Jorge Plata-Chaves;Alexander Bertrand;Marc Moonen;Sergios Theodoridis;Abdelhak M. Zoubir

IEEE Journal of Selected Topics in Signal Processing
Year: 2017 | Volume: 11, Issue: 3 | Journal Article |
Cited by: Papers (39)
Unlike traditional homogeneous single-task wireless sensor networks (WSNs), heterogeneous and multitask WSNs allow the cooperation among multiple heterogeneous devices dedicated to solving different signal processing tasks. Despite their heterogeneous nature and the fact that each device may solve a different task, the devices could still benefit from a collaboration between them to achieve a supe...Show More

Heterogeneous and Multitask Wireless Sensor Networks—Algorithms, Applications, and Challenges

Jorge Plata-Chaves;Alexander Bertrand;Marc Moonen;Sergios Theodoridis;Abdelhak M. Zoubir

Year: 2017 | Volume: 11, Issue: 3 | Journal Article |

Adapt-Align-Combine for diffusion-based distributed dictionary learning

D. Ampeliotis;C. Mavrokefalidis;K. Berberidis;S. Theodoridis

2016 24th European Signal Processing Conference (EUSIPCO)
Year: 2016 | Conference Paper |
Cited by: Papers (5)
Diffusion-based distributed dictionary learning methods are studied in this work. We consider the classical mixed l2-l1 cost function, that employs an l2 representation error term and an l1 sparsity promoting regularizer. First, we observe that this cost function suffers from an inherent permutation ambiguity. This ambiguity may deteriorate significantly the performance of diffusion-based schemes,...Show More

Adapt-Align-Combine for diffusion-based distributed dictionary learning

D. Ampeliotis;C. Mavrokefalidis;K. Berberidis;S. Theodoridis

Year: 2016 | Conference Paper |

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