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2020 – today
- 2024
[c51]Yuki Akiyama, Konstantinos Slavakis:
Proximal Bellman Mappings for Reinforcement Learning and Their Application to Robust Adaptive Filtering. ICASSP 2024: 5855-5859- [c50]Duc Thien Nguyen, Konstantinos Slavakis:
Multi-Linear Kernel Regression and Imputation VIA Manifold Learning: the Dynamic MRI Case. ICASSP 2024: 9466-9470 - [i22]Duc Thien Nguyen, Konstantinos Slavakis:
Multilinear Kernel Regression and Imputation via Manifold Learning. CoRR abs/2402.03648 (2024) - [i21]Yuki Akiyama, Minh Vu, Konstantinos Slavakis:
Nonparametric Bellman Mappings for Reinforcement Learning: Application to Robust Adaptive Filtering. CoRR abs/2403.20020 (2024) - [i20]Minh Vu, Konstantinos Slavakis:
Gaussian-Mixture-Model Q-Functions for Reinforcement Learning by Riemannian Optimization. CoRR abs/2409.04374 (2024) - [i19]Duc Thien Nguyen, Konstantinos Slavakis, Dimitris Pados:
Imputation of Time-varying Edge Flows in Graphs by Multilinear Kernel Regression and Manifold Learning. CoRR abs/2409.05135 (2024) - 2023
- [c49]Minh Vu, Yuki Akiyama, Konstantinos Slavakis:
Dynamic Selection of p-norm in Linear Adaptive Filtering via online Kernel-based Reinforcement Learning. ICASSP 2023: 1-5 - [i18]Duc Thien Nguyen, Konstantinos Slavakis:
Multi-Linear Kernel Regression and Imputation in Data Manifolds. CoRR abs/2304.03041 (2023) - [i17]Yuki Akiyama, Konstantinos Slavakis:
Proximal Bellman mappings for reinforcement learning and their application to robust adaptive filtering. CoRR abs/2309.07548 (2023) - 2022
- [j28]Konstantinos Slavakis
, Gaurav N. Shetty, Loris Cannelli, Gesualdo Scutari, Ukash Nakarmi, Leslie Ying:
Kernel Regression Imputation in Manifolds Via Bi-Linear Modeling: The Dynamic-MRI Case. IEEE Trans. Computational Imaging 8: 133-147 (2022) - [d1]Konstantinos Slavakis, Gaurav N. Shetty:
Kernel Regression Imputation in Manifolds: Videos of dMRI data. IEEE DataPort, 2022
- [i16]Minh Vu, Yuki Akiyama, Konstantinos Slavakis:
Dynamic selection of p-norm in linear adaptive filtering via online kernel-based reinforcement learning. CoRR abs/2210.11317 (2022) - [i15]Yuki Akiyama, Minh Vu, Konstantinos Slavakis:
online and lightweight kernel-based approximated policy iteration for dynamic p-norm linear adaptive filtering. CoRR abs/2210.11755 (2022) - 2021
- [j27]Cong Ye, Konstantinos Slavakis, Pratik V. Patil, Johan Nakuci, Sarah Feldt Muldoon, John D. Medaglia:
Network clustering via kernel-ARMA modeling and the Grassmannian: The brain-network case. Signal Process. 179: 107834 (2021) - [c48]Cong Ye, Konstantinos Slavakis, Johan Nakuci, Sarah Feldt Muldoon, John D. Medaglia:
Online Classification of Dynamic Multilayer-Network Time Series in Riemannian Manifolds. ICASSP 2021: 3815-3819 - [c47]Konstantinos Slavakis, Masahiro Yukawa:
Outlier-Robust Kernel Hierarchical-Optimization RLS on a Budget with Affine Constraints. ICASSP 2021: 5335-5339 - 2020
- [j26]Konstantinos Slavakis, Sinjini Banerjee:
Robust Hierarchical-Optimization RLS Against Sparse Outliers. IEEE Signal Process. Lett. 27: 171-175 (2020) - [j25]Gaurav N. Shetty, Konstantinos Slavakis, Abhishek Bose, Ukash Nakarmi, Gesualdo Scutari, Leslie Ying:
Bi-Linear Modeling of Data Manifolds for Dynamic-MRI Recovery. IEEE Trans. Medical Imaging 39(3): 688-702 (2020) - [c46]Gaurav N. Shetty, Konstantinos Slavakis, Ukash Nakarmi, Gesualdo Scutari, Leslie Ying:
Kernel Bi-Linear Modeling for Reconstructing Data on Manifolds: The Dynamic-MRI Case. EUSIPCO 2020: 1482-1486 - [i14]Cong Ye, Konstantinos Slavakis, Pratik V. Patil, Johan Nakuci, Sarah Feldt Muldoon, John D. Medaglia:
Network Clustering Via Kernel-ARMA Modeling and the Grassmannian The Brain-Network Case. CoRR abs/2002.09943 (2020) - [i13]Gaurav N. Shetty, Konstantinos Slavakis, Ukash Nakarmi, Gesualdo Scutari, Leslie Ying:
Kernel Bi-Linear Modeling for Reconstructing Data on Manifolds: The Dynamic-MRI Case. CoRR abs/2002.11885 (2020)
2010 – 2019
- 2019
- [j24]Konstantinos Slavakis:
The Stochastic Fejér-Monotone Hybrid Steepest Descent Method and the Hierarchical RLS. IEEE Trans. Signal Process. 67(11): 2868-2883 (2019) - [i12]Cong Ye, Konstantinos Slavakis, Pratik V. Patil, Sarah Feldt Muldoon, John D. Medaglia:
Brain-Network Clustering via Kernel-ARMA Modeling and the Grassmannian. CoRR abs/1906.02292 (2019) - [i11]Konstantinos Slavakis, Sinjini Banerjee:
Robust Hierarchical-Optimization RLS Against Sparse Outliers. CoRR abs/1910.05399 (2019) - 2018
- [j23]Konstantinos Slavakis, Shiva Salsabilian, David S. Wack, Sarah Feldt Muldoon, Henry E. Baidoo-Williams, Jean M. Vettel, Matthew Cieslak, Scott T. Grafton:
Clustering Brain-Network Time Series by Riemannian Geometry. IEEE Trans. Signal Inf. Process. over Networks 4(3): 519-533 (2018) - [c45]Konstantinos Slavakis:
Stochastic Composite Convex Minimization with Affine Constraints. ACSSC 2018: 1871-1875 - [c44]Konstantinos Slavakis, Aritra Konar, Nicholas D. Sidiropoulos:
Fast Projection-Based Solvers for the Non-Convex Quadratically Constrained Feasibility Problem. ICASSP 2018: 3954-3958 - [c43]Ukash Nakarmi, Konstantinos Slavakis, Leslie Ying:
MLS: Joint manifold-learning and sparsity-aware framework for highly accelerated dynamic magnetic resonance imaging. ISBI 2018: 1213-1216 - [i10]Gaurav N. Shetty, Konstantinos Slavakis, Abhishek Bose, Ukash Nakarmi, Leslie Ying, Gesualdo Scutari:
Bi-Linear Modeling of Data Manifolds for Dynamic-MRI Recovery. CoRR abs/1812.10617 (2018) - 2017
- [c42]Konstantinos Slavakis, Gaurav N. Shetty, Abhishek Bose, Ukash Nakarmi, Leslie Ying:
Bi-Linear modeling of manifold-data geometry for Dynamic-MRI recovery. CAMSAP 2017: 1-5 - [c41]Konstantinos Slavakis, Isao Yamada, Shunsuke Ono:
Accelerating the hybrid steepest descent method for affinely constrained convex composite minimization tasks. ICASSP 2017: 4711-4715 - [c40]Ukash Nakarmi, Konstantinos Slavakis, Jingyuan Lyu, Leslie Ying:
M-MRI: A manifold-based framework to highly accelerated dynamic magnetic resonance imaging. ISBI 2017: 19-22 - [i9]Konstantinos Slavakis, Shiva Salsabilian, David S. Wack, Sarah Feldt Muldoon, Henry E. Baidoo-Williams, Jean M. Vettel, Matthew Cieslak, Scott T. Grafton:
Riemannian-geometry-based modeling and clustering of network-wide non-stationary time series: The brain-network case. CoRR abs/1701.07767 (2017) - 2016
- [c39]Konstantinos Slavakis, Shiva Salsabilian, David S. Wack, Sarah Feldt Muldoon, Henry E. Baidoo-Williams, Jean M. Vettel, Matthew Cieslak, Scott T. Grafton:
Clustering brain-network-connectivity states using kernel partial correlations. ACSSC 2016: 268-272 - [c38]G. V. Karanikolas, Georgios B. Giannakis, Konstantinos Slavakis, Richard M. Leahy:
Multi-kernel based nonlinear models for connectivity identification of brain networks. ICASSP 2016: 6315-6319 - [c37]Ukash Nakarmi, Yihang Zhou, Jingyuan Lyu, Konstantinos Slavakis, Leslie Ying:
Accelerating dynamic magnetic resonance imaging by nonlinear sparse coding. ISBI 2016: 510-513 - [c36]Konstantinos Slavakis, Shiva Salsabilian, David S. Wack, Sarah Feldt Muldoon:
Clustering time-varying connectivity networks by riemannian geometry: The brain-network case. SSP 2016: 1-5 - 2015
- [j22]Panagiotis A. Traganitis, Konstantinos Slavakis, Georgios B. Giannakis:
Sketch and Validate for Big Data Clustering. IEEE J. Sel. Top. Signal Process. 9(4): 678-690 (2015) - [c35]Panagiotis A. Traganitis, Konstantinos Slavakis, Georgios B. Giannakis:
Large-scale subspace clustering using random sketching and validation. ACSSC 2015: 107-111 - [c34]Xu Wang, Konstantinos Slavakis, Gilad Lerman:
Multi-Manifold Modeling in Non-Euclidean spaces. AISTATS 2015 - [c33]Panagiotis A. Traganitis, Konstantinos Slavakis, Georgios B. Giannakis:
Spectral clustering of large-scale communities via random sketching and validation. CISS 2015: 1-6 - [i8]Panagiotis A. Traganitis, Konstantinos Slavakis, Georgios B. Giannakis:
Sketch and Validate for Big Data Clustering. CoRR abs/1501.05590 (2015) - [i7]Panagiotis A. Traganitis, Konstantinos Slavakis, Georgios B. Giannakis:
Large-scale subspace clustering using sketching and validation. CoRR abs/1510.01628 (2015) - 2014
- [j21]Konstantinos Slavakis, Georgios B. Giannakis, Gonzalo Mateos:
Modeling and Optimization for Big Data Analytics: (Statistical) learning tools for our era of data deluge. IEEE Signal Process. Mag. 31(5): 18-31 (2014) - [j20]Konstantinos Slavakis, Seung-Jun Kim, Gonzalo Mateos, Georgios B. Giannakis:
Stochastic Approximation vis-a-vis Online Learning for Big Data Analytics [Lecture Notes]. IEEE Signal Process. Mag. 31(6): 124-129 (2014) - [c32]Panagiotis A. Traganitis, Konstantinos Slavakis, Georgios B. Giannakis:
Big data clustering via random sketching and validation. ACSSC 2014: 1046-1050 - [c31]Mahdi Zamanighomi, Zhengdao Wang, Konstantinos Slavakis, Georgios B. Giannakis:
Linear minimum mean-square error estimation based on high-dimensional data with missing values. CISS 2014: 1-5 - [c30]Panagiotis A. Traganitis, Konstantinos Slavakis, Georgios B. Giannakis:
Clustering high-dimensional data via random sampling and consensus. GlobalSIP 2014: 307-311 - [c29]Konstantinos Slavakis, Georgios B. Giannakis:
Online dictionary learning from big data using accelerated stochastic approximation algorithms. ICASSP 2014: 16-20 - [i6]Xu Wang, Konstantinos Slavakis, Gilad Lerman:
Riemannian Multi-Manifold Modeling. CoRR abs/1410.0095 (2014) - 2013
- [j19]Symeon Chouvardas, Konstantinos Slavakis, Sergios Theodoridis:
Trading off Complexity With Communication Costs in Distributed Adaptive Learning via Krylov Subspaces for Dimensionality Reduction. IEEE J. Sel. Top. Signal Process. 7(2): 257-273 (2013) - [j18]Konstantinos Slavakis, Isao Yamada:
The Adaptive Projected Subgradient Method Constrained by Families of Quasi-nonexpansive Mappings and Its Application to Online Learning. SIAM J. Optim. 23(1): 126-152 (2013) - [j17]Symeon Chouvardas, Konstantinos Slavakis, Sergios Theodoridis, Isao Yamada:
Stochastic Analysis of Hyperslab-Based Adaptive Projected Subgradient Method Under Bounded Noise. IEEE Signal Process. Lett. 20(7): 729-732 (2013) - [j16]Konstantinos Slavakis, Yannis Kopsinis, Sergios Theodoridis, Stephen McLaughlin:
Generalized Thresholding and Online Sparsity-Aware Learning in a Union of Subspaces. IEEE Trans. Signal Process. 61(15): 3760-3773 (2013) - [c28]Sergios Theodoridis, Yannis Kopsinis, Konstantinos Slavakis, Symeon Chouvardas:
Sparsity-Aware Adaptive Learning: A Set Theoretic Estimation Approach. ALCOSP 2013: 748-756 - [c27]Konstantinos Slavakis, Georgios B. Giannakis, Geert Leus:
Robust sparse embedding and reconstruction via dictionary learning. CISS 2013: 1-6 - [c26]Konstantinos Slavakis, Yannis Kopsinis, Sergios Theodoridis:
New operators for fixed-point theory: The sparsity-aware learning case. EUSIPCO 2013: 1-5 - [c25]Konstantinos Slavakis, Geert Leus, Georgios B. Giannakis:
Online robust portfolio risk management using total least-squares and parallel splitting algorithms. ICASSP 2013: 5686-5690 - [c24]Yannis Kopsinis, Konstantinos Slavakis, Sergios Theodoridis, Stephen McLaughlin:
Thresholding-based online algorithms of complexity comparable to sparse LMS methods. ISCAS 2013: 513-516 - [c23]Konstantinos Slavakis, Yannis Kopsinis, Sergios Theodoridis, Georgios B. Giannakis, Vassilis Kekatos:
Generalized Iterative Thresholding for Sparsity-Aware Online Volterra System Identification. ISWCS 2013: 1-5 - 2012
- [j15]Konstantinos Slavakis, Pantelis Bouboulis, Sergios Theodoridis:
Adaptive Multiregression in Reproducing Kernel Hilbert Spaces: The Multiaccess MIMO Channel Case. IEEE Trans. Neural Networks Learn. Syst. 23(2): 260-276 (2012) - [j14]Pantelis Bouboulis, Konstantinos Slavakis, Sergios Theodoridis:
Adaptive Learning in Complex Reproducing Kernel Hilbert Spaces Employing Wirtinger's Subgradients. IEEE Trans. Neural Networks Learn. Syst. 23(3): 425-438 (2012) - [j13]Symeon Chouvardas, Konstantinos Slavakis, Yannis Kopsinis, Sergios Theodoridis:
A Sparsity Promoting Adaptive Algorithm for Distributed Learning. IEEE Trans. Signal Process. 60(10): 5412-5425 (2012) - [c22]Symeon Chouvardas, Konstantinos Slavakis, Yannis Kopsinis, Sergios Theodoridis:
Sparsity-promoting adaptive algorithm for distributed learning in diffusion networks. EUSIPCO 2012: 1084-1088 - [c21]Yannis Kopsinis, Konstantinos Slavakis, Sergios Theodoridis, Steve McLaughlin:
Generalized thresholding sparsity-aware algorithm for low complexity online learning. ICASSP 2012: 3277-3280 - [i5]Sergios Theodoridis, Yannis Kopsinis, Konstantinos Slavakis:
Sparsity-Aware Learning and Compressed Sensing: An Overview. CoRR abs/1211.5231 (2012) - 2011
- [j12]Sergios Theodoridis, Konstantinos Slavakis, Isao Yamada:
Adaptive Learning in a World of Projections. IEEE Signal Process. Mag. 28(1): 97-123 (2011) - [j11]Yannis Kopsinis, Konstantinos Slavakis, Sergios Theodoridis:
Online Sparse System Identification and Signal Reconstruction Using Projections Onto Weighted ell1 Balls. IEEE Trans. Signal Process. 59(3): 936-952 (2011) - [j10]Symeon Chouvardas, Konstantinos Slavakis, Sergios Theodoridis:
Adaptive Robust Distributed Learning in Diffusion Sensor Networks. IEEE Trans. Signal Process. 59(10): 4692-4707 (2011) - [c20]Konstantinos Slavakis, Yannis Kopsinis, Sergios Theodoridis:
Robust adaptive sparse system identification by using weighted l1 balls and Moreau envelopes. EUSIPCO 2011: 1924-1928 - [c19]Symeon Chouvardas, Konstantinos Slavakis, Sergios Theodoridis:
Trading off communications bandwidth with accuracy in adaptive diffusion networks. ICASSP 2011: 2048-2051 - [c18]Konstantinos Slavakis, Yannis Kopsinis, Sergios Theodoridis:
Revisiting adaptive least-squares estimation and application to online sparse signal recovery. ICASSP 2011: 4292-4295 - [c17]Yannis Kopsinis, Konstantinos Slavakis, Sergios Theodoridis, Steve McLaughlin:
Reduced complexity online sparse signal reconstruction using projections onto weighted ℓ1 balls. DSP 2011: 1-8 - [i4]Yannis Kopsinis, Konstantinos Slavakis, Sergios Theodoridis, Steve McLaughlin:
Generalized Thresholding Sparsity-Aware Online Learning in a Union of Subspaces. CoRR abs/1112.0665 (2011) - [i3]Symeon Chouvardas, Konstantinos Slavakis, Yannis Kopsinis, Sergios Theodoridis:
A Sparsity-Aware Adaptive Algorithm for Distributed Learning. CoRR abs/1112.5716 (2011) - 2010
- [j9]Masahiro Yukawa, Konstantinos Slavakis, Isao Yamada:
Multi-Domain Adaptive Learning Based on Feasibility Splitting and Adaptive Projected Subgradient Method. IEICE Trans. Fundam. Electron. Commun. Comput. Sci. 93-A(2): 456-466 (2010) - [j8]Pantelis Bouboulis, Konstantinos Slavakis, Sergios Theodoridis:
Adaptive Kernel-Based Image Denoising Employing Semi-Parametric Regularization. IEEE Trans. Image Process. 19(6): 1465-1479 (2010) - [c16]Symeon Chouvardas, Konstantinos Slavakis, Sergios Theodoridis:
A novel adaptive algorithm for diffusion networks using projections onto hyperslabs. CIP 2010: 393-398 - [c15]Konstantinos Slavakis, Yannis Kopsinis, Sergios Theodoridis:
Adaptive algorithm for sparse system identification using projections onto weighted l1 balls. ICASSP 2010: 3742-3745 - [c14]Masahiro Yukawa, Konstantinos Slavakis, Isao Yamada:
Multi-domain adaptive filtering by feasibility splitting. ICASSP 2010: 3814-3817 - [c13]Pantelis Bouboulis, Sergios Theodoridis, Konstantinos Slavakis:
Edge Preserving Image Denoising in Reproducing Kernel Hilbert Spaces. ICPR 2010: 2660-2663 - [i2]Yannis Kopsinis, Konstantinos Slavakis, Sergios Theodoridis:
Online Sparse System Identification and Signal Reconstruction using Projections onto Weighted ℓ1 Balls. CoRR abs/1004.3040 (2010) - [i1]Konstantinos Slavakis, Isao Yamada:
Asymptotic minimization of sequences of loss functions constrained by families of quasi-nonexpansive mappings and its application to online learning. CoRR abs/1008.5231 (2010)
2000 – 2009
- 2009
- [j7]Konstantinos Slavakis, Sergios Theodoridis, Isao Yamada:
Adaptive constrained learning in reproducing Kernel Hilbert spaces: the robust beamforming case. IEEE Trans. Signal Process. 57(12): 4744-4764 (2009) - [c12]Konstantinos Slavakis, Sergios Theodoridis:
Affinely constrained online learning and its application to beamforming. ICASSP 2009: 1573-1576 - [c11]Masahiro Yukawa, Konstantinos Slavakis, Isao Yamada:
Signal processing in dual domain by adaptive projected subgradient method. DPS 2009: 1-6 - 2008
- [j6]Konstantinos Slavakis, Sergios Theodoridis:
Sliding Window Generalized Kernel Affine Projection Algorithm Using Projection Mappings. EURASIP J. Adv. Signal Process. 2008 (2008) - [j5]Konstantinos Slavakis, Sergios Theodoridis, Isao Yamada:
Online Kernel-Based Classification Using Adaptive Projection Algorithms. IEEE Trans. Signal Process. 56(7-1): 2781-2796 (2008) - [c10]Konstantinos Slavakis, Sergios Theodoridis, Isao Yamada:
Robust adaptive nonlinear beamforming by kernels and projection mappings. EUSIPCO 2008: 1-5 - [c9]Konstantinos Slavakis, Sergios Theodoridis:
Sliding window online Kernel-based classification by projection mappings. ISCAS 2008: 49-52 - 2007
- [j4]Masahiro Yukawa, Konstantinos Slavakis, Isao Yamada:
Adaptive Parallel Quadratic-Metric Projection Algorithms. IEEE Trans. Speech Audio Process. 15(5): 1665-1680 (2007) - [j3]Konstantinos Slavakis, Isao Yamada:
Robust Wideband Beamforming by the Hybrid Steepest Descent Method. IEEE Trans. Signal Process. 55(9): 4511-4522 (2007) - [c8]Florendia Fourli-Kartsouni, Konstantinos Slavakis, Georgios Kouroupetroglou, Sergios Theodoridis:
A Bayesian Network Approach to Semantic Labelling of Text Formatting in XML Corpora of Documents. HCI (7) 2007: 299-308 - [c7]Konstantinos Slavakis, Sergios Theodoridis, Isao Yamada:
Online Kernel-Based Classification by Projections. ICASSP (2) 2007: 425-428 - 2006
- [c6]Konstantinos Slavakis, Masahiro Yukawa, Isao Yamada:
Robust Capon Beamforming by the Adaptive Projected Subgradient Method. ICASSP (4) 2006: 1005-1008 - [c5]Isao Yamada, Konstantinos Slavakis, Masahiro Yukawa, Renato L. G. Cavalcante:
Adaptive projected subgradient method and its applications to robust signal processing. ISCAS 2006 - 2003
- [j2]Konstantinos Slavakis, Isao Yamada, Kohichi Sakaniwa:
Computation of symmetric positive definite Toeplitz matrices by the hybrid steepest descent method. Signal Process. 83(5): 1135-1140 (2003) - 2002
- [j1]Isao Yamada, Konstantinos Slavakis, Kenyu Yamada:
An efficient robust adaptive filtering algorithm based on parallel subgradient projection techniques. IEEE Trans. Signal Process. 50(5): 1091-1101 (2002) - [c4]Konstantinos Slavakis, Isao Yamada, Kohichi Sakaniwa:
Spectrum estimation of real vector wide sense stationary processes by the Hybrid Steepest Descent Method. ICASSP 2002: 1357-1360 - 2001
- [c3]Isao Yamada, Konstantinos Slavakis, Kenyu Yamada:
An efficient robust adaptive filtering scheme based on parallel subgradient projection techniques. ICASSP 2001: 3725-3728 - [c2]Konstantinos Slavakis, Isao Yamada:
Compactly supported matrix valued wavelets-biorthogonal unconditional bases. ISCAS (2) 2001: 485-488
1990 – 1999
- 1999
- [c1]Konstantinos Slavakis, Isao Yamada:
Biorthogonal bases of compactly supported matrix valued wavelets. ISSPA 1999: 981-984
Coauthor Index
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