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2020 – today
- 2024
[j47]Hugo Lebeau
, Florent Chatelain, Romain Couillet:
Asymptotic Gaussian Fluctuations of Eigenvectors in Spectral Clustering. IEEE Signal Process. Lett. 31: 1920-1924 (2024)- [i58]Hugo Lebeau, Florent Chatelain, Romain Couillet:
A Random Matrix Approach to Low-Multilinear-Rank Tensor Approximation. CoRR abs/2402.03169 (2024) - [i57]Hugo Lebeau, Florent Chatelain, Romain Couillet:
Asymptotic Gaussian Fluctuations of Eigenvectors in Spectral Clustering. CoRR abs/2402.12302 (2024) - [i56]Victor Leger, Romain Couillet:
A Large Dimensional Analysis of Multi-task Semi-Supervised Learning. CoRR abs/2402.13646 (2024) - [i55]Victor Leger, Romain Couillet:
Asymptotic Bayes risk of semi-supervised learning with uncertain labeling. CoRR abs/2403.17767 (2024) - 2023
- [c99]Minh-Toan Nguyen, Romain Couillet:
Asymptotic Bayes risk of semi-supervised multitask learning on Gaussian mixture. AISTATS 2023: 5063-5078 - [c98]Cyprien Doz, Chengfang Ren, Jean Philippe Ovarlez, Romain Couillet:
Large Dimensional Analysis of LS-SVM Transfer Learning: Application to Polsar Classification. ICASSP 2023: 1-5 - [c97]Malik Tiomoko, Romain Couillet, Frédéric Pascal:
PCA-based Multi-Task Learning: a Random Matrix Approach. ICML 2023: 34280-34300 - [i54]Minh-Toan Nguyen, Romain Couillet:
Asymptotic Bayes risk of semi-supervised multitask learning on Gaussian mixture. CoRR abs/2303.02048 (2023) - 2022
- [j46]José Henrique de Morais Goulart, Romain Couillet, Pierre Comon:
A Random Matrix Perspective on Random Tensors. J. Mach. Learn. Res. 23: 264:1-264:36 (2022) - [j45]Romain Couillet, Denis Trystram, Thierry Ménissier:
The Submerged Part of the AI-Ceberg [Perspectives]. IEEE Signal Process. Mag. 39(5): 10-17 (2022) - [c96]Hafiz Tiomoko Ali, Zhenyu Liao, Romain Couillet:
Random matrices in service of ML footprint: ternary random features with no performance loss. ICLR 2022 - [c95]Hugo Lebeau, Romain Couillet, Florent Chatelain:
A Random Matrix Analysis of Data Stream Clustering: Coping With Limited Memory Resources. ICML 2022: 12253-12281 - 2021
- [j44]Xiaoyi Mai, Romain Couillet:
Consistent Semi-Supervised Graph Regularization for High Dimensional Data. J. Mach. Learn. Res. 22: 94:1-94:48 (2021) - [j43]Lorenzo Dall'Amico, Romain Couillet, Nicolas Tremblay:
A Unified Framework for Spectral Clustering in Sparse Graphs. J. Mach. Learn. Res. 22: 217:1-217:56 (2021) - [c94]Mohamed El Amine Seddik, Cosme Louart, Romain Couillet, Mohamed Tamaazousti:
The Unexpected Deterministic and Universal Behavior of Large Softmax Classifiers. AISTATS 2021: 1045-1053 - [c93]Charles Séjourné, Romain Couillet, Pierre Comon:
A Large-Dimensional Analysis of Symmetric SNE. ICASSP 2021: 2970-2974 - [c92]Zhenyu Liao, Romain Couillet, Michael W. Mahoney:
Sparse Quantized Spectral Clustering. ICLR 2021 - [c91]Malik Tiomoko, Hafiz Tiomoko Ali, Romain Couillet:
Deciphering and Optimizing Multi-Task Learning: a Random Matrix Approach. ICLR 2021 - [c90]Romain Couillet, Florent Chatelain, Nicolas Le Bihan:
Two-way kernel matrix puncturing: towards resource-efficient PCA and spectral clustering. ICML 2021: 2156-2165 - [i53]Romain Couillet, Florent Chatelain, Nicolas Le Bihan:
Two-way kernel matrix puncturing: towards resource-efficient PCA and spectral clustering. CoRR abs/2102.12293 (2021) - [i52]Lorenzo Dall'Amico, Romain Couillet, Nicolas Tremblay:
Nishimori meets Bethe: a spectral method for node classification in sparse weighted graphs. CoRR abs/2103.03561 (2021) - [i51]José Henrique de Morais Goulart, Romain Couillet, Pierre Comon:
A Random Matrix Perspective on Random Tensors. CoRR abs/2108.00774 (2021) - [i50]Hafiz Tiomoko Ali, Zhenyu Liao, Romain Couillet:
Random matrices in service of ML footprint: ternary random features with no performance loss. CoRR abs/2110.01899 (2021) - [i49]Sami Fakhry, Romain Couillet, Malik Tiomoko:
Multi-task learning on the edge: cost-efficiency and theoretical optimality. CoRR abs/2110.04639 (2021) - [i48]Malik Tiomoko, Romain Couillet, Frédéric Pascal:
PCA-based Multi Task Learning: a Random Matrix Approach. CoRR abs/2111.00924 (2021) - 2020
- [j42]Khalil Elkhalil, Abla Kammoun, Romain Couillet, Tareq Y. Al-Naffouri, Mohamed-Slim Alouini:
A Large Dimensional Study of Regularized Discriminant Analysis. IEEE Trans. Signal Process. 68: 2464-2479 (2020) - [c89]Romain Couillet, Yagmur Gizem Cinar, Éric Gaussier, Muhammad Imran:
Word Representations Concentrate and This is Good News! CoNLL 2020: 325-334 - [c88]Lorenzo Dall'Amico, Romain Couillet, Nicolas Tremblay:
Optimal Laplacian Regularization for Sparse Spectral Community Detection. ICASSP 2020: 3237-3241 - [c87]Malik Tiomoko, Cosme Louart, Romain Couillet:
Large Dimensional Asymptotics of Multi-Task Learning. ICASSP 2020: 8787-8791 - [c86]Mohamed El Amine Seddik, Cosme Louart, Mohamed Tamaazousti, Romain Couillet:
Random Matrix Theory Proves that Deep Learning Representations of GAN-data Behave as Gaussian Mixtures. ICML 2020: 8573-8582 - [c85]Tayeb Zarrouk, Romain Couillet, Florent Chatelain, Nicolas Le Bihan:
Performance-Complexity Trade-Off in Large Dimensional Statistics. MLSP 2020: 1-6 - [c84]Lorenzo Dall'Amico, Romain Couillet, Nicolas Tremblay:
Community detection in sparse time-evolving graphs with a dynamical Bethe-Hessian. NeurIPS 2020 - [c83]Zhenyu Liao, Romain Couillet, Michael W. Mahoney:
A random matrix analysis of random Fourier features: beyond the Gaussian kernel, a precise phase transition, and the corresponding double descent. NeurIPS 2020 - [i47]Mohamed El Amine Seddik, Cosme Louart, Mohamed Tamaazousti, Romain Couillet:
Random Matrix Theory Proves that Deep Learning Representations of GAN-data Behave as Gaussian Mixtures. CoRR abs/2001.08370 (2020) - [i46]Lorenzo Dall'Amico, Romain Couillet, Nicolas Tremblay:
A unified framework for spectral clustering in sparse graphs. CoRR abs/2003.09198 (2020) - [i45]Lorenzo Dall'Amico, Romain Couillet, Nicolas Tremblay:
Community detection in sparse time-evolving graphs with a dynamical Bethe-Hessian. CoRR abs/2006.04510 (2020) - [i44]Zhenyu Liao, Romain Couillet, Michael W. Mahoney:
A random matrix analysis of random Fourier features: beyond the Gaussian kernel, a precise phase transition, and the corresponding double descent. CoRR abs/2006.05013 (2020) - [i43]Xiaoyi Mai, Romain Couillet:
Consistent Semi-Supervised Graph Regularization for High Dimensional Data. CoRR abs/2006.07575 (2020) - [i42]Malik Tiomoko Ali, Romain Couillet, Hafiz Tiomoko:
Large Dimensional Analysis and Improvement of Multi Task Learning. CoRR abs/2009.01591 (2020) - [i41]Zhenyu Liao, Romain Couillet, Michael W. Mahoney:
Sparse Quantized Spectral Clustering. CoRR abs/2010.01376 (2020)
2010 – 2019
- 2019
- [j41]Romain Couillet, Malik Tiomoko, Steeve Zozor, Eric Moisan:
Random matrix-improved estimation of covariance matrix distances. J. Multivar. Anal. 174 (2019) - [j40]Zhenyu Liao, Romain Couillet:
A Large Dimensional Analysis of Least Squares Support Vector Machines. IEEE Trans. Signal Process. 67(4): 1065-1074 (2019) - [c82]Malik Tiomoko, Romain Couillet:
Estimation of Covariance Matrix Distances in the High Dimension Low Sample Size Regime. CAMSAP 2019: 341-345 - [c81]Romain Couillet:
A Random Matrix Analysis and Optimization Framework to Large Dimensional Transfer Learning. CAMSAP 2019: 401-404 - [c80]Romain Couillet:
High Dimensional Robust Classification: A Random Matrix Analysis. CAMSAP 2019: 420-424 - [c79]Zhenyu Liao, Romain Couillet:
On Inner-Product Kernels of High Dimensional Data. CAMSAP 2019: 579-583 - [c78]Cosme Louart, Romain Couillet:
A Concentration of Measure Perspective to Robust Statistics. CAMSAP 2019: 679-683 - [c77]Arun Kadavankandy, Romain Couillet:
Asymptotic Gaussian Fluctuations of Spectral Clustering Eigenvectors. CAMSAP 2019: 694-698 - [c76]Malik Tiomoko, Romain Couillet:
Random Matrix-Improved Estimation of the Wasserstein Distance between two Centered Gaussian Distributions. EUSIPCO 2019: 1-5 - [c75]Lorenzo Dall'Amico, Romain Couillet:
Community Detection in Sparse Realistic Graphs: Improving the Bethe Hessian. ICASSP 2019: 2942-2946 - [c74]Xiaoyi Mai, Zhenyu Liao, Romain Couillet:
A Large Scale Analysis of Logistic Regression: Asymptotic Performance and New Insights. ICASSP 2019: 3357-3361 - [c73]Xiaoyi Mai, Romain Couillet:
Revisiting and Improving Semi-supervised Learning: A Large Dimensional Approach. ICASSP 2019: 3547-3551 - [c72]Malik Tiomoko, Romain Couillet, Eric Moisan, Steeve Zozor:
Improved Estimation of the Distance between Covariance Matrices. ICASSP 2019: 7445-7449 - [c71]Mohamed El Amine Seddik, Mohamed Tamaazousti, Romain Couillet:
Kernel Random Matrices of Large Concentrated Data: the Example of GAN-Generated Images. ICASSP 2019: 7480-7484 - [c70]Hafiz Tiomoko Ali, Sijia Liu, Yasin Yilmaz, Romain Couillet, Indika Rajapakse, Alfred O. Hero III:
Latent Heterogeneous Multilayer Community Detection. ICASSP 2019: 8142-8146 - [c69]Mohamed El Amine Seddik, Mohamed Tamaazousti, Romain Couillet:
A Kernel Random Matrix-Based Approach for Sparse PCA. ICLR (Poster) 2019 - [c68]Malik Tiomoko, Romain Couillet, Florent Bouchard, Guillaume Ginolhac:
Random Matrix Improved Covariance Estimation for a Large Class of Metrics. ICML 2019: 6254-6263 - [c67]Lorenzo Dall'Amico, Romain Couillet, Nicolas Tremblay:
Revisiting the Bethe-Hessian: Improved Community Detection in Sparse Heterogeneous Graphs. NeurIPS 2019: 4039-4049 - [i40]Lorenzo Dall'Amico, Romain Couillet, Nicolas Tremblay:
Optimized Deformed Laplacian for Spectrum-based Community Detection in Sparse Heterogeneous Graphs. CoRR abs/1901.09715 (2019) - [i39]Malik Tiomoko, Florent Bouchard, Guillaume Ginolhac, Romain Couillet:
Random Matrix Improved Covariance Estimation for a Large Class of Metrics. CoRR abs/1902.02554 (2019) - [i38]Malik Tiomoko, Romain Couillet:
Random Matrix-Improved Estimation of the Wasserstein Distance between two Centered Gaussian Distributions. CoRR abs/1903.03447 (2019) - [i37]Zhenyu Liao, Romain Couillet:
Inner-product Kernels are Asymptotically Equivalent to Binary Discrete Kernels. CoRR abs/1909.06788 (2019) - [i36]Lorenzo Dall'Amico, Romain Couillet, Nicolas Tremblay:
Optimal Laplacian regularization for sparse spectral community detection. CoRR abs/1912.01419 (2019) - 2018
- [j39]Abla Kammoun, Romain Couillet, Frédéric Pascal, Mohamed-Slim Alouini:
Optimal Design of the Adaptive Normalized Matched Filter Detector Using Regularized Tyler Estimators. IEEE Trans. Aerosp. Electron. Syst. 54(2): 755-769 (2018) - [j38]Liusha Yang, Matthew R. McKay, Romain Couillet:
High-Dimensional MVDR Beamforming: Optimized Solutions Based on Spiked Random Matrix Models. IEEE Trans. Signal Process. 66(7): 1933-1947 (2018) - [j37]Nicolas Auguin, David Morales-Jiménez, Matthew R. McKay, Romain Couillet:
Large-Dimensional Behavior of Regularized Maronna's M-Estimators of Covariance Matrices. IEEE Trans. Signal Process. 66(13): 3529-3542 (2018) - [j36]Apostolos Karadimitrakis, Aris L. Moustakas, Romain Couillet:
Gallager Bound for MIMO Channels: Large- $N$ Asymptotics. IEEE Trans. Wirel. Commun. 17(2): 1323-1330 (2018) - [c66]Xiaoyi Mai, Romain Couillet:
Semi-Supervised Spectral Clustering. ACSSC 2018: 2012-2016 - [c65]Romain Couillet, Zhenyu Liao, Xiaoyi Mai:
Classification Asymptotics in the Random Matrix Regime. EUSIPCO 2018: 1875-1879 - [c64]Hafiz Tiomoko Ali, Abla Kammoun, Romain Couillet:
Random Matrix Asymptotics of Inner Product Kernel Spectral Clustering. ICASSP 2018: 2441-2445 - [c63]Cosme Louart, Romain Couillet:
A Random Matrix and Concentration Inequalities Framework for Neural Networks Analysis. ICASSP 2018: 4214-4218 - [c62]Zhenyu Liao, Romain Couillet:
On the Spectrum of Random Features Maps of High Dimensional Data. ICML 2018: 3069-3077 - [c61]Zhenyu Liao, Romain Couillet:
The Dynamics of Learning: A Random Matrix Approach. ICML 2018: 3078-3087 - [c60]Hafiz Tiomoko Ali, Abla Kammoun, Romain Couillet:
Random Matrix-Improved Kernels For Large Dimensional Spectral Clustering. SSP 2018: 453-457 - [c59]Liusha Yang, Matthew R. McKay, Romain Couillet:
Random Matrix-Optimized High-Dimensional MVDR Beamforming. SSP 2018: 473-477 - [i35]Zhenyu Liao, Romain Couillet:
On the Spectrum of Random Features Maps of High Dimensional Data. CoRR abs/1805.11916 (2018) - [i34]Zhenyu Liao, Romain Couillet:
The Dynamics of Learning: A Random Matrix Approach. CoRR abs/1805.11917 (2018) - [i33]Hafiz Tiomoko Ali, Sijia Liu, Yasin Yilmaz, Alfred O. Hero III, Romain Couillet, Indika Rajapakse:
Latent heterogeneous multilayer community detection. CoRR abs/1806.07963 (2018) - [i32]Romain Couillet, Malik Tiomoko, Steeve Zozor, Eric Moisan:
Random matrix-improved estimation of covariance matrix distances. CoRR abs/1810.04534 (2018) - [i31]Yacine Chitour, Zhenyu Liao, Romain Couillet:
A Geometric Approach of Gradient Descent Algorithms in Neural Networks. CoRR abs/1811.03568 (2018) - 2017
- [j35]Hafiz Tiomoko Ali, Romain Couillet:
Improved spectral community detection in large heterogeneous networks. J. Mach. Learn. Res. 18: 225:1-225:49 (2017) - [j34]Meysam Sadeghi, Luca Sanguinetti, Romain Couillet, Chau Yuen:
Large System Analysis of Power Normalization Techniques in Massive MIMO. IEEE Trans. Veh. Technol. 66(10): 9005-9017 (2017) - [j33]Meysam Sadeghi, Luca Sanguinetti, Romain Couillet, Chau Yuen:
Reducing the Computational Complexity of Multicasting in Large-Scale Antenna Systems. IEEE Trans. Wirel. Commun. 16(5): 2963-2975 (2017) - [c58]Cosme Louart, Romain Couillet:
Harnessing neural networks: A random matrix approach. ICASSP 2017: 2282-2286 - [c57]Zhenyu Liao, Romain Couillet:
Random matrices meet machine learning: A large dimensional analysis of LS-SVM. ICASSP 2017: 2397-2401 - [c56]Xiaoyi Mai, Romain Couillet:
The counterintuitive mechanism of graph-based semi-supervised learning in the big data regime. ICASSP 2017: 2821-2825 - [c55]Khalil Elkhalil, Abla Kammoun, Romain Couillet, Tareq Y. Al-Naffouri, Mohamed-Slim Alouini:
Asymptotic performance of regularized quadratic discriminant analysis based classifiers. MLSP 2017: 1-6 - [c54]Apostolos Karadimitrakis, Romain Couillet, Aris L. Moustakas, Luca Sanguinetti:
The Gallager Bound in Fiber Optical MIMO. WSA 2017: 1-8 - [i30]Cosme Louart, Zhenyu Liao, Romain Couillet:
A Random Matrix Approach to Neural Networks. CoRR abs/1702.05419 (2017) - [i29]Meysam Sadeghi, Luca Sanguinetti, Romain Couillet, Chau Yuen:
Reducing the Computational Complexity of Multicasting in Large-Scale Antenna Systems. CoRR abs/1702.05901 (2017) - [i28]Meysam Sadeghi, Luca Sanguinetti, Romain Couillet, Chau Yuen:
Large System Analysis of Power Normalization Techniques in Massive MIMO. CoRR abs/1705.07183 (2017) - [i27]Xiaoyi Mai, Romain Couillet:
A random matrix analysis and improvement of semi-supervised learning for large dimensional data. CoRR abs/1711.03404 (2017) - [i26]Apostolos Karadimitrakis, Aris L. Moustakas, Romain Couillet:
Gallager Bound for MIMO Channels: Large-N Asymptotics. CoRR abs/1711.08632 (2017) - 2016
- [j32]Romain Couillet, Gilles Wainrib, Harry Sevi, Hafiz Tiomoko Ali:
The Asymptotic Performance of Linear Echo State Neural Networks. J. Mach. Learn. Res. 17: 178:1-178:35 (2016) - [j31]Romain Couillet, Abla Kammoun, Frédéric Pascal:
Second order statistics of robust estimators of scatter. Application to GLRT detection for elliptical signals. J. Multivar. Anal. 143: 249-274 (2016) - [j30]Romain Couillet:
Introduction. Traitement du Signal 33(2-3): 159-160 (2016) - [j29]Romain Couillet:
Estimation robuste et matrices aléatoires. Traitement du Signal 33(2-3): 273-320 (2016) - [j28]Romain Couillet, Gilles Wainrib:
Perspectives en matrices aléatoires et grands réseaux. Traitement du Signal 33(2-3): 351-376 (2016) - [j27]Azary Abboud, Franck Iutzeler, Romain Couillet, Mérouane Debbah, Houria Siguerdidjane:
Distributed Production-Sharing Optimization and Application to Power Grid Networks. IEEE Trans. Signal Inf. Process. over Networks 2(1): 16-28 (2016) - [j26]Abla Kammoun, Romain Couillet, Frédéric Pascal, Mohamed-Slim Alouini:
Convergence and Fluctuations of Regularized Tyler Estimators. IEEE Trans. Signal Process. 64(4): 1048-1060 (2016) - [j25]Rémy Boyer, Romain Couillet, Bernard Henri Fleury, Pascal Larzabal:
Large-System Estimation Performance in Noisy Compressed Sensing With Random Support of Known Cardinality - A Bayesian Analysis. IEEE Trans. Signal Process. 64(21): 5525-5535 (2016) - [j24]Luca Sanguinetti, Romain Couillet, Mérouane Debbah:
Large System Analysis of Base Station Cooperation for Power Minimization. IEEE Trans. Wirel. Commun. 15(8): 5480-5496 (2016) - [c53]Romain Couillet, Abla Kammoun:
Random matrix improved subspace clustering. ACSSC 2016: 90-94 - [c52]Hafiz Tiomoko Ali, Romain Couillet:
Random matrix improved community detection in heterogeneous networks. ACSSC 2016: 1385-1389 - [c51]Hafiz Tiomoko Ali, Romain Couillet:
Performance analysis of spectral community detection in realistic graph models. ICASSP 2016: 4548-4552 - [c50]Romain Couillet, Gilles Wainrib, Hafiz Tiomoko Ali, Harry Sevi:
A Random Matrix Approach to Echo-State Neural Networks. ICML 2016: 517-525 - [c49]Nicolas Auguin, David Morales-Jiménez, Matthew R. McKay, Romain Couillet:
Robust shrinkage M-estimators of large covariance matrices. SSP 2016: 1-4 - [c48]Romain Couillet, Gilles Wainrib, Harry Sevi, Hafiz Tiomoko Ali:
Training performance of echo state neural networks. SSP 2016: 1-4 - [c47]Abla Kammoun, Romain Couillet, Frédéric Pascal, Mohamed-Slim Alouini:
Optimal adaptive normalized matched filter for large antenna arrays. SSP 2016: 1-5 - [i25]Romain Couillet, Gilles Wainrib, Harry Sevi, Hafiz Tiomoko Ali:
The Asymptotic Performance of Linear Echo State Neural Networks. CoRR abs/1603.07866 (2016) - 2015
- [j23]Romain Couillet, Frédéric Pascal, Jack W. Silverstein:
The random matrix regime of Maronna's M-estimator with elliptically distributed samples. J. Multivar. Anal. 139: 56-78 (2015) - [j22]Romain Couillet:
Robust spiked random matrices and a robust G-MUSIC estimator. J. Multivar. Anal. 140: 139-161 (2015) - [j21]Yacine Chitour, Romain Couillet, Frédéric Pascal:
On the Convergence of Maronna's M-Estimators of Scatter. IEEE Signal Process. Lett. 22(6): 709-712 (2015) - [j20]Jakob Hoydis, Romain Couillet, Pablo Piantanida:
The Second-Order Coding Rate of the MIMO Quasi-Static Rayleigh Fading Channel. IEEE Trans. Inf. Theory 61(12): 6591-6622 (2015) - [j19]Axel Müller, Romain Couillet, Emil Björnson, Sebastian Wagner, Mérouane Debbah:
Interference-Aware RZF Precoding for Multicell Downlink Systems. IEEE Trans. Signal Process. 63(15): 3959-3973 (2015) - [j18]Julia Vinogradova, Romain Couillet, Walid Hachem:
Estimation of Toeplitz Covariance Matrices in Large Dimensional Regime With Application to Source Detection. IEEE Trans. Signal Process. 63(18): 4903-4913 (2015) - [j17]David Morales-Jiménez, Romain Couillet, Matthew R. McKay:
Large Dimensional Analysis of Robust M-Estimators of Covariance With Outliers. IEEE Trans. Signal Process. 63(21): 5784-5797 (2015) - [j16]Liusha Yang, Romain Couillet, Matthew R. McKay:
A Robust Statistics Approach to Minimum Variance Portfolio Optimization. IEEE Trans. Signal Process. 63(24): 6684-6697 (2015) - [c46]Liusha Yang, Romain Couillet, Matthew R. McKay:
Minimum variance portfolio optimization in the spiked covariance model. CAMSAP 2015: 13-16 - [c45]Romain Couillet, Florent Benaych-Georges:
Understanding big data spectral clustering. CAMSAP 2015: 29-32 - [c44]Romain Couillet, Maria Sabrina Greco, Jean Philippe Ovarlez, Frédéric Pascal:
RMT for whitening space correlation and applications to radar detection. CAMSAP 2015: 149-152 - [c43]Luca Sanguinetti, Romain Couillet, Mérouane Debbah:
Base Station Cooperation for Power Minimization in the Downlink: Large System Analysis. GLOBECOM 2015: 1-6 - [c42]Abla Kammoun, Romain Couillet, Frédéric Pascal:
Second order statistics of bilinear forms of robust scatter estimators. ICASSP 2015: 3412-3416 - [c41]David Morales-Jiménez, Romain Couillet, Matthew R. McKay:
Large dimensional analysis of Maronna's M-estimator with outliers. ICASSP 2015: 3417-3421 - [c40]