137 citations · 218 across the 12 of their papers we have counts for
12 papers
The Ideal Continual Learner: An Agent That Never Forgets
Liangzu Peng, Paris V. Giampouras, René Vidal
The goal of continual learning is to find a model that solves multiple learning tasks which are presented sequentially to the learner. A key challenge in this setting is that the l…
A Linearly Convergent GAN Inversion-based Algorithm for Reverse Engineering of Deceptions
Darshan Thaker, Paris Giampouras, René Vidal
An important aspect of developing reliable deep learning systems is devising strategies that make these systems robust to adversarial attacks. There is a long line of work that foc…
Reverse Engineering attacks: A block-sparse optimization approach with recovery guarantees
Darshan Thaker, Paris Giampouras, René Vidal
Deep neural network-based classifiers have been shown to be vulnerable to imperceptible perturbations to their input, such as -bounded norm adversarial attacks. This has mo…
Implicit Bias of Projected Subgradient Method Gives Provable Robust Recovery of Subspaces of Unknown Codimension
Paris V. Giampouras, Benjamin D. Haeffele, René Vidal
Robust subspace recovery (RSR) is a fundamental problem in robust representation learning. Here we focus on a recently proposed RSR method termed Dual Principal Component Pursuit (…
Online Rank-Revealing Block-Term Tensor Decomposition
Athanasios A. Rontogiannis, Eleftherios Kofidis, Paris V. Giampouras
The so-called block-term decomposition (BTD) tensor model, especially in its rank- version, has been recently receiving increasing attention due to its enhanced abilit…
Block-Term Tensor Decomposition Model Selection and Computation: The Bayesian Way
Paris V. Giampouras, Athanasios A. Rontogiannis, Eleftherios Kofidis
The so-called block-term decomposition (BTD) tensor model, especially in its rank- version, has been recently receiving increasing attention due to its enhanced abilit…
A novel variational form of the Schatten- quasi-norm
Paris Giampouras, René Vidal, Athanasios Rontogiannis +1
The Schatten- quasi-norm with has recently gained considerable attention in various low-rank matrix estimation problems offering significant benefits over relevant c…
Block-Term Tensor Decomposition: Model Selection and Computation
Athanasios A. Rontogiannis, Eleftherios Kofidis, Paris V. Giampouras
The so-called block-term decomposition (BTD) tensor model has been recently receiving increasing attention due to its enhanced ability of representing systems and signals that are…
Alternating Iteratively Reweighted Minimization Algorithms for Low-Rank Matrix Factorization
Paris V. Giampouras, Athanasios A. Rontogiannis, Konstantinos D. Koutroumbas
Nowadays, the availability of large-scale data in disparate application domains urges the deployment of sophisticated tools for extracting valuable knowledge out of this huge bulk…
Low-rank and Sparse NMF for Joint Endmembers' Number Estimation and Blind Unmixing of Hyperspectral Images
Paris V. Giampouras, Athanasios A. Rontogiannis, Konstantinos D. Koutroumbas
Estimation of the number of endmembers existing in a scene constitutes a critical task in the hyperspectral unmixing process. The accuracy of this estimate plays a crucial role in…
Online Low-Rank Subspace Learning from Incomplete Data: A Bayesian View
Paris V. Giampouras, Athanasios A. Rontogiannis, Konstantinos E. Themelis +1
Extracting the underlying low-dimensional space where high-dimensional signals often reside has long been at the center of numerous algorithms in the signal processing and machine…
Simultaneously sparse and low-rank abundance matrix estimation for hyperspectral image unmixing
Paris Giampouras, Konstantinos Themelis, Athanasios Rontogiannis +1
In a plethora of applications dealing with inverse problems, e.g. in image processing, social networks, compressive sensing, biological data processing etc., the signal of interest…