18 citations · 38 across the 10 of their papers we have counts for
10 papers
Kernel Regression with Tensor Trains and Hadamard Overparameterization
Duc Thien Nguyen, Konstantinos Slavakis, Eleftherios Kofidis +1
Kernel regression with tensor trains and Hadamard overparameterization (KReTTaH) is introduced as a training-data-free, interpretable, and nonparametric framework for multi-way dat…
Kernel Regression of Multi-Way Data via Tensor Trains with Hadamard Overparametrization: The Dynamic Graph Flow Case
Duc Thien Nguyen, Konstantinos Slavakis, Eleftherios Kofidis +1
A regression-based framework for interpretable multi-way data imputation, termed Kernel Regression via Tensor Trains with Hadamard overparametrization (KReTTaH), is introduced. KRe…
Model-Free Adversarial Purification via Coarse-To-Fine Tensor Network Representation
Guang Lin, Duc Thien Nguyen, Zerui Tao +3
Deep neural networks are known to be vulnerable to well-designed adversarial attacks. Although numerous defense strategies have been proposed, many are tailored to the specific att…
Imputation of Time-varying Edge Flows in Graphs by Multilinear Kernel Regression and Manifold Learning
Duc Thien Nguyen, Konstantinos Slavakis, Dimitris Pados
This paper extends the recently developed framework of multilinear kernel regression and imputation via manifold learning (MultiL-KRIM) to impute time-varying edge flows in a graph…
Multilinear Kernel Regression and Imputation via Manifold Learning
Duc Thien Nguyen, Konstantinos Slavakis
This paper introduces a novel nonparametric framework for data imputation, coined multilinear kernel regression and imputation via the manifold assumption (MultiL-KRIM). Motivated…
Multi-Linear Kernel Regression and Imputation in Data Manifolds
Duc Thien Nguyen, Konstantinos Slavakis
This paper introduces an efficient multi-linear nonparametric (kernel-based) approximation framework for data regression and imputation, and its application to dynamic magnetic-res…