18 citations · 24 across the 8 of their papers we have counts for
5 papers · 1 filter
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…
Fast Temporal Wavelet Graph Neural Networks
Duc Thien Nguyen, Manh Duc Tuan Nguyen, Truong Son Hy +1
Spatio-temporal signals forecasting plays an important role in numerous domains, especially in neuroscience and transportation. The task is challenging due to the highly intricate…
Neural-Progressive Hedging: Enforcing Constraints in Reinforcement Learning with Stochastic Programming
Supriyo Ghosh, Laura Wynter, Shiau Hong Lim +1
We propose a framework, called neural-progressive hedging (NP), that leverages stochastic programming during the online phase of executing a reinforcement learning (RL) policy. The…