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stat.ML2025
A Fully Probabilistic Tensor Network for Regularized Volterra System Identification
Afra Kilic, Kim Batselier
Modeling nonlinear systems with Volterra series is challenging because the number of kernel coefficients grows exponentially with the model order. This work introduces Bayesian Ten…
math.OC2025
Automatic Structure Identification for Highly Nonlinear MIMO Volterra Tensor Networks
Eva Memmel, Kim Batselier
The Volterra Tensor Network lifts the curse of dimensionality for truncated, discrete times Volterra models, enabling scalable representation of highly nonlinear system. This scala…
stat.ML2025
Interpretable Bayesian Tensor Network Kernel Machines with Automatic Rank and Feature Selection
Afra Kilic, Kim Batselier
Tensor Network (TN) Kernel Machines speed up model learning by representing parameters as low-rank TNs, reducing computation and memory use. However, most TN-based Kernel methods a…