6 papers
Tensor Network Kernel Machines: A JAX Framework for Machine Learning and Nonlinear System Identification
Albert Saiapin, Kim Batselier
Developing nonlinear models that are both expressive and computationally efficient remains a challenge in machine learning and nonlinear system identification. Tensor network kerne…
Laplace Approximation for Bayesian Tensor Network Kernel Machines
Albert Saiapin, Kim Batselier
Uncertainty estimation is essential for robust decision-making in the presence of ambiguous or out-of-distribution inputs. Gaussian Processes (GPs) are classical kernel-based model…
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…
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…
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…
A Kernelizable Primal-Dual Formulation of the Multilinear Singular Value Decomposition
Frederiek Wesel, Kim Batselier
The ability to express a learning task in terms of a primal and a dual optimization problem lies at the core of a plethora of machine learning methods. For example, Support Vector…