4 citations · 7 across the 16 of their papers we have counts for
4 papers · 1 filter
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…
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…
Projecting basis functions with tensor networks for Gaussian process regression
Clara Menzen, Eva Memmel, Kim Batselier +1
This paper presents a method for approximate Gaussian process (GP) regression with tensor networks (TNs). A parametric approximation of a GP uses a linear combination of basis func…