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Bayesian Tensor Network Kernel Machines use Laplace approximation for uncertainty estimation

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a new Bayesian Tensor Network Kernel Machine (LA-TNKM) that utilizes a linearized Laplace approximation for inference. This method addresses the challenge of providing uncertainty estimates in tensor network kernel machines, which typically break Gaussianity assumptions. Experiments indicate that LA-TNKM performs comparably to or better than Gaussian Processes and Bayesian Neural Networks on various regression tasks. AI

IMPACT Introduces a new method for uncertainty quantification in kernel machines, potentially improving robustness in AI decision-making.

RANK_REASON Academic paper introducing a novel method for uncertainty estimation in machine learning models.

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Bayesian Tensor Network Kernel Machines use Laplace approximation for uncertainty estimation

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Academic paper introducing a novel method for uncertainty estimation in machine learning models.
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COVERAGE [2]

  1. arXiv stat.ML TIER_1 English(EN) · Albert Saiapin, Kim Batselier ·

    Laplace Approximation for Bayesian Tensor Network Kernel Machines

    arXiv:2604.26673v1 Announce Type: new Abstract: 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 models that offer principled uncertainty quantification a…

  2. arXiv stat.ML TIER_1 English(EN) · Kim Batselier ·

    Laplace Approximation for Bayesian Tensor Network Kernel Machines

    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 models that offer principled uncertainty quantification and perform well on small- to medium-scale datase…

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