Laplace Approximation for Bayesian Tensor Network Kernel Machines
A novel method brings principled uncertainty estimation to scalable tensor networks...
Uncertainty estimation is critical for AI systems operating in ambiguous or out-of-distribution scenarios. Gaussian Processes (GPs) are the gold standard for this, but they struggle with scalability on large datasets. Tensor network kernel machines offer a more scalable alternative by reformulating weight-space learning, but their tensor structure breaks Gaussianity, making probabilistic inference difficult.
To solve this, Albert Saiapin and Kim Batselier introduce the Laplace Approximation for Bayesian Tensor Network Kernel Machine (LA-TNKM). They apply a linearized Laplace approximation to recover principled uncertainty estimates. Across extensive UCI regression benchmarks, LA-TNKM consistently matches or outperforms both GPs and Bayesian Neural Networks, proving its effectiveness for real-world applications requiring robust predictions with confidence intervals.
- LA-TNKM uses a linearized Laplace approximation to enable Bayesian inference in tensor network kernel machines
- Matches or surpasses Gaussian Processes and Bayesian Neural Networks on UCI regression benchmarks
- Offers scalable uncertainty quantification for large datasets without sacrificing performance
Why It Matters
Brings reliable uncertainty estimates to scalable tensor network models, enhancing AI trustworthiness in critical applications.