2 citations · 2 across the 1 of their papers we have counts for
3 papers
cs.LG2026★ 2 cited
Cooperative Variance Estimation and Bayesian Neural Networks for Disentangling Aleatoric and Epistemic Uncertainties
Jiaxiang Yi, Miguel A. Bessa
Real-world data contains aleatoric uncertainty - irreducible noise arising from imperfect measurements or from incomplete knowledge about the data generation process. Mean-variance…
cs.LG2025
Single- to multi-fidelity history-dependent learning with uncertainty quantification and disentanglement: application to data-driven constitutive modeling
Jiaxiang Yi, Bernardo P. Ferreira, Miguel A. Bessa
Data-driven learning is generalized to consider history-dependent multi-fidelity data, while quantifying epistemic uncertainty and disentangling it from data noise (aleatoric uncer…
cs.LG2025
Practical multi-fidelity machine learning: fusion of deterministic and Bayesian models
Jiaxiang Yi, Ji Cheng, Miguel A. Bessa
Multi-fidelity machine learning methods address the accuracy-efficiency trade-off by integrating scarce, resource-intensive high-fidelity data with abundant but less accurate low-f…