Distribution-free uncertainty quantification for kernel methods by gradient perturbations

RT @StatMLPapers: Uncertainty Quantification for Kernel Methods. (arXiv:1812.09632v1 [cs.LG]) https://t.co/KqhIk52yDH

Uncertainty Quantification for Kernel Methods. (arXiv:1812.09632v1 [cs.LG]) https://t.co/0wwzQl0cul

#arXiv #machinelearning [cs.LG] Uncertainty Quantification for Kernel Methods. (arXiv:1812.09632v1 [cs.LG]) https://t.co/zZf260ulOS We propose a data-driven approach to quantify the uncertainty of models constructed by kernel methods. Our approach minimiz

"Uncertainty Quantification for Kernel Methods", Balázs Csanád Csáji, Krisztián Balázs Kis https://t.co/8IAan3PzKP

Uncertainty Quantification for Kernel Methods. https://t.co/RYB6WD0NWw

Uncertainty Quantification for Kernel Methods. (arXiv:1812.09632v1 [cs.LG]) https://t.co/KqhIk52yDH