Distribution-Free Uncertainty Quantification for Kernel Methods by Gradient Perturbations

arXiv:1812.09632 · cs.LG, stat.ML · Submitted 2018-12-23 · Read on arXiv

cs.LG, stat.ML

Submitted: 2018-12-23

Updated: 2026-09-20

Comments: 18 figures

Journal ref: Machine Learning, Volume 108, Issue 8, 2019, pp. 1677-1699

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

Uncertainty Quantification — 18× in this paper · explained in 8 episodes
Objective Function — 12× in this paper · explained in 2 episodes
System Identification — 10× in this paper · explained in 3 episodes
Sign-Perturbed Sums — 7× in this paper
Kernel Ridge Regression — 6× in this paper
Hilbert Space — 5× in this paper · explained in 2 episodes
Loss Function — 5× in this paper · explained in 2 episodes
Regularization — 4× in this paper · explained in 3 episodes
Core — 3× in this paper · explained in 2 episodes
Gaussian Process — 3× in this paper
Generalization — 3× in this paper · explained in 13 episodes
Consistency — 2× in this paper · explained in 3 episodes
Exchangeability — 2× in this paper · explained in 2 episodes
Sparsity — 2× in this paper · explained in 4 episodes
Conformal Prediction — 1× in this paper · explained in 5 episodes
Data Peturbation — 1× in this paper
Reproducing Kernel Hilbert Space — 1× in this paper
Reproducing Kernel Hilbert Space (RKHS) — 1× in this paper · explained in 3 episodes