Statistical Learning
On the Asymptotic Learning Curves of Kernel Ridge Regression under Power-law Decay
The widely observed'benign overfitting phenomenon' in the neural network literature raises the challenge to the'bias-variance trade-off' doctrine in the statistical learning theory. Since the generalization ability of the'lazy trained' over-parametrized neural network can be well approximated by that of the neural tangent
Kernel Stein Discrepancy thinning: a theoretical perspective of pathologies and a practical fix with regularization Clรฉment Bรฉnard 1 Brian Staber 1 Sรฉbastien Da Veiga 2 1
Stein thinning is a promising algorithm proposed by Riabiz et al. [2022] for post-processing outputs of Markov chain Monte Carlo (MCMC). The main principle is to greedily minimize the kernelized Stein discrepancy (KSD), which only requires the gradient of the log-target distribution, and is thus well-suited for Bayesian inference.
