Tag: arbitrary

Optimization and Regularization Under Arbitrary Objectives arXiv:2511.19628v1 Announce Type: new Abstract: This study investigates the limitations of applying Markov Chain Monte Carlo (MCMC) methods to arbitrary objective functions, focusing on a two-block MCMC framework which alternates between Metropolis-Hastings and Gibbs sampling. While such approaches are often considered advantageous for enabling data-driven regularization, we show that…