Tuning-Free Maximum Likelihood Training of Latent Variable Models via Coin Betting

arXiv:2305.14916v2 Announce Type: replace-cross
Abstract: We introduce two new particle-based algorithms for learning latent variable models via marginal maximum likelihood estimation, including one which is entirely tuning-free. Our methods are based on the perspective of marginal maximum likelihood estimation as an optimization problem: namely, as the minimization of a free energy functional. One way to solve this problem is via the discretization of a gradient flow associated with the free energy. We study one such approach, which resembles an extension …

abstract algorithms arxiv cs.lg free likelihood maximum likelihood estimation optimization particle perspective stat.me stat.ml training type via