2 papers
stat.CO2024
Stochastic Approximation with Biased MCMC for Expectation Maximization
Samuel Gruffaz, Kyurae Kim, Alain Oliviero Durmus +1
The expectation maximization (EM) algorithm is a widespread method for empirical Bayesian inference, but its expectation step (E-step) is often intractable. Employing a stochastic…
cs.LG2023
Practical and Matching Gradient Variance Bounds for Black-Box Variational Bayesian Inference
Kyurae Kim, Kaiwen Wu, Jisu Oh +1
Understanding the gradient variance of black-box variational inference (BBVI) is a crucial step for establishing its convergence and developing algorithmic improvements. However, e…