Two-Timescale Stochastic EM Algorithms

Belhal Karimi (1) , Ping Li (1)

1 Baidu Research

The Expectation-Maximization (EM) algorithm is a popular choice for learning latent variable models. Variants of the EM have been initially introduced by Neal and Hinton (1998), using in-cremental updates to scale to large datasets, and by Wei and Tanner (1990); Delyon et al. (1999), using Monte Carlo (MC) approximations to bypass the intractable conditional expectation of the latent data for most nonconvex models. In this paper, we propose a general class of methods called Two-Timescale EM Methods based on a two-stage approach of stochastic updates to tackle an essential nonconvex optimization task for latent variable models. We motivate the choice of a double dynamic by invoking the variance reduction virtue of each stage of the method on both sources of noise: the index sampling for the incremental update and the MC approximation. We establish finite-time and global convergence bounds for nonconvex objective functions. Numerical applications on various models such as deformable template for image analysis or nonlinear mixed-effects models for pharmacokinetics are also presented to illustrate our findings.

Mots clés: twotimescale; stochastic; EM; sampling; MCMC; Monte Carlo

Domaines: Statistiques [math.ST]

Belhal Karimi, Ping Li. Two-Timescale Stochastic EM Algorithms. 2020. ⟨hal-02994707⟩