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Variational inference (VI) combined with Bayesian nonlinear filteringproduces state-of-the-art results for latent time-series modeling. A body ofrecent work has focused on sequential Monte Carlo (SMC) and its variants, e.g.,forward filtering backward simulation (FFBSi). Although these studies havesucceeded, serious problems remain in particle degeneracy and biased gradientestimators. In this paper, we propose Ensemble Kalman Variational Objective(EnKO), a hybrid method of VI and the ensemble Kalman filter (EnKF), to inferstate space models (SSMs). Our proposed method can efficiently identify latentdynamics because of its particle diversity and unbiased gradient estimators. Wedemonstrate that our EnKO outperforms SMC-based methods in terms of predictiveability and particle efficiency for three benchmark nonlinear systemidentification tasks.

Ensemble Kalman Variational Objectives: Nonlinear Latent Trajectory Inference with A Hybrid of Variational Inference and Ensemble Kalman Filter