Ensemble Kalman Variational Objectives: Nonlinear Latent Trajectory Inference with A Hybrid of Variational Inference and Ensemble Kalman Filter
This work addresses a specific bottleneck in state space model inference for researchers in machine learning and statistics, offering an incremental improvement over existing methods.
The paper tackled the problems of particle degeneracy and biased gradient estimators in variational inference for latent time-series modeling by proposing Ensemble Kalman Variational Objective (EnKO), a hybrid of variational inference and ensemble Kalman filter, which outperformed SMC-based methods in predictive ability and particle efficiency on three benchmark nonlinear system identification tasks.
Variational inference (VI) combined with Bayesian nonlinear filtering produces state-of-the-art results for latent time-series modeling. A body of recent work has focused on sequential Monte Carlo (SMC) and its variants, e.g., forward filtering backward simulation (FFBSi). Although these studies have succeeded, serious problems remain in particle degeneracy and biased gradient estimators. In this paper, we propose Ensemble Kalman Variational Objective (EnKO), a hybrid method of VI and the ensemble Kalman filter (EnKF), to infer state space models (SSMs). Our proposed method can efficiently identify latent dynamics because of its particle diversity and unbiased gradient estimators. We demonstrate that our EnKO outperforms SMC-based methods in terms of predictive ability and particle efficiency for three benchmark nonlinear system identification tasks.