Particle Smoothing Variational Objectives
This work addresses the challenge of efficient and accurate smoothing in sequential Monte Carlo methods for researchers in probabilistic machine learning, though it is incremental as it builds on existing variational SMC frameworks.
The authors tackled the problem of improving variational inference for state-space models by introducing Particle Smoothing Variational Objectives (SVO), a novel backward simulation technique that enhances particle diversity and outperforms filtered objectives with fewer Monte Carlo samples on three nonlinear systems.
A body of recent work has focused on constructing a variational family of filtered distributions using Sequential Monte Carlo (SMC). Inspired by this work, we introduce Particle Smoothing Variational Objectives (SVO), a novel backward simulation technique and smoothed approximate posterior defined through a subsampling process. SVO augments support of the proposal and boosts particle diversity. Recent literature argues that increasing the number of samples K to obtain tighter variational bounds may hurt the proposal learning, due to a signal-to-noise ratio (SNR) of gradient estimators decreasing at the rate $\mathcal{O}(1/\sqrt{K})$. As a second contribution, we develop theoretical and empirical analysis of the SNR in filtering SMC, which motivates our choice of biased gradient estimators. We prove that introducing bias by dropping Categorical terms from the gradient estimate or using Gumbel-Softmax mitigates the adverse effect on the SNR. We apply SVO to three nonlinear latent dynamics tasks and provide statistics to rigorously quantify the predictions of filtered and smoothed objectives. SVO consistently outperforms filtered objectives when given fewer Monte Carlo samples on three nonlinear systems of increasing complexity.