SIXO: Smoothing Inference with Twisted Objectives
This work addresses a fundamental bottleneck in probabilistic inference for researchers and practitioners in machine learning, offering a novel approach to improve SMC performance, though it is incremental in building upon existing SMC frameworks.
The paper tackled the problem of Sequential Monte Carlo (SMC) inference in state space models, which often uses filtering distributions that ignore future observations, leading to limitations. The result was SIXO, a method that learns smoothing distributions using density ratio estimation, yielding provably tighter log marginal lower bounds and significantly more accurate posterior inferences and parameter estimates across various domains.
Sequential Monte Carlo (SMC) is an inference algorithm for state space models that approximates the posterior by sampling from a sequence of target distributions. The target distributions are often chosen to be the filtering distributions, but these ignore information from future observations, leading to practical and theoretical limitations in inference and model learning. We introduce SIXO, a method that instead learns targets that approximate the smoothing distributions, incorporating information from all observations. The key idea is to use density ratio estimation to fit functions that warp the filtering distributions into the smoothing distributions. We then use SMC with these learned targets to define a variational objective for model and proposal learning. SIXO yields provably tighter log marginal lower bounds and offers significantly more accurate posterior inferences and parameter estimates in a variety of domains.