Learning to Filter with Predictive State Inference Machines - Robotics Institute Carnegie Mellon University

Learning to Filter with Predictive State Inference Machines

Conference Paper, Proceedings of (ICML) International Conference on Machine Learning, pp. 1197 - 1205, June, 2016

Abstract

Latent state space models are a fundamental and widely used tool for modeling dynamical systems. However, they are difficult to learn from data and learned models often lack performance guarantees on inference tasks such as filtering and prediction. In this work, we present the PREDICTIVE STATE INFERENCE MACHINE (PSIM), a data-driven method that considers the inference procedure on a dynamical system as a composition of predictors. The key idea is that rather than first learning a latent state space model, and then using the learned model for inference, PSIM directly learns predictors for inference in predictive state space. We provide theoretical guarantees for inference, in both realizable and agnostic settings, and showcase practical performance on a variety of simulated and real world robotics benchmarks.

BibTeX

@conference{Sun-2016-5539,
author = {Wen Sun and Arun Venkatraman and Byron Boots and J. Andrew (Drew) Bagnell},
title = {Learning to Filter with Predictive State Inference Machines},
booktitle = {Proceedings of (ICML) International Conference on Machine Learning},
year = {2016},
month = {June},
pages = {1197 - 1205},
}