Learning to Filter with Predictive State Inference Machines

Wen Sun, Arun Venkatraman, Byron Boots, J.Andrew Bagnell
Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1197-1205, 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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v48-sun16, title = {Learning to Filter with Predictive State Inference Machines}, author = {Sun, Wen and Venkatraman, Arun and Boots, Byron and Bagnell, J.Andrew}, booktitle = {Proceedings of The 33rd International Conference on Machine Learning}, pages = {1197--1205}, year = {2016}, editor = {Balcan, Maria Florina and Weinberger, Kilian Q.}, volume = {48}, series = {Proceedings of Machine Learning Research}, address = {New York, New York, USA}, month = {20--22 Jun}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v48/sun16.pdf}, url = {https://proceedings.mlr.press/v48/sun16.html}, 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.} }
Endnote
%0 Conference Paper %T Learning to Filter with Predictive State Inference Machines %A Wen Sun %A Arun Venkatraman %A Byron Boots %A J.Andrew Bagnell %B Proceedings of The 33rd International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2016 %E Maria Florina Balcan %E Kilian Q. Weinberger %F pmlr-v48-sun16 %I PMLR %P 1197--1205 %U https://proceedings.mlr.press/v48/sun16.html %V 48 %X 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.
RIS
TY - CPAPER TI - Learning to Filter with Predictive State Inference Machines AU - Wen Sun AU - Arun Venkatraman AU - Byron Boots AU - J.Andrew Bagnell BT - Proceedings of The 33rd International Conference on Machine Learning DA - 2016/06/11 ED - Maria Florina Balcan ED - Kilian Q. Weinberger ID - pmlr-v48-sun16 PB - PMLR DP - Proceedings of Machine Learning Research VL - 48 SP - 1197 EP - 1205 L1 - http://proceedings.mlr.press/v48/sun16.pdf UR - https://proceedings.mlr.press/v48/sun16.html AB - 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. ER -
APA
Sun, W., Venkatraman, A., Boots, B. & Bagnell, J.. (2016). Learning to Filter with Predictive State Inference Machines. Proceedings of The 33rd International Conference on Machine Learning, in Proceedings of Machine Learning Research 48:1197-1205 Available from https://proceedings.mlr.press/v48/sun16.html.

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