Active Learning based Structural Inference

Aoran Wang, Jun Pang
Proceedings of the 40th International Conference on Machine Learning, PMLR 202:36224-36245, 2023.

Abstract

In this paper, we propose a novel framework, Active Learning based Structural Inference (ALaSI), to infer the existence of directed connections from observed agents’ states over a time period in a dynamical system. With the help of deep active learning, ALaSI is competent in learning the representation of connections with a relatively small pool of prior knowledge. Moreover, based on information theory, the proposed inter- and out-of-scope message learning pipelines are remarkably beneficial to structural inference for large dynamical systems. We evaluate ALaSI on various large datasets including simulated systems and real-world networks, to demonstrate that ALaSI is able to outperform previous methods in precisely inferring the existence of connections in large systems under either supervised learning or unsupervised learning.

Cite this Paper

BibTeX
@InProceedings{pmlr-v202-wang23ac, title = {Active Learning based Structural Inference}, author = {Wang, Aoran and Pang, Jun}, booktitle = {Proceedings of the 40th International Conference on Machine Learning}, pages = {36224--36245}, year = {2023}, editor = {Krause, Andreas and Brunskill, Emma and Cho, Kyunghyun and Engelhardt, Barbara and Sabato, Sivan and Scarlett, Jonathan}, volume = {202}, series = {Proceedings of Machine Learning Research}, month = {23--29 Jul}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v202/wang23ac/wang23ac.pdf}, url = {https://proceedings.mlr.press/v202/wang23ac.html}, abstract = {In this paper, we propose a novel framework, Active Learning based Structural Inference (ALaSI), to infer the existence of directed connections from observed agents’ states over a time period in a dynamical system. With the help of deep active learning, ALaSI is competent in learning the representation of connections with a relatively small pool of prior knowledge. Moreover, based on information theory, the proposed inter- and out-of-scope message learning pipelines are remarkably beneficial to structural inference for large dynamical systems. We evaluate ALaSI on various large datasets including simulated systems and real-world networks, to demonstrate that ALaSI is able to outperform previous methods in precisely inferring the existence of connections in large systems under either supervised learning or unsupervised learning.} }
Endnote
%0 Conference Paper %T Active Learning based Structural Inference %A Aoran Wang %A Jun Pang %B Proceedings of the 40th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2023 %E Andreas Krause %E Emma Brunskill %E Kyunghyun Cho %E Barbara Engelhardt %E Sivan Sabato %E Jonathan Scarlett %F pmlr-v202-wang23ac %I PMLR %P 36224--36245 %U https://proceedings.mlr.press/v202/wang23ac.html %V 202 %X In this paper, we propose a novel framework, Active Learning based Structural Inference (ALaSI), to infer the existence of directed connections from observed agents’ states over a time period in a dynamical system. With the help of deep active learning, ALaSI is competent in learning the representation of connections with a relatively small pool of prior knowledge. Moreover, based on information theory, the proposed inter- and out-of-scope message learning pipelines are remarkably beneficial to structural inference for large dynamical systems. We evaluate ALaSI on various large datasets including simulated systems and real-world networks, to demonstrate that ALaSI is able to outperform previous methods in precisely inferring the existence of connections in large systems under either supervised learning or unsupervised learning.
APA
Wang, A. & Pang, J.. (2023). Active Learning based Structural Inference. Proceedings of the 40th International Conference on Machine Learning, in Proceedings of Machine Learning Research 202:36224-36245 Available from https://proceedings.mlr.press/v202/wang23ac.html.

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