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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence / EAAI-20

Granger-Causal Attentive Mixtures of Experts: Learning Important Features with Neural Networks

February 1, 2023

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Authors

Patrick Schwab

ETH Zurich


Djordje Miladinovic

ETH Zurich


Walter Karlen

ETH Zurich


DOI:

10.1609/aaai.v33i01.33014846


Abstract:

Knowledge of the importance of input features towards decisions made by machine-learning models is essential to increase our understanding of both the models and the underlying data. Here, we present a new approach to estimating feature importance with neural networks based on the idea of distributing the features of interest among experts in an attentive mixture of experts (AME). AMEs use attentive gating networks trained with a Granger-causal objective to learn to jointly produce accurate predictions as well as estimates of feature importance in a single model. Our experiments show (i) that the feature importance estimates provided by AMEs compare favourably to those provided by state-of-theart methods, (ii) that AMEs are significantly faster at estimating feature importance than existing methods, and (iii) that the associations discovered by AMEs are consistent with those reported by domain experts.

Topics: AAAI

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HOW TO CITE:

Patrick Schwab||Djordje Miladinovic||Walter Karlen Granger-Causal Attentive Mixtures of Experts: Learning Important Features with Neural Networks Proceedings of the AAAI Conference on Artificial Intelligence (2019) 4846-4853.

Patrick Schwab||Djordje Miladinovic||Walter Karlen Granger-Causal Attentive Mixtures of Experts: Learning Important Features with Neural Networks AAAI 2019, 4846-4853.

Patrick Schwab||Djordje Miladinovic||Walter Karlen (2019). Granger-Causal Attentive Mixtures of Experts: Learning Important Features with Neural Networks. Proceedings of the AAAI Conference on Artificial Intelligence, 4846-4853.

Patrick Schwab||Djordje Miladinovic||Walter Karlen. Granger-Causal Attentive Mixtures of Experts: Learning Important Features with Neural Networks. Proceedings of the AAAI Conference on Artificial Intelligence 2019 p.4846-4853.

Patrick Schwab||Djordje Miladinovic||Walter Karlen. 2019. Granger-Causal Attentive Mixtures of Experts: Learning Important Features with Neural Networks. "Proceedings of the AAAI Conference on Artificial Intelligence". 4846-4853.

Patrick Schwab||Djordje Miladinovic||Walter Karlen. (2019) "Granger-Causal Attentive Mixtures of Experts: Learning Important Features with Neural Networks", Proceedings of the AAAI Conference on Artificial Intelligence, p.4846-4853

Patrick Schwab||Djordje Miladinovic||Walter Karlen, "Granger-Causal Attentive Mixtures of Experts: Learning Important Features with Neural Networks", AAAI, p.4846-4853, 2019.

Patrick Schwab||Djordje Miladinovic||Walter Karlen. "Granger-Causal Attentive Mixtures of Experts: Learning Important Features with Neural Networks". Proceedings of the AAAI Conference on Artificial Intelligence, 2019, p.4846-4853.

Patrick Schwab||Djordje Miladinovic||Walter Karlen. "Granger-Causal Attentive Mixtures of Experts: Learning Important Features with Neural Networks". Proceedings of the AAAI Conference on Artificial Intelligence, (2019): 4846-4853.

Patrick Schwab||Djordje Miladinovic||Walter Karlen. Granger-Causal Attentive Mixtures of Experts: Learning Important Features with Neural Networks. AAAI[Internet]. 2019[cited 2023]; 4846-4853.


ISSN: 2374-3468


Published by AAAI Press, Palo Alto, California USA
Copyright 2022, Association for the Advancement of
Artificial Intelligence 1900 Embarcadero Road, Suite
101, Palo Alto, California 94303 All Rights Reserved

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