Fast Non-Parametric Conditional Density Estimation using Moment Trees

Hinder F, Vaquet V, Brinkrolf J, Hammer B (2021)
In: 2021 IEEE Symposium Series on Computational Intelligence (SSCI) Proceedings. Piscataway, NJ: IEEE: 1-7.

Konferenzbeitrag | Veröffentlicht | Englisch
 
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Abstract / Bemerkung
In many machine learning tasks, one tries to infer unknown quantities such as the conditional density p(Y | X) from observed ones X. Conditional density estimation (CDE) constitutes a challenging problem due to the trade-off between model complexity, distribution complexity, and overfitting. In case of online learning, where the distribution may change over time (concept drift) or only few data points are available at once, robust, non-parametric approaches are of particular interest. In this paper we present a new, non-parametric tree-ensemble-based method for CDE that reduces the problem to a simple regression task on the transformed input data and a (unconditional) density estimation. We prove the correctness of our approach and show its usefulness in empirical evaluation on standard benchmarks. We show that our method is comparable to other state-of-the-art methods, but is much faster and more robust.
Erscheinungsjahr
2021
Titel des Konferenzbandes
2021 IEEE Symposium Series on Computational Intelligence (SSCI) Proceedings
Seite(n)
1-7
Konferenz
2021 IEEE Symposium Series on Computational Intelligence (SSCI)
Konferenzort
Orlando, FL, USA
Konferenzdatum
2021-12-05 – 2021-12-07
eISBN
978-1-7281-9048-8
Page URI
https://pub.uni-bielefeld.de/record/2960755

Zitieren

Hinder F, Vaquet V, Brinkrolf J, Hammer B. Fast Non-Parametric Conditional Density Estimation using Moment Trees. In: 2021 IEEE Symposium Series on Computational Intelligence (SSCI) Proceedings. Piscataway, NJ: IEEE; 2021: 1-7.
Hinder, F., Vaquet, V., Brinkrolf, J., & Hammer, B. (2021). Fast Non-Parametric Conditional Density Estimation using Moment Trees. 2021 IEEE Symposium Series on Computational Intelligence (SSCI) Proceedings, 1-7. Piscataway, NJ: IEEE. https://doi.org/10.1109/SSCI50451.2021.9660031
Hinder, F., Vaquet, V., Brinkrolf, J., and Hammer, B. (2021). “Fast Non-Parametric Conditional Density Estimation using Moment Trees” in 2021 IEEE Symposium Series on Computational Intelligence (SSCI) Proceedings (Piscataway, NJ: IEEE), 1-7.
Hinder, F., et al., 2021. Fast Non-Parametric Conditional Density Estimation using Moment Trees. In 2021 IEEE Symposium Series on Computational Intelligence (SSCI) Proceedings. Piscataway, NJ: IEEE, pp. 1-7.
F. Hinder, et al., “Fast Non-Parametric Conditional Density Estimation using Moment Trees”, 2021 IEEE Symposium Series on Computational Intelligence (SSCI) Proceedings, Piscataway, NJ: IEEE, 2021, pp.1-7.
Hinder, F., Vaquet, V., Brinkrolf, J., Hammer, B.: Fast Non-Parametric Conditional Density Estimation using Moment Trees. 2021 IEEE Symposium Series on Computational Intelligence (SSCI) Proceedings. p. 1-7. IEEE, Piscataway, NJ (2021).
Hinder, Fabian, Vaquet, Valerie, Brinkrolf, Johannes, and Hammer, Barbara. “Fast Non-Parametric Conditional Density Estimation using Moment Trees”. 2021 IEEE Symposium Series on Computational Intelligence (SSCI) Proceedings. Piscataway, NJ: IEEE, 2021. 1-7.

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