Towards Robust Classification with Deep Generative Forests

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Abstract

Decision Trees and Random Forests are among the most widely used machine learning models, and often achieve state-of-the-art performance in tabular, domain-agnostic datasets. Nonetheless, being primarily discriminative models they lack principled methods to manipulate the uncertainty of predictions. In this paper, we exploit Generative Forests (GeFs), a recent class of deep probabilistic models that addresses these issues by extending Random Forests to generative models representing the full joint distribution over the feature space. We demonstrate that GeFs are uncertainty-aware classifiers, capable of measuring the robustness of each prediction as well as detecting out-of-distribution samples.
Original languageEnglish
Title of host publicationICML 2020 Workshop on Uncertainty and Robustness in Deep Learning
Publication statusPublished - 11 Jul 2020
EventInternational Conference on Machine Learning -
Duration: 12 Jul 202018 Jul 2020
Conference number: 37

Conference

ConferenceInternational Conference on Machine Learning
Abbreviated titleICML
Period12/07/2018/07/20

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  • Cite this

    Correia, A. H. C., Peharz, R., & Campos, C. D. (2020). Towards Robust Classification with Deep Generative Forests. In ICML 2020 Workshop on Uncertainty and Robustness in Deep Learning