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Conference Papers Year : 2022

Robust Kernel Density Estimation with Median-of-Means principle

Abstract

In this paper, we introduce a robust nonparametric density estimator combining the popular Kernel Density Estimation method and the Median-of-Means principle (MoM-KDE). This estimator is shown to achieve robustness to any kind of anomalous data, even in the case of adversarial contamination. In particular, while previous works only prove consistency results under known contamination model, this work provides finite-sample high-probability error-bounds without a priori knowledge on the outliers. Finally, when compared with other robust kernel estimators, we show that MoM-KDE achieves competitive results while having significant lower computational complexity.
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Dates and versions

hal-02882092 , version 1 (29-06-2020)

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Pierre Humbert, Batiste Le Bars, Ludovic Minvielle. Robust Kernel Density Estimation with Median-of-Means principle. ICML 2022 - The 39th International Conference on Machine Learning (ICML), Jul 2022, Baltimore, United States. ⟨hal-02882092⟩
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