تقرير
Online non-parametric likelihood-ratio estimation by Pearson-divergence functional minimization
| العنوان: | Online non-parametric likelihood-ratio estimation by Pearson-divergence functional minimization |
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| المؤلفون: | de la Concha, Alejandro, Vayatis, Nicolas, Kalogeratos, Argyris |
| سنة النشر: | 2023 |
| المجموعة: | Computer Science Statistics |
| مصطلحات موضوعية: | Statistics - Machine Learning, Computer Science - Machine Learning |
| الوصف: | Quantifying the difference between two probability density functions, $p$ and $q$, using available data, is a fundamental problem in Statistics and Machine Learning. A usual approach for addressing this problem is the likelihood-ratio estimation (LRE) between $p$ and $q$, which -- to our best knowledge -- has been investigated mainly for the offline case. This paper contributes by introducing a new framework for online non-parametric LRE (OLRE) for the setting where pairs of iid observations $(x_t \sim p, x'_t \sim q)$ are observed over time. The non-parametric nature of our approach has the advantage of being agnostic to the forms of $p$ and $q$. Moreover, we capitalize on the recent advances in Kernel Methods and functional minimization to develop an estimator that can be efficiently updated online. We provide theoretical guarantees for the performance of the OLRE method along with empirical validation in synthetic experiments. |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2311.01900 |
| رقم الأكسشن: | edsarx.2311.01900 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |