Statistics > Machine Learning
[Submitted on 17 Jan 2023 (v1), last revised 24 Mar 2023 (this version, v2)]
Title:Deep Conditional Measure Quantization
View PDFAbstract:Quantization of a probability measure means representing it with a finite set of Dirac masses that approximates the input distribution well enough (in some metric space of probability measures). Various methods exists to do so, but the situation of quantizing a conditional law has been less explored. We propose a method, called DCMQ, involving a Huber-energy kernel-based approach coupled with a deep neural network architecture. The method is tested on several examples and obtains promising results.
Submission history
From: Gabriel Turinici [view email][v1] Tue, 17 Jan 2023 14:18:17 UTC (98 KB)
[v2] Fri, 24 Mar 2023 17:47:43 UTC (364 KB)
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