Deep Conditional Measure Quantization
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 11 of its 42 references that are in the map. First posted on arXiv 17 Jan 2023.
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References · 11
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- 2022Nystr"om Kernel Mean Embeddingscited by 21
- 2022Huber-energy measure quantizationcited by 3
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