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Adaptive generative moment matching networks for improved learning of dependence structures

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arXiv:2508. 21531v2 Announce Type: replace Abstract: An adaptive bandwidth selection procedure for the mixture kernel in the maximum mean discrepancy (MMD) for fitting generative moment matching networks (GMMNs) is introduced, and improved learning of copula random number generators is demonstrated.

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Authors: Marius Hofert, Gan Yao
Released: 2026-06-12 (2mo ago)
Topic: Related to current project
Relation to my work: Read next because Adaptive generative moment matching networks for improved learning of dependence structures overlaps with clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)", clean result "A pretraining-data-poisoned Qwen3-4B backdoor only fires on the exact trigger tokens — paraphrases don't activate it, and base-model similarity to the trigger doesn't predict which inputs fire (MODERATE confidence)", clean result "The marker is a representational handle, not a behavioural one — sharing it between a villain persona and the assistant transfers no misalignment (HIGH confidence)". Matching terms: width, rate, model. Source: arxiv stat.ML (Machine Learning).
arXiv: 2508.21531

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