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Probabilistic Forecasting with Generative Networks via Scoring Rule Minimization. (arXiv:2112.08217v2 [stat.ML] UPDATED)
May 30, 2022, 1:11 a.m. | Lorenzo Pacchiardi, Rilwan Adewoyin, Peter Dueben, Ritabrata Dutta
stat.ML updates on arXiv.org arxiv.org
Generative networks are often trained to minimize a statistical divergence
between the reference distribution and the generative one in an adversarial
setting. Some works trained instead generative networks to minimize Scoring
Rules, functions assessing how well the generative distribution matches each
training sample individually. We show how the Scoring Rule formulation easily
extends to the so-called prequential (predictive-sequential) score, whose
minimization allows performing probabilistic forecasting with generative
networks. This objective leads to adversarial-free training, therefore easily
avoiding uncertainty underestimation due …
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