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- W2788867418 abstract "A generative may generate utter nonsense when it is fit to maximize the likelihood of observed data. This happens due to model error, i.e., when the true data generating distribution does not fit within the class of generative models being learned. To address this, we propose a of active distribution learning using a binary invalidity oracle that identifies some examples as clearly invalid, together with random positive examples sampled from the true distribution. The goal is to maximize the likelihood of the positive examples subject to the constraint of (almost) never generating examples labeled invalid by the oracle. Guarantees are agnostic compared to a class of probability distributions. We show that, while proper learning often requires exponentially many queries to the invalidity oracle, improper distribution learning can be done using polynomially many queries." @default.
- W2788867418 created "2018-03-06" @default.
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- W2788867418 date "2018-02-20" @default.
- W2788867418 modified "2023-09-27" @default.
- W2788867418 title "Actively Avoiding Nonsense in Generative Models" @default.
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