Related papers: Inductive randomness predictors: beyond conformal

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Universality of conformal prediction under the assumption of randomness [0.0]
Conformal predictors provide set or functional predictions that are valid under the assumption of randomness.<n>The question is whether there are predictors that are valid in the same sense under the assumption of randomness and that are more efficient than conformal predictors.<n>The answer is that the class of conformal predictors is universal in that only limited gains in predictive efficiency are possible.
arXiv Detail & Related papers (2025-02-26T16:00:42Z)
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It introduces new kinds of confidence predictors, including randomness predictors and exchangeability predictors.<n>The main result implies that both are close to conformal predictors and quantifies the difference between randomness prediction and conformal prediction.
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arXiv Detail & Related papers (2024-02-06T09:37:06Z)
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arXiv Detail & Related papers (2023-02-03T15:11:39Z)
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We show that there seems to be no loss function that provides an incentive for a second-order learner to faithfully represent its uncertainty. As a main mathematical tool to prove this result, we introduce the generalised notion of second-order scoring rules.
arXiv Detail & Related papers (2023-01-30T08:59:45Z)
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arXiv Detail & Related papers (2022-10-01T02:57:37Z)
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DEUP: Direct Epistemic Uncertainty Prediction [56.087230230128185]
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arXiv Detail & Related papers (2021-02-16T23:50:35Z)
Sequential prediction under log-loss and misspecification [47.66467420098395]
We consider the question of sequential prediction under the log-loss in terms of cumulative regret. We show that cumulative regrets in the well-specified and misspecified cases coincideally. We provide an $o(1)$ characterization of the distribution-free or PAC regret.
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arXiv Detail & Related papers (2020-02-23T17:54:07Z)

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