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Credal geometry is established as a principled framework for quantifying conformal uncertainty and understanding its limitations, reducing potentially exponential computations to linear time after sorting the conformal p-values.
This work introduces epistemic learning from credal supervision, a framework that uses convex sets of plausible labelling distributions as supervision and learns sets of predictive distributions, and shows how credal labels can be obtained through a simple relaxation of existing probabilistic labels, reducing commitmen...
QuadraSHAP is introduced, a method for $\epsilon$-exact Shapley computation in product games, cooperative games whose coalition values factorize across players, and extends to weighted sums of product games, supporting baseline and empirical interventional attribution across a broad class of models.
Textual descriptions of reaction conditions are encoded by a fine-tuned language model trained jointly with Gaussian process surrogates, yielding task-adaptive representations within a multi-objective Bayesian optimisation loop.
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