arXiv Machine Learning
Sample-Smooth Spaces: A Convenient Category for Differentiable Probabilistic Programming
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arXiv Machine Learning
Jul 20
Testing Distributions Against Bounded Distinguishers
arXiv:2607. 15645v1 Announce Type: cross Abstract: Motivated by the challenge of testing distributions over high-dimensional or continuous domains, we study distribution testing with respect to bounded classes of distinguishers.
arXiv Statistics ML
Sep 25
Transitional Conditional Independence
arXiv:2104. 11547v5 Announce Type: replace-cross Abstract: Statistical models contain variables that are not random: parameters, treatments, environments, design points.
arXiv Machine Learning
Aug 7
Realizable Bayes-Consistency for General Metric Losses
arXiv:2605. 03823v3 Announce Type: replace Abstract: We study strong universal Bayes-consistency in the realizable setting for learning with general metric losses, extending classical characterizations beyond $0$-$1$ classification (Bousquet et al.
arXiv Machine Learning
Sep 1
A Borel Concept Class of VC Dimension One with a Non-PAC Consistent Learner in ZFC
arXiv:2608.30246v1 Announce Type: cross Abstract: The fundamental theorem of statistical learning states that, under suitable measurability assumptions, finite Vapnik--Chervonenkis (VC) dimension gua...
arXiv Machine Learning
Sep 22
Exactness at Inference: A Representational Criterion for Out-of-Distribution Generalization
arXiv:2609.24942v1 Announce Type: new Abstract: A model generalizes outside its training distribution only when it computes a representation structurally equivalent to the generating mechanism, not a...
arXiv AI
Jul 20
A cubical formalisation of topos causal models: intervention, sheaf gluing, and the intuitionistic do-calculus
arXiv:2607. 15629v1 Announce Type: cross Abstract: Topos causal models recast causal inference inside a topos: a causal world is a presheaf, an intervention is a characteristic map into the subobject classifier, and reasoning is carried out in the intuitionistic internal language.