Axiomatic Learning and Probabilistic Structures with first applications to variants of Arithmetic Machine Learning and beyond
Abstract
We introduce the Axiomatic Learning Structures (ALS) and Axiomatic Probabilistic Structure (APS) in order to study the interactions of Machine Learning, Optimization, with research frameworks- which, we take the view, can be characterized at the core by the corresponding axioms. The ALS explores the interplays between structures that one is interested in Learning Theory such as Loss functions and the axioms of the objects and transformations involved in these functions. The APS studies the interaction between structures of interest in Probability Theory such as Gibbs distributions, equations with noise, etc. and the axioms of the corresponding objects and transformations. We also introduce in Section 6 Transformations-Axiomatic Based Learning (TABLe) on Mathematical data-sets. One feature of this approach is the ranking of transformations from a table for a certain task based on scores which reflect which transformations work better than others, similarly to when human investigators try various methods on a task. We also study structure recovery from noisy data. In particular, to exemplify the ideas, we consider loss functions which contain derivations and derivations-like transformations. As a first application we study recovery in the context of polynomials with integer coefficients using standard derivatives along with other types of derivations which appear in Arithmetic: the p-derivations which satisfy \textit{different axioms} from the usual derivatives. As a first application of the APS we study a class of noisy (partial/mixed) derivations equations. In the last sections, to provide further potential applications of this framework, we give an overview of an initial simplified Opinion Dynamics model and its (simultaneous) study of rules of updates.
Related
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BibTeX
@misc{margarint2026axiomatic,
title = {{Axiomatic Learning and Probabilistic Structures with first applications to variants of Arithmetic Machine Learning and beyond}},
author = {Vlad Margarint},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.36601},
eprint = {2610.36601},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.36601},
}
RIS
TY - UNPB
TI - Axiomatic Learning and Probabilistic Structures with first applications to variants of Arithmetic Machine Learning and beyond
AU - Vlad Margarint
PY - 2026
DA - 2026/10/27
PB - arXiv
DO - 10.48550/arxiv.2610.36601
UR - https://arxiv.org/abs/2610.36601
AB - We introduce the Axiomatic Learning Structures (ALS) and Axiomatic Probabilistic Structure (APS) in order to study the interactions of Machine Learning, Optimization, with research frameworks- which, we take the view, can be characterized at the core by the corresponding axioms. The ALS explores the interplays between structures that one is interested in Learning Theory such as Loss functions and the axioms of the objects and transformations involved in these functions. The APS studies the interaction between structures of interest in Probability Theory such as Gibbs distributions, equations with noise, etc. and the axioms of the corresponding objects and transformations. We also introduce in Section 6 Transformations-Axiomatic Based Learning (TABLe) on Mathematical data-sets. One feature of this approach is the ranking of transformations from a table for a certain task based on scores which reflect which transformations work better than others, similarly to when human investigators try various methods on a task. We also study structure recovery from noisy data. In particular, to exemplify the ideas, we consider loss functions which contain derivations and derivations-like transformations. As a first application we study recovery in the context of polynomials with integer coefficients using standard derivatives along with other types of derivations which appear in Arithmetic: the p-derivations which satisfy \textit{different axioms} from the usual derivatives. As a first application of the APS we study a class of noisy (partial/mixed) derivations equations. In the last sections, to provide further potential applications of this framework, we give an overview of an initial simplified Opinion Dynamics model and its (simultaneous) study of rules of updates.
ER -