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An extension of the Dirac procedure for the quantization of constrained systems is necessary to address certain issues that are left open in Dirac's original proposal. These issues play an important role especially in the context of…

General Relativity and Quantum Cosmology · Physics 2009-10-22 A. Ashtekar , Ranjeet S. Tate

The aim of this work is to develop a global calculus for pseudo-differential operators acting on suitable algebras of generalized functions. In particular, a condition of global hypoellipticity of the symbols gives a result of regularity…

Analysis of PDEs · Mathematics 2007-05-23 Claudia Garetto

Generalised Bayesian learning algorithms are increasingly popular in machine learning, due to their PAC generalisation properties and flexibility. The present paper aims at providing a self-contained survey on the resulting PAC-Bayes…

Machine Learning · Statistics 2020-02-06 Benjamin Guedj

We construct a Galois correspondence for finite purely inseparable field extensions $F/K$, generalising a classical result of Jacobson for extensions of exponent one (where $x^p \in K$ for all $x\in F$).

Number Theory · Mathematics 2023-01-10 Lukas Brantner , Joe Waldron

J{\'o}nsson and Tarski's notion of the perfect extension of a Boolean algebra with operators has evolved into an extensive theory of canonical extensions of lattice-based algebras. After reviewing this evolution we make two contributions.…

Logic · Mathematics 2023-11-08 Robert Goldblatt

Generalized numberings are an extension of Ershov's notion of numbering, based on partial combinatory algebra (pca) instead of the natural numbers. We study various algebraic properties of generalized numberings, relating properties of the…

Logic · Mathematics 2020-04-30 H. P. Barendregt , S. A. Terwijn

The notion of bounded expansion captures uniform sparsity of graph classes and renders various algorithmic problems that are hard in general tractable. In particular, the model-checking problem for first-order logic is fixed-parameter…

In statistical learning theory, a generalization bound usually involves a complexity measure imposed by the considered theoretical framework. This limits the scope of such bounds, as other forms of capacity measures or regularizations are…

Machine Learning · Statistics 2024-02-22 Paul Viallard , Rémi Emonet , Amaury Habrard , Emilie Morvant , Valentina Zantedeschi

In this first work dedicated to the generalisation of classic algebraic geometry to non algebraically closed fields and axiomatisable classes of fields, we develop the foundations for equiresidual algebraic geometry (EQAG), i.e. algebraic…

Algebraic Geometry · Mathematics 2022-07-12 Jean Barbet

Regular groups and fields are common generalizations of minimal and quasi-minimal groups and fields, so the conjectures that minimal or quasi-minimal fields are algebraically closed have their common generalization to the conjecture that…

Logic · Mathematics 2012-11-19 Tomasz Gogacz , Krzysztof Krupinski

We classify all division algebras that are principal Albert isotopes of a cyclic Galois field extension of degree $n>2$ up to isomorphisms. We achieve a ``tight'' classification when the cyclic Galois field extension is cubic. The…

Rings and Algebras · Mathematics 2025-02-28 Susanne Pumpluen

We introduce a notion of algorithmic randomness for algebraic fields. We prove the existence of a continuum of algebraic extensions of $\mathbb{Q}$ that are random according to our definition. We show that there are noncomputable algebraic…

Logic · Mathematics 2024-07-08 Wesley Calvert , Valentina Harizanov , Alexandra Shlapentokh

We investigate the cold posterior effect through the lens of PAC-Bayes generalization bounds. We argue that in the non-asymptotic setting, when the number of training samples is (relatively) small, discussions of the cold posterior effect…

Machine Learning · Computer Science 2022-06-23 Konstantinos Pitas , Julyan Arbel

PAC-Bayes learning is a comprehensive setting for (i) studying the generalisation ability of learning algorithms and (ii) deriving new learning algorithms by optimising a generalisation bound. However, optimising generalisation bounds might…

Machine Learning · Statistics 2024-11-27 Antoine Picard-Weibel , Roman Moscoviz , Benjamin Guedj

We extend the theory of PAC learning in a way which allows to model a rich variety of learning tasks where the data satisfy special properties that ease the learning process. For example, tasks where the distance of the data from the…

Machine Learning · Computer Science 2021-07-22 Noga Alon , Steve Hanneke , Ron Holzman , Shay Moran

We establish primitive recursive versions of some known facts about computable ordered fields of reals and computable reals, and then apply them to proving primitive recursiveness of some natural problems in linear algebra and analysis. In…

Computational Complexity · Computer Science 2021-11-09 Victor Selivanov , Svetlana Selivanova

We describe a Picard-Vessiot theory for differential fields with non algebraically closed fields of constants. As a technique for constructing and classifying Picard-Vessiot extensions, we develop a Galois descent theory. We utilize this…

Classical Analysis and ODEs · Mathematics 2008-02-21 Tobias Dyckerhoff

The general class of the graded Lie algebras is defined. These algebras could be constructed using an arbitrary dynamical systems with discrete time and with invarinat measure. In this papers we consider the case of the central extension of…

Dynamical Systems · Mathematics 2007-05-23 A. Vershik

Different from the view that information is objective reality, this paper adopts the idea that all information needs to be compiled by the interpreter before it can be observed. From the traditional complexity definition, this paper defines…

Logic in Computer Science · Computer Science 2025-02-18 Zhifeng Ma , Tianyi Wu , Zhangang Han

We derive explicit non-asymptotic PAC-Bayes generalization bounds for Gibbs posteriors, that is, data-dependent distributions over model parameters obtained by exponentially tilting a prior with the empirical risk. Unlike classical…

Machine Learning · Statistics 2026-04-21 Chenyang Wang , Yun Yang
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