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We consider the problem of constructing nonparametric undirected graphical models for high-dimensional functional data. Most existing statistical methods in this context assume either a Gaussian distribution on the vertices or linear…

Statistics Theory · Mathematics 2021-03-22 Eftychia Solea , Holger Dette

Regression models are used in a wide range of applications providing a powerful scientific tool for researchers from different fields. Linear, or simple parametric, models are often not sufficient to describe complex relationships between…

Machine Learning · Statistics 2021-11-24 Aliaksandr Hubin , Geir Storvik , Florian Frommlet

This paper presents a theory of non-linear integer/real arithmetic and algorithms for reasoning about this theory. The theory can be conceived as an extension of linear integer/real arithmetic with a weakly-axiomatized multiplication…

Logic in Computer Science · Computer Science 2022-11-09 Zachary Kincaid , Nicolas Koh , Shaowei Zhu

Let n be a positive integer, and let R be a finitely presented (but not necessarily finite dimensional) associative algebra over a computable field. We examine algorithmic tests for deciding (1) if every n-dimensional representation of R is…

Rings and Algebras · Mathematics 2007-05-23 Edward S. Letzter

Graph auto-encoders are widely used to construct graph representations in Euclidean vector spaces. However, it has already been pointed out empirically that linear models on many tasks can outperform graph auto-encoders. In our work, we…

Machine Learning · Computer Science 2023-12-01 Solveig Klepper , Ulrike von Luxburg

A simple sparse coding mechanism appears in the sensory systems of several organisms: to a coarse approximation, an input $x \in \R^d$ is mapped to much higher dimension $m \gg d$ by a random linear transformation, and is then sparsified by…

Neural and Evolutionary Computing · Computer Science 2020-06-09 Sanjoy Dasgupta , Christopher Tosh

We consider the problem of high-dimensional non-linear variable selection for supervised learning. Our approach is based on performing linear selection among exponentially many appropriately defined positive definite kernels that…

Machine Learning · Computer Science 2009-09-08 Francis Bach

Linear algebra's main concerns are sets of vectors, linear functions, subspaces, linear systems, matrices and concepts about those, such as whether the solution of linear system exists or is unique; a set of vectors is linearly independent…

Symbolic Computation · Computer Science 2025-04-15 Iago Leal de Freitas , Júlia Mota , João Paixão , Lucas Rufino

We study the structure of the asymptotic expansion of the probability that a combinatorial object is connected. We show that the coefficients appearing in those asymptotics are integers and can be interpreted as the counting sequences of…

Combinatorics · Mathematics 2024-01-02 Thierry Monteil , Khaydar Nurligareev

Recent advancements in quantum computing and quantum-inspired algorithms have sparked renewed interest in binary optimization. These hardware and software innovations promise to revolutionize solution times for complex problems. In this…

A method of classification of integrable equations on quad-graphs is discussed based on algebraic ideas. We assign a Lie ring to the equation and study the function describing the dimensions of linear spaces spanned by multiple commutators…

Exactly Solvable and Integrable Systems · Physics 2015-05-19 Ismagil T. Habibullin , Elena V. Gudkova

We study kernel functions, and associated reproducing kernel Hilbert spaces $\mathscr{H}$ over infinite, discrete and countable sets $V$. Numerical analysis builds discrete models (e.g., finite element) for the purpose of finding…

Functional Analysis · Mathematics 2015-08-17 Palle Jorgensen , Feng Tian

In this paper, we introduce a graph matching method that can account for constraints of arbitrary order, with arbitrary potential functions. Unlike previous decomposition approaches that rely on the graph structures, we introduce a…

Computer Vision and Pattern Recognition · Computer Science 2018-02-26 D. Khuê Lê-Huu , Nikos Paragios

We construct new linear codes with high minimum distance d. In at least 12 cases these codes improve the minimum distance of the previously known best linear codes for fixed parameters n,k. Among these new codes there is an optimal ternary…

Information Theory · Computer Science 2007-07-16 Axel Kohnert

An effective means to approximate an analytic, nonperiodic function on a bounded interval is by using a Fourier series on a larger domain. When constructed appropriately, this so-called Fourier extension is known to converge geometrically…

Numerical Analysis · Mathematics 2013-05-14 Ben Adcock , Daan Huybrechs , Jesus Martin-Vaquero

Can we effectively learn a nonlinear representation in time comparable to linear learning? We describe a new algorithm that explicitly and adaptively expands higher-order interaction features over base linear representations. The algorithm…

Machine Learning · Computer Science 2014-10-03 Alekh Agarwal , Alina Beygelzimer , Daniel Hsu , John Langford , Matus Telgarsky

We initiate the probabilistic analysis of linear programming (LP) decoding of low-density parity-check (LDPC) codes. Specifically, we show that for a random LDPC code ensemble, the linear programming decoder of Feldman et al. succeeds in…

Information Theory · Computer Science 2016-11-15 Constantinos Daskalakis , Alexandros G. Dimakis , Richard M. Karp , Martin J. Wainwright

The paper describes a new algorithm of construction of the nonlinear arithmetic triangle on the basis of numerical simulation and the binary system. It demonstrates that the numbers that fill the nonlinear arithmetic triangle may be…

General Mathematics · Mathematics 2013-03-12 Alexander V. Yurkin

This work presents an initial analysis of using bijective mappings to extend the Theory of Functional Connections to non-rectangular two-dimensional domains. Specifically, this manuscript proposes three different mappings techniques: a)…

Numerical Analysis · Mathematics 2020-08-18 Daniele Mortari , David Anas

The modular decomposition of a graph is a canonical representation of its modules. Algorithms for computing the modular decomposition of directed and undirected graphs differ significantly, with the undirected case being simpler, and…

Discrete Mathematics · Computer Science 2017-10-13 Henning Koehler