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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,695 papers · 148 categories

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138275413550 · Jun 202019922001200920172026
48 results for Linear relation

DREAM model improves computational efficiency for non-linear effects in relational event models.

problem Efficiently modeling non-linear effects in dynamic relational networks.
method Introduces Deep Relational Event Additive Model (DREAM) using Neural Additive Models.
result Demonstrates superior computational efficiency compared to traditional REM approaches.

The study finds parametrizations for surfaces of revolution with a linear curvature ratio.

problem Deriving surfaces of revolution with a specific curvature ratio.
method Derives parametrizations for surfaces of revolution with an affine-linear relation between their curvature radii.
result Explicit parametrizations found for a countably-infinite number of surfaces.

A new complexity measure for neural networks improves upon classical methods.

problem Lack of a refined complexity measure for comparing different neural network architectures, especially permutation-invariant ones.
method Introduced an equivalence relation among linear functions and counted them relative to this relation.
result The new complexity measure clearly distinguishes between different models and increases exponentially with depth.

Legendre transformations link related integrable hierarchies.

problem Understanding relationships between integrable hierarchies.
method Legendre-type transformations of generalized Frobenius manifolds.
result Linear reciprocal transformations link related hierarchies.

Monotonic relationship found between in-distribution and out-of-distribution performance.

problem Understanding performance of machine learning models under distribution shifts.
method Analyzing ridge-regularized models and linear inverse problems under covariate shift.
result Monotonic relationship between in-distribution and out-of-distribution performance for certain models.

In this note we discuss some formal properties of universal linearization operator, relate this to brackets of non-linear differential operators and discuss application to the calculus of auxiliary integrals, used in compatibility reductions of PDEs.

2007-12-20abs ↗pdf ↗

Researchers prove no unexpected relations between complex manifold numbers.

problem Proving no unexpected universal linear relations between Hodge, Betti, and Chern numbers of compact complex manifolds.
method Developed a framework to tackle more general questions involving all cohomological invariants, solved specific construction problems.
result Obtained full answers to general questions about universal relations and bimeromorphic invariants in low dimensions.

The paper investigates non-linear and heavy-tailed predictability in transition-energy financial markets.

problem Incomplete representation of dependence structure in Gaussian-linear forecasting frameworks.
method Develops a hybrid forecasting framework combining Student-t Vector Autoregressions with nonlinear recurrent residual learning architectures.
result The proposed framework consistently improves predictive accuracy relative to conventional models, especially during macro-financial stress.

New algorithm for learning causal structures with disjoint cycles in linear non-Gaussian models.

problem Learning causal structures with cycles in linear non-Gaussian models.
method Characterizing when graphs determine the same model, using quadratic and cubic polynomial relations, and a strategy of decorrelating cycles and multivariate regression.
result Consistent and computationally efficient algorithm for learning causal structures with disjoint cycles.

We show that gradient descent on full-width linear convolutional networks of depth LL converges to a linear predictor related to the 2/L\ell_{2/L} bridge penalty in the frequency domain. This is in contrast to linearly fully connected networks, where gradient descent converges to the hard margin linear support vector m…

2018-06-01abs ↗pdf ↗

In this paper we are interested in non trivial bi-Hamiltonian deformations of the Poisson pencil $ω_λ=ω_2+λω_1=uδ'(x-y)+\f{1}{2}u_xδ(x-y)+λδ'(x-y)$. Deformations are generated by a sequence of vector fields {X2,X4,...}\{X_2, X_4,...\}, where each X2kX_{2k} is homogenous of degree 2k2k with respect to a grading induced by rescali…

2010-12-30abs ↗pdf ↗

We explain some interesting relations in the degree three bounded cohomology of surface groups. Specifically, we show that if two faithful Kleinian surface group representations are quasi-isometric, then their bounded fundamental classes are the same in bounded cohomology. This is novel in the setting that one end is d…

2018-08-17abs ↗pdf ↗

The aim of this paper is to present a complete description of all rotational linear Weingarten surface into the Euclidean sphere S3. These surfaces are characterized by a linear relation aH+bK=c, where H and K stand for their mean and Gaussian curvatures, respectively, whereas a; b and c are real constants.

2010-12-20abs ↗pdf ↗

In this paper we give an example of a linear group such that its tensor square is not linear. Also, we formulate some sufficient conditions for the linearity of non-abelian tensor products GHG \otimes H and tensor squares GGG \otimes G. Using these results we prove that tensor squares of some groups with one relation a…

2017-10-06abs ↗pdf ↗

We classify all rotational surfaces in Euclidean space whose principal curvatures κ1κ_1 and κ2κ_2 satisfy the linear relation κ1=aκ2+bκ_1=aκ_2+b, where aa and bb are two constants. We give a variational characterization of these surfaces in terms of its generating curve. As a consequence of our classification, we find clos…

2018-08-22abs ↗pdf ↗

Two models predict similar high-frequency price dynamics but differ in low-frequency impact strength.

problem Understanding the relationship between market prices and fundamental information.
method Comparing a microfounded linear model with a data-driven model at high and low frequencies.
result Both models predict similar high-frequency price dynamics but differ in low-frequency impact strength.

In this paper, we deal with the linear Weingarten factorable surfaces in the isotropic 3-space I^{3} satisfying the relation aK+bH=c, where K is the relative curvature and H the isotropic mean curvature, a,b,cR. We obtain a complete classification for such surfaces in I^{3}. As a further study, we classify all graph su…

2016-04-06abs ↗pdf ↗

The paper explores the pentagon relation and its algebraic forms.

problem Exploring the pentagon relation and its various forms.
method Starting with geometric form, then algebraic form as a family of equations, deriving equivalent forms using 6j-symbols, and extracting solutions from modular categories.
result Extracting a solution of the pentagon relation from any modular category.

In these lectures notes I discuss the Linearization Theorem for Lie groupoids, and its relation to the various classical linearization theorems for submersions, foliations and group actions. In particular, I explain in some detail the recent metric approach to this problem.

2014-12-17abs ↗pdf ↗

Variational auto-encoder frameworks have demonstrated success in reducing complex nonlinear dynamics in molecular simulation to a single non-linear embedding. In this work, we illustrate how this non-linear latent embedding can be used as a collective variable for enhanced sampling, and present a simple modification th…

2018-01-02abs ↗pdf ↗

Data analyses based on linear methods constitute the simplest, most robust, and transparent approaches to the automatic processing of large amounts of data for building supervised or unsupervised machine learning models. Principal covariates regression (PCovR) is an underappreciated method that interpolates between pri…

2020-02-12abs ↗pdf ↗

We solve structure learning for cyclic linear causal models using observational data.

problem Learning the structure of cyclic linear causal models from observational data.
method Assuming simple graphs, we use a criterion for distributional equivalence and implement a greedy search method.
result We show that simple cyclic models are of expected dimension and justify score-based methods for structure learning.

We provide theoretical and empirical evidence for a type of asymmetry between causes and effects that is present when these are related via linear models contaminated with additive non-Gaussian noise. Assuming that the causes and the effects have the same distribution, we show that the distribution of the residuals of …

2014-09-16abs ↗pdf ↗

We relate canonical algebraic curvature tensors that are built from a self-adjoint (RASR^S_A) or skew adjoint (RAΛR^Λ_A) linear operator A. Several authors have proven that any algebraic curvature tensor RR may be expressed as a sum of RASR^S_A, or as a sum of RAΛR^Λ_A. This motivates our interest in relating them as well…

2015-10-09abs ↗pdf ↗

Defines linear weightings for vector bundles and explores their applications.

problem Understanding and extending the concept of weightings in vector bundles.
method Constructs weighted normal bundles and deformation spaces; explains the relationship between weightings and differential operators.
result Captures the rescaled spinor bundle and related constructions.

Time-related features improve time series forecasting models.

problem Lack of explicit time-related encoding in current forecasting models limits their ability to capture cyclical and seasonal trends.
method Introducing Time Stamp Forecaster (TimeSter) to encode time-related features and integrating it with a linear backbone.
result TimeLinear model reduces MSE by 23% on benchmark datasets, improving performance with exceptional efficiency.

Lecture notes on advanced linear regression methods.

problem Understanding the properties of linear regression estimators in high dimensions.
method Proposition-proof exploration of least squares, ridgeless, ridge, and lasso estimators.
result Detailed analysis of the existence, uniqueness, relations, computation, and non-asymptotic properties of these estimators.

A linear Weingarten surface in Euclidean space R3{\bf R}^3 is a surface whose mean curvature HH and Gaussian curvature KK satisfy a relation of the form aH+bK=caH+bK=c, where a,b,cRa,b,c\in {\bf R}. Such a surface is said to be hyperbolic when a2+4bc<0a^2+4bc<0. In this paper we classify all rotational linear Weingarten surfaces of…

2006-10-18abs ↗pdf ↗