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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.

168,742 papers · 148 categories

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52104156208 · Jun 202019922001200920172026
48 results for parametric families

Study algebraic relations of Vassiliev invariants for families of knots.

problem Understanding algebraic structure of Vassiliev invariants for knot families.
method Analyzing algebraic relations and generating sets of Vassiliev invariants in 3D Chern-Simons theory.
result For 1-parametric knot families, Vassiliev invariants are finitely generated. For more parameters, there can be an infinite number of generators.

In the context of the cohomological deformation theory, infinitesimal description of one-parametric families of Backlund transformations of special type including classical examples is given. It is shown that any family of such a kind evolves in the direction of a nonlocal symmetry shadow.

2000-10-25abs ↗pdf ↗

Recently Penskoi [J. Geom. Anal. 25 (2015), 2645-2666, arXiv:1308.1628] generalized the well known two-parametric family of Lawson tau-surfaces τr,mτ_{r,m} minimally immersed in spheres to a three-parametric family Ta,b,cT_{a,b,c} of tori and Klein bottles minimally immersed in spheres. It was remarked that this family inclu…

2014-06-18abs ↗pdf ↗

New minimal surfaces in 4D space derived from parametric equations.

problem Deriving explicit parametric equations for higher-order Henneberg-type minimal surfaces in R4\mathbb{R}^4.
method Generalized Weierstrass--Enneper representation and differential geometric analysis.
result Explicit parametric equations and differential geometric characteristics of the Henneberg-type minimal surfaces in R4\mathbb{R}^4.

In this work we prove the fact that, for a short time, it is possible to construct a smooth parametrized family of isometric embeddings of an arbitrary smooth parametrized family of Riemannian metrics on a smooth closed manifold into an Euclidean space. In order to prove this statement we work out stability estimates w…

2017-12-07abs ↗pdf ↗

We propose a novel approach for density estimation with exponential families for the case when the true density may not fall within the chosen family. Our approach augments the sufficient statistics with features designed to accumulate probability mass in the neighborhood of the observed points, resulting in a non-para…

2012-06-22abs ↗pdf ↗

A tractable pseudo-metric for non-parametric distributions via SPD geometry.

problem Computing distances between non-parametric probability distributions is intractable.
method Two-stage framework: projection onto parametric family, embedding into SPD matrices.
result Closed-form pseudo-metric for two-sample hypothesis testing.

New inequalities for convex curves with multiple geometric factors.

problem Establishing inequalities for convex curves with multiple geometric factors.
method Parametric isoperimetric-type inequalities for closed convex curves with parameter conditions and equality conditions.
result Derived new inequalities and improved versions of existing inequalities.

The paper extends Euler class theory to measurable cocycles.

problem Understanding the structure of measurable cocycles and their cohomology.
method Constructing a parametrized Euler class in bounded cohomology and studying semicohomologous cocycles.
result The parametrized Euler class vanishes if and only if the cocycle can be lifted and admits an equivariant family of points.

A parametric manifold is a manifold on which all tensor fields depend on an additional parameter, such as time, together with a parametric structure, namely a given (parametric) 1-form field. Such a manifold admits natural generalizations of Lie differentiation, exterior differentiation, and covariant differentiation, …

1994-07-12abs ↗pdf ↗

Improved inference for models with continuous latent variables.

problem Inference accuracy with traditional variational methods is limited.
method Reparameterized Variational Rejection Sampling (RVRS) using a proposal distribution with a reparameterized gradient estimator.
result RVRS offers a better trade-off between computational cost and inference fidelity.

Using Takahashi theorem we propose an approach to extend known families of minimal tori in spheres. As an example, the well-known two-parametric family of Lawson tau-surfaces including tori and Klein bottles is extended to a three-parametric family of tori and Klein bottles minimally immersed in spheres. Extremal spect…

2013-08-07abs ↗pdf ↗

Geometric families of low-rank covariances improve flexibility and tractability in high dimensions.

problem Interpolating and identifying covariance matrices in high dimensions with limited data.
method Differential geometric construction of low-rank covariance families, interpolation on manifolds, and distance minimization for identification.
result Differential geometric covariance families offer significant flexibility and computational tractability.

A parametric manifold can be viewed as the manifold of orbits of a (regular) foliation of a manifold by means of a family of curves. If the foliation is hypersurface orthogonal, the parametric manifold is equivalent to the 1-parameter family of hypersurfaces orthogonal to the curves, each of which inherits a metric and…

1994-07-12abs ↗pdf ↗

We propose a robust estimator to improve maximum likelihood in probabilistic models.

problem Overfitting and sensitivity to noise in maximum likelihood estimation.
method Distributionally robust maximum likelihood estimator that minimizes worst-case expected log-loss.
result The robust estimator is statistically consistent and performs well in regression and classification tasks.

This study improves tail risk forecasting by integrating overnight information into semi-parametric models.

problem Improving tail risk forecasting in financial markets.
method Proposes RES-CAViaR-oc models combining overnight return and realized volatility, using Bayesian estimation.
result Realized volatility and overnight return significantly improve tail risk forecasting.

We solve the mean parametrization of von Mises-Fisher distribution.

problem No closed-form normalization function for mean parameters exists.
method Derived a second-order ODE for mean normalizer and provided approximations.
result Rapid evaluation of densities and natural parameters in terms of mean parameters.

In this paper we address the problem of learning the structure of a Bayesian network in domains with continuous variables. This task requires a procedure for comparing different candidate structures. In the Bayesian framework, this is done by evaluating the {em marginal likelihood/} of the data given a candidate struct…

2013-01-16abs ↗pdf ↗

This paper starts a systematic description of colored knot polynomials, beginning from the first non-(anti)symmetric representation R=[2,1]. The project involves several steps: (i) parametrization of big families of knots a la arXiv:1506.00339, (ii) evaluating Racah/mixing matrices for various numbers of strands in var…

2015-08-12abs ↗pdf ↗

Study shows improper learning can outperform proper learning in misspecified models.

problem Misspecification in probabilistic prediction models.
method Investigates the performance of proper and improper learning strategies in misspecified models.
result Improper learning can achieve lower regret compared to proper learning, especially in high-dimensional settings.

Sparse Gaussian processes with compact kernels for faster inference.

problem Efficient Gaussian process inference with high computational complexity.
method Parametric families of compactly-supported kernels for sparse matrix representations.
result Sub-quadratic inference complexity and improved performance on real-world tasks.

Improved spatial distribution learning with Bayesian transport maps and parametric shrinkage.

problem Learning non-Gaussian spatial distributions with limited training data.
method Proposed ShrinkTM approach using Bayesian transport maps with parametric shrinkage.
result ShrinkTM outperforms existing BTM, especially with few training samples.

We propose a discrete surface theory in R3\mathbb R^3 that unites the most prevalent versions of discrete special parametrizations. This theory encapsulates a large class of discrete surfaces given by a Lax representation and, in particular, the one-parameter associated families of constant curvature surfaces. The theo…

2014-12-23abs ↗pdf ↗

We investigate minimal surfaces passing a given curve in R3R^{3}. Using the Frenet frame of a given curve and isothermal parameter, we derive the necessary and sufficient condition for minimal surface. Also we derive the parametric representation of two minimal surface families passing a circle and a helix as examples.

2014-08-16abs ↗pdf ↗

An index theory for projective families of elliptic pseudodifferential operators is developed. The topological and the analytic index of such a family both take values in twisted K-theory of the parametrizing space, X. The main result is the equality of these two notions of index when the twisting class is in the torsi…

2002-06-01abs ↗pdf ↗

Inspired by a string duality, we construct a deformation family for G2G_2-orbifolds given as total spaces of coassociative fibrations by ADE singularities over a closed and oriented smooth three-manifold QQ. The deformations are parametrized by sections of a fiber bundle on QQ that can be interpreted as spectral/came…

2019-10-23abs ↗pdf ↗

We analyzed the problem of finding a surfaces family through an asymptotic curve with Cartan frame. We obtain the parametric representation for surfaces family whose members have the same as an asymptotic curve. By using the Cartan frame of the given null curve, we present the surface as a linear combination of this fr…

2014-09-02abs ↗pdf ↗

Let MM be a hyperkaehler manifold, and ηη a closed, positive (1,1)-form which is degenerate everywhere on MM. We associate to ηη a family of complex structures on MM, called a degenerate twistor family, and parametrized by a complex line. When ηη is a pullback of a Kaehler form under a Lagrangian fibration LL, a…

2013-11-20abs ↗pdf ↗