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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.

168,657 papers · 148 categories

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16324864 · May 202619922001200920172026
48 results for underlap coefficient

Generalizes underlap coefficient for multivariate group separation.

problem Quantifying distributional separation across groups in statistical learning.
method Generalizes underlap coefficient (UNL) to multivariate settings, studies its relationship with Bayes risk and mutual information, proposes an efficient importance sampling estimator.
result UNL as a measure of dependence between group labels and variables of interest, interpretable measure of partition-covariate dependence in clustering.

Generalizes underlap coefficient for multivariate group separation.

problem Quantifying distributional separation across groups in statistical learning.
method Generalizes underlap coefficient (UNL) to multivariate variables, establishes key properties, interprets as dependence measure, proposes efficient estimator.
result Highlights the UNL's utility in clustering for evaluating group structure dependence on covariates.

Machine learning predicts Kronecker coefficients with high accuracy.

problem Predicting Kronecker coefficients from tensor products of symmetric group representations.
method Training machine learning models (NN, CNN, GBDT) to classify Kronecker coefficients as zero or non-zero.
result Trained models achieve high accuracy (0.98\approx 0.98) in classifying Kronecker coefficients.

Abstract: Determines thermoelastic coefficients from boundary data.

problem Determining coefficients of thermoelastic system from boundary information.
method Explicit expression for thermoelastic Dirichlet-to-Neumann map with variable coefficients.
result Thermoelastic Dirichlet-to-Neumann map uniquely determines coefficients on the manifold.

New filling functions for groups with coefficients show different asymptotic behavior.

problem Difficulty in filling loops with surfaces in Cayley graphs.
method Defining homological filling functions with coefficients and proving their differences.
result Filling functions for nn-cycles with coefficients in different groups have distinct asymptotic behavior.

Abstract: Determines Lamé coefficients from boundary measurements.

problem Determining Lamé coefficients from elastic boundary measurements.
method Explicit symbol of elastic Dirichlet-to-Neumann map, partial derivatives determination.
result Elastic Dirichlet-to-Neumann map uniquely determines Lamé coefficients.

Defines and proves properties of weighted renormalized volume coefficients.

problem None explicitly stated; focuses on mathematical definitions and proofs.
method Defines weighted renormalized volume coefficients and proves their variational nature and polynomial representation.
result Weighted renormalized volume coefficients are variational and can be expressed as polynomials of specific tensors.

Improved 3D LiDAR data classification using product coefficients.

problem Enhancing accuracy in 3D LiDAR data classification.
method Introducing product coefficients derived from measure theory as additional features in the classification process, alongside PCA.
result Significant improvement in classification accuracy with product coefficients.

We study the structure of the stable coefficients of the Jones polynomial of an alternating link. We start by identifying the first four stable coefficients with polynomial invariants of a (reduced) Tait graph of the link projection. This leads us to introduce a free polynomial algebra of invariants of graphs whose ele…

2013-09-23abs ↗pdf ↗

Paper calculates third coefficient in Kaehler-Einstein metric expansion.

problem Understanding Kaehler-Einstein metrics and their epsilon functions.
method Computes the third coefficient in the TYCZ-expansion of the epsilon function.
result Discovers the vanishing of the third coefficient's significance.

Develops local elliptic regularity for geometrically-natural operators with low regularity coefficients.

problem Local elliptic regularity for operators with low regularity coefficients in Sobolev-type spaces.
method Rescaling estimates and multiplication results for function spaces.
result Unified set of interior estimates and regularity inference for operators with Sobolev-type coefficients.

Extends A-type coefficient polynomials to B-type setting, introducing new invariants.

problem Tackles the B-type skein relation and introduces new coefficient polynomials.
method Introduces coefficient polynomials associated with the B-type skein relation and proves their invariance under Reidemeister moves.
result Shows that the generating series of these coefficient polynomials recovers the Kauffman polynomial.

We simplify complex regression coefficients using linearization and feature comparison.

problem Interpreting high-dimensional regression coefficients from nonlinear responses.
method Developed a linearization method to derive feature coefficients and compare them with regression coefficients.
result Shows how regression coefficients relate to linearized feature coefficients and how they change under regularization.

Bounding twist number of surface links using polynomial coefficients.

problem Bounding the twist number of alternating surface links.
method Introducing a generalized homological Kauffman bracket and applying it to surface link diagrams.
result A bound for the twist number of alternating surface links in terms of polynomial coefficients.

Characterizes differential forms and vector fields with constant coefficients on manifolds.

problem Understanding constant coefficient differential forms and vector fields on manifolds.
method Analyzes differential forms and vector fields of specific degrees, proving obstructions and characterizing solutions to partial differential systems.
result Characterizes differential forms and vector fields with constant coefficients of various degrees on smooth manifolds.

Study on gradient descent in Hilbert spaces with Markov chains, focusing on mixing coefficients.

problem Analyzing convergence of gradient descent in Hilbert spaces with stationary Markov chains.
method Examined strictly stationary Markov chains with φφ- and ββ-mixing coefficients, derived probabilistic upper bounds.
result Probabilistic upper bounds on convergence behavior of gradient descent algorithm based on mixing coefficients.

The conformal powers of the Laplacian of a Riemannian metric which are known as the GJMS-operators admit a combinatorial description in terms of the Taylor coefficients of a natural second-order one-parameter family (˝r;g)\H(r;g) of self-adjoint elliptic differential operators. (˝r;g)\H(r;g) is a non-Laplace-type perturbation …

2014-11-28abs ↗pdf ↗

Guts determine the leading coefficients of L2L^2-Alexander torsions for 3-manifolds.

problem Determining the leading coefficient of L2L^2-Alexander torsions for 3-manifolds.
method Using a new criterion for the convergence of Fuglede-Kadison determinants and the work of Agol and Zhang on guts of 3-manifolds.
result The leading coefficient equals the relative L2L^2-torsion of the guts associated to the cohomology class.

Diffusion models adapt to low-dimensional data regardless of coefficient choices.

problem Understanding how diffusion models adapt to low-dimensional data structures.
method Analysis of diffusion models with flexible coefficient choices.
result Proven that O~(k/ε)\widetilde{O}(k/\varepsilon) iterations suffice for accurate sampling in total variation distance.

GenMod uses generative models to approximate high-dimensional PDE solutions with limited evaluations.

problem Quantifying uncertainty in high-dimensional PDE systems with random parameters.
method Develops a method using generative models to approximate polynomial chaos coefficients in underdetermined systems.
result The method outperforms sparsity-promoting methods in approximating PDE solutions with limited evaluations.

We prove the 33-manifold $\RP^3 \# \RP^3$ is of Z2\Z_{2}-coefficient homology (1,2)(1, 2)-systolic freedom. Given a Riemannian metric on $\RP^{3}\# \RP^{3}$, we define Z2\Z_{2}-coefficient homology 11-systole as the infimum of lengths of all nonseparating geodesic loops representing nontrivial classes in $H_{1}(\RP^3\#\…

2014-02-18abs ↗pdf ↗

The nullspace and regularization impact high-dimensional linear regression interpretability.

problem Interpreting high-dimensional linear regression coefficients in complex data.
method Optimization formulation to compare coefficients and physical knowledge.
result Regularization and z-scoring choices affect interpretability and true coefficient closeness.

Framework uses deep learning and statistical models to solve PDEs with discontinuous coefficients.

problem Solving PDEs with discontinuous coefficients.
method Two-stage physics-informed deep learning and statistical mixture models.
result Framework achieves adaptability and accurate parameter identification.

A fundamental property of complex networks is the tendency for edges to cluster. The extent of the clustering is typically quantified by the clustering coefficient, which is the probability that a length-2 path is closed, i.e., induces a triangle in the network. However, higher-order cliques beyond triangles are crucia…

2017-04-12abs ↗pdf ↗

The paper solves MMV and MV problems with random coefficients and finds shared optimal strategies.

problem Optimal trading strategies with random market coefficients.
method Backward stochastic differential equations (BSDEs) to find optimal strategies.
result MMV and MV problems share the same optimal portfolio and value under random coefficients.

We construct explicitly the Khovanov homology theory for virtual links with arbitrary coefficients by using the twisted coefficients method. This method also works for constructing Khovanov homology for ``non-oriented virtual knots'' in the sense of Viro, in particular, for knots in RP3{\bf R}P^{3}.

2006-01-08abs ↗pdf ↗

A new tree-based model for varying coefficients using CGBM.

problem Modeling varying coefficients with high dimensionality and complex interactions.
method Tree-based varying coefficient model with CGBM for varying coefficients, dimension-wise early stopping, and feature importance scores.
result The model produces comparable out-of-sample loss to neural networks, demonstrating effectiveness.

Extends Kyle model to multiple traders with different time-preference coefficients.

problem Existence and convergence of discrete-time Kyle models with multiple insiders.
method Extends Basak and Cuoco's model to include traders with different time-preference coefficients.
result Parameter restrictions ensure the existence of a Radner equilibrium and long-term survival of traders.

Let PP be a non-negative self-adjoint Laplace type operator acting on sections of a hermitian vector bundle over a closed Riemannian manifold. In this paper we review the close relations between various PP-related coefficients such as the mollified spectral counting coefficients, the heat trace coefficients, the reso…

2015-09-01abs ↗pdf ↗

The behavior under conformal change of the renormalized volume coefficients associated to a pseudo-Riemannian metric is investigated. It is shown that they define second order fully nonlinear operators in the conformal factor whose algebraic structure is elucidated via the introduction of "extended obstruction tensors"…

2008-10-23abs ↗pdf ↗

Defines Floer homology with DG coefficients for symplectic manifolds.

problem Computing Floer homology with DG coefficients for symplectic manifolds.
method Develops DG Floer toolset, defines spectral invariants, and proves Viterbo isomorphism theorem.
result Establishes almost existence of contractible periodic orbits on cotangent bundles.

Study reconstructs Faber-Schauder coefficients from antiderivative observations.

problem Reconstructing Faber-Schauder coefficients from discrete antiderivative observations.
method Piecewise quadratic spline interpolation and closed-form solution.
result Final-generation coefficients are unstable; others are robust.

The fused lasso is analyzed for high-dimensional piecewise-constant regression coefficients.

problem Estimation of high-dimensional piecewise-constant regression coefficients.
method Formulated a restricted isometry condition for the fused lasso estimator and derived estimation bounds.
result The estimation error can be dominated by either the lasso or the fused lasso rate, depending on the number of non-zero coefficients and piece-wise constant segments.

In this note, we establish a relationship between fractional Dehn twist coefficients of Riemann surface automorphisms and modular invariants of holomorphic families of algebraic curves. Specially, we give a characterization of pseudo-periodic maps with nontrivial fractional Dehn twist coefficients. We also obtain some …

2019-12-31abs ↗pdf ↗

Paper proposes methods for transfer learning with random coefficient ridge regression.

problem Estimation and prediction in high-dimensional settings with related models.
method Two estimators using weighted sums of ridge estimates from target and source models.
result Explicit expression of estimation and prediction risks derived using random matrix theory.

Sharp lower bounds for modular invariants and Dehn twist coefficients in genus 2 and 3.

problem Finding sharp lower bounds for modular invariants and Dehn twist coefficients.
method Analyzing the relation between fractional Dehn twists and modular invariants, classifying pseudo-periodic maps, and proving rigidity properties.
result Sharp lower bounds for modular invariants and Dehn twist coefficients in genus 2 and 3.