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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.

168,738 papers · 148 categories

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1223 · Sep 202519922001200920172026
36 results for non-invariance

The paper explores invariant vs non-invariant complex structures on Lie groups.

problem Understanding complex structures on Lie groups and their properties.
method Analysis of invariant and non-invariant almost complex structures on compact quotients of Lie groups.
result New computations of Kodaira dimension for invariant and non-invariant structures.

The article classifies six-dimensional solvmanifolds with non-invariant trivializing sections of their canonical bundle.

problem Classifying six-dimensional solvmanifolds with non-invariant trivializing sections of their canonical bundle.
method Complete classification of six-dimensional solvable strongly unimodular Lie algebras admitting complex structures, identifying those with non-invariant holomorphic sections of their canonical bundle.
result Construction of a new six-dimensional solvmanifold with non-invariant holomorphic sections of its canonical bundle.

This work extends PAC-Bayesian learning guarantees to non-compact symmetries and non-invariant data.

problem Lack of theoretical guarantees explaining the benefits of symmetries in machine learning models.
method Adapting and tightening PAC-Bayes bounds for non-compact symmetries and non-invariant data distributions.
result Theoretical evidence that symmetric models are preferable for symmetric data, beyond compact groups and invariant distributions.

Left-invariant Cotton solitons on homogeneous manifolds are determined. Moreover, algebraic Cotton solitons are studied providing examples of non-invariant Cotton solitons, both in the Riemannian and Lorentzian homogeneous settings.

2013-03-15abs ↗pdf ↗

We present proofs of basic results, including those developed by Harold Bell, for the plane fixed point problem: does every map of a non-separating plane continuum have a fixed point? Some of these results had been announced much earlier by Bell but without accessible proofs. We define the concept of the variation of a…

2010-04-01abs ↗pdf ↗

The Wong-Rosay theorem characterizes the strongly pseudoconvex domains of Cn\mathbb{C}^n by their automorphism groups. It has a lot of generalizations to other kinds of domains (for example, the weakly pseudoconvex domains). However, most of them are for domains of Cn\mathbb{C}^n. In this note, we generalize the Wong-R…

2014-07-18abs ↗pdf ↗

We review some previous results about the Calabi-Yau equation on the Kodaira-Thurston manifold equipped with an invariant almost-Kaehler structure and assuming the volume form invariant by the action of a torus. In particular, we observe that under some restrictions the problem is reduced to a Monge-Ampère equation by …

2016-09-04abs ↗pdf ↗

Study on Kodaira dimension of SU(m)-structures on almost complex manifolds.

problem Understanding the Kodaira dimension of almost complex manifolds with SU(m)-structures.
method Introduced almost complex structure of splitting type and associated SU(m)-structure. Provided constructions for non-invariant almost complex structures with specific Kodaira dimensions.
result Found non-invariant almost complex structures with Kodaira dimensions 0 and -∞.

For generic torus-invariant metrics, eigenspaces are 2D and nodal sets are connected hypersurfaces.

problem Understanding spectral multiplicity and nodal sets for generic torus-invariant metrics.
method Analyzing real ΔgΔ_g-eigenspaces and nodal sets for generic TT-invariant metrics.
result For generic TT-invariant metrics, real ΔgΔ_g-eigenspaces are irreducible and have dimension at most 2, and nodal sets are connected hypersurfaces with specific properties.

Bayesian optimization gains efficiency by leveraging symmetries through a modified max kernel.

problem Improving Bayesian optimization efficiency for functions with group symmetries.
method Developed a PSD projection of the max kernel to exploit symmetries without violating kernel properties.
result The modified max kernel achieves lower regret compared to existing invariant and non-invariant kernels.

Researchers compute the ν-invariant for specific G2-structures on nilmanifolds.

problem Detecting connected components of G2-structure moduli spaces.
method Defined and computed the ν-invariant using Mathai-Quillen currents, harmonic spinors, and η-invariants.
result Determined the parity of harmonic spinor dimensions and deduced ν vanishing on invariant spinors.

This paper studies the generalization error of invariant classifiers. In particular, we consider the common scenario where the classification task is invariant to certain transformations of the input, and that the classifier is constructed (or learned) to be invariant to these transformations. Our approach relies on fa…

2016-10-14abs ↗pdf ↗

Study complex solvmanifolds with trivial canonical bundle and hypercomplex geometry.

problem Characterize and produce examples of complex solvmanifolds with trivial canonical bundle.
method Characterize invariant trivializing sections using Koszul 1-form, provide algebraic obstructions, and exhibit specific examples.
result New examples of complex solvmanifolds with trivial canonical bundle and algebraic obstructions for triviality.

Group-invariant neural networks improve approximation accuracy for symmetric functions.

problem Improving approximation accuracy for symmetric functions using neural networks.
method Investigates the generalization error of group-invariant neural networks within the Barron framework.
result Group invariance introduces a factor δ that can significantly improve approximation accuracy when it is small.

We construct metrics of positive scalar curvature on manifolds with circle actions. One of our main results is that there exist S1S^1-invariant metrics of positive scalar curvature on every S1S^1-manifold which has a fixed point component of codimension 2. As a consequence we can prove that there are non-invariant metr…

2013-05-10abs ↗pdf ↗

Monge SAM improves deep learning by making sharpness-aware minimization invariant to reparametrizations.

problem Non-invariance of sharpness-aware minimization (SAM) to reparametrizations.
method Introduces Monge SAM, a reparametrization-invariant version of SAM using a Riemannian metric.
result Monge SAM enhances robustness and generalization compared to previous methods.

Proposes an alternative invariance penalty to address domain generalization issues.

problem Addressing domain generalization problems by finding invariant representations.
method Revisits the Gramian matrix of the data representation to propose an alternative invariance penalty.
result The proposed approach guarantees recovery of an invariant representation under mild conditions.

Study shows how to reduce data needed for learning under geometric constraints.

problem Learning high-dimensional data with geometric priors.
method Spherical harmonic decompositions and kernel methods for invariance and geometric stability.
result Improvements in sample complexity by leveraging group invariance, with asymptotic behavior depending on spectral properties.

Reduces path integrals for interacting systems using dependent coordinates.

problem Reducing path integrals for systems with symmetry.
method Reduction procedure based on Wiener-type path integral, optimal nonlinear filtering, and projection of mean curvature vector field.
result Shows non-invariance of the measure in the path integral under reduction and generates the Jacobian.

Extends optimal regularity and compactness to vector bundles over non-Riemannian manifolds.

problem Optimal regularity and compactness for connections on vector bundles.
method Derive RT-equations, establish existence theory, handle curvature up to L1L^1.
result Optimal regularity and compactness extended to vector bundles over non-Riemannian manifolds.

Regularising for invariance to data augmentation improves machine learning models.

problem Improving generalization in machine learning models through data augmentation.
method Explicit regularisation to encourage invariance at the level of individual model predictions.
result Explicit regularisation improves generalization and equalizes performance differences between objectives.

In this paper, we study the analytic continuation to complex time of the Hamiltonian flow of certain G×TG\times T-invariant functions on the cotangent bundle of a compact connected Lie group GG with maximal torus TT. Namely, we will take the Hamiltonian flows of one G×GG\times G-invariant function, hh, and one $G\time…

2019-07-11abs ↗pdf ↗

Study on the parity of fold map singular points, showing non-invariance for odd-dimensional manifolds.

problem Parity of connected components of fold map singular points for odd-dimensional manifolds.
method Constructive proofs using open book decompositions, round fold maps, and allowable moves.
result Parity of connected components is not a homotopy invariant for odd-dimensional manifolds.

New geometric interpretation of Amari-Cencov α-connections on probability densities.

problem Geometric interpretation of Amari-Cencov α-connections on probability densities.
method Riemannian metrics and Levi-Civita connections.
result Geodesics of α-connections are energy-minimizing curves.

Consider an oriented four-dimensional Lorentzian manifold (M~3,1,g~)(\widetilde{M}^{3, 1}, \widetilde{g}) and an oriented seven-dimensional Riemannian manifold (M7,g)(M^{7}, g). We describe a class of decomposable eleven-dimensional supergravity backgrounds on the product manifold $({\mathcal{M}}^{10, 1}=\widetilde{M}^{3,1} \times…

2018-02-01abs ↗pdf ↗