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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.

169,051 papers · 148 categories

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48 results for vector-valued one-forms

Introduces a metric on vector-valued one-forms for functional data analysis.

problem Metric on vector-valued one-forms for functional data analysis.
method Diffeomorphism-invariant Riemannian metric calculation and geodesic equations.
result Geodesically and metrically incomplete space with specific curvature properties.

Unique continuation for X-ray transforms of one-forms with partial data.

problem Proving unique continuation for X-ray transforms of one-forms with limited data.
method Proved unique continuation for the normal operator of X-ray transforms of one-forms, leading to partial data results.
result Unique continuation for X-ray transforms of one-forms with partial data.

The paper examines parallel one forms on Riemannian and Finslerian manifolds.

problem Existence of parallel one forms on Riemannian and Finslerian manifolds.
method Using Finslerian settings, the paper investigates the existence of parallel one forms on Riemannian manifolds and Finslerian manifolds, proving conditions for their existence and non-existence.
result Conditions for the existence and non-existence of parallel one forms on Riemannian and Finslerian manifolds.

The paper extends consistency results for sequential design strategies to vector-valued Gaussian processes.

problem Estimating excursion sets of vector-valued Gaussian processes.
method Clarifying the connection between continuous Gaussian processes and Gaussian measures in Banach spaces, extending concepts and properties from scalar-valued settings to vector-valued settings.
result Consistency results for sequential design strategies can be applied to vector-valued Gaussian processes.

Optimal rates for vector-valued regression on various norms.

problem Optimal rates for vector-valued ridge regression on continuous norms.
method Combining standard capacity assumptions with tensor product constructions of vector-valued interpolation spaces.
result Optimal rates for vector-valued ridge regression, independent of output space dimension.

Boosting framework for vector-valued prediction with geometric stability.

problem Lack of a general theoretical understanding of aggregation for structured prediction.
method Identifies (α,β)(α,β)-stability property and proposes a boosting framework based on exponential reweighting and geometric-median aggregation.
result Obtains exponential decay of empirical divergence error under weak learner condition and (α,β)(α,β)-stability.

Study confirms learning rates for vector-valued spectral algorithms, proving consistency.

problem Theoretical confirmation of learning rates for vector-valued spectral algorithms.
method Rigorous analysis of learning rates for various vector-valued spectral algorithms, including kernel ridge regression and gradient descent.
result Upper and lower bounds on learning rates for vector-valued spectral algorithms, proving minimax optimality in various scenarios.

The space of vector-valued forms on any manifold is a graded Lie algebra with respect to the Frolicher-Nijenhuis bracket. In this paper we consider multiplicative vector-valued forms on Lie groupoids and show that they naturally form a graded Lie subalgebra. Along the way, we discuss various examples and different char…

2017-06-02abs ↗pdf ↗

Study proves Kotschick's conjecture for certain compact Kähler manifolds.

problem Proving a conjecture about one-forms without zeros on compact Kähler manifolds.
method Using a conjecture about homologically trivial fibrations and properties of Albanese torus.
result Proves Kotschick's conjecture for specific compact Kähler manifolds.

This study improves graph signal denoising for vector-valued data with non-convex penalties.

problem Denoising piecewise smooth graph signals with varying smoothness levels.
method Extended graph trend filtering with non-convex penalties and ADMM algorithm.
result Non-convex penalties outperform convex ones in recovery performance.

The paper proposes methods to find a shared active subspace for multivariate vector-valued functions.

problem Minimizing the deviation between function evaluations in the original and reconstructed spaces.
method Manipulating gradients or SPD matrices to identify a shared structure.
result Summing SPD matrices often identifies the best shared active subspace.

Optimal transport for vector Gaussian mixtures improves efficiency and structure preservation.

problem Optimal mass transport for vector-valued Gaussian mixtures.
method Vectorizing Gaussian mixture models and studying optimal mass transport problems.
result Computational efficiency and structure preservation in optimal mass transport.

No arbitrage holds if a Pareto solution exists for vector-valued utility maximization.

problem Existence of no arbitrage in markets with transaction costs and multiple assets.
method Prove no arbitrage condition equivalent to Pareto solution for vector-valued utility maximization.
result A consistent price process can be constructed from the Pareto maximizer.

We present a framework to derive risk bounds for vector-valued learning with a broad class of feature maps and loss functions. Multi-task learning and one-vs-all multi-category learning are treated as examples. We discuss in detail vector-valued functions with one hidden layer, and demonstrate that the conditions under…

2016-06-05abs ↗pdf ↗

In this paper, we study general (α,β)(α,β)-metrics which αα is a Riemannian metric and ββ is an one-form. We have proven that every weak Landsberg general (α,β)(α,β)-metric is a Berwald metric, where ββ is a closed and conformal one-form. This show that there exist no generalized unicorn metric in this class of general $(…

2017-06-13abs ↗pdf ↗

Study improves self-normalized bounds for vector-valued processes beyond sub-Gaussianity.

problem Limited understanding of self-normalized concentration for vector-valued processes outside sub-Gaussian frameworks.
method Developed concentration inequalities for self-normalized processes with light tails (e.g., Bennett, Bernstein bounds) for vector-valued data.
result Provided new insights and bounds for self-normalized processes with non-sub-Gaussian distributions.

We approximate derivatives of functions on manifolds by embedding them and applying vector-valued operators.

problem Derivatives of manifold-valued functions are harder to approximate than vector-valued functions.
method Embed the manifold into a higher space, approximate the derivative of the vector-valued function, and project back.
result We provide error bounds for the approximation of manifold-valued function derivatives.

Paper introduces vector-valued variation spaces for multi-output neural networks.

problem Understanding and optimizing multi-output neural networks.
method Development of vector-valued variation spaces and representer theorem.
result Novel bounds for layer widths in deep networks and a convex optimization method for compression.

An anologue of the Calabi invariant for Poisson manifolds is considered. For any Poisson manifold PP, the Poisson bracket on C(P)C^{\infty}(P) extends to a Lie bracket on the space Ω1(P)Ω^{1}(P) of all differential one-forms, under which the space Z1(P)Z^{1}(P) of closed one-forms and the space B1(P)B^{1}(P) of exact one-forms a…

1996-05-05abs ↗pdf ↗

Paper analyzes error bounds for learning with vector-valued RF, improving existing analyses.

problem Learning with vector-valued random features in infinite-dimensional settings.
method Direct analysis of risk functional, avoiding random matrix theory.
result Strong consistency and minimax optimal convergence rates established.

Randomized algorithm solves vector-valued regression problems with low-rank operators.

problem Vector-valued regression problems involving infinite-dimensional spaces.
method Randomized Reduced Rank Regression (R4) using Gaussian sketching for optimization.
result R4 estimators are efficient and accurate, with empirical risk close to optimal.

Short note proves Poincaré inequality for 4-manifold forms.

problem Quantifying Poincaré inequality for one forms on 4-manifolds.
method Hodge theory on orbifolds, comparison of fundamental groups, spectral convergence, degeneration to orbifolds.
result First non-trivial global Poincaré inequality without higher curvature assumptions.

We discuss the Morse-Novikov cohomology of a compact manifold, associated to a closed one--form whose free abelian group generated by its periods γη[γ]π1(M)\langle \int_γη\mid [γ] \in π_1(M)\rangle is of rank 1, the focus being on locally conformally symplectic manifolds. In particular, we provide an explicit computation for t…

2016-07-06abs ↗pdf ↗

Develops vector-valued RKBS for neural networks and operators.

problem Understanding function spaces of Rd\mathbb{R}^d-valued neural networks and neural operators.
method Defines and constructs vector-valued RKBS (vv-RKBS) without restrictive assumptions.
result Establishes Representer Theorem for neural architectures.

In this paper, we prove that the zero-locus of any global holomorphic log-one-form on a projective log-smooth pair (X,D)\left(X,D\right) of log-general type must be non-empty. Applying this result, we give an answer to the algebraic hyperbolicity part of Shafarevich's conjecture, with the generic fiber being Kawamata-log-…

2017-11-15abs ↗pdf ↗

Extends spectral Einstein functionals computation to 4D spin manifolds with boundary.

problem Computing spectral Einstein functionals for 4D spin manifolds with boundary.
method Generalizes Dabrowski's results to 4D spin manifolds with boundary using noncommutative residue.
result Generalized spectral Einstein functionals computation for 4D spin manifolds with boundary.

In this paper, we introduce the notion of one form deformation of sprays. The metrizability of the new spray, when the background spray is flat, is characterized. Therefore, we obtain new projectively flat metrics of constant flag curvature 11. Moreover, these new metrics are not, generally, isometric to the Klein met…

2017-11-25abs ↗pdf ↗