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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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112224335447 · Jun 202019922001200920182026
48 results for refined proof techniques

Paper extends distributed learning with random features to non-attainable cases.

problem Generalization properties for distributed learning with random features under non-attainable conditions.
method Refined proof techniques, data-dependent generating strategy, additional unlabeled data.
result Remarkably reduces computational cost while preserving optimal generalization accuracy.

New Orlicz Brunn-Minkowski inequalities are established for rigid motion compatible Minkowski valuations of arbitrary degree. These extend classical log-concavity properties of intrinsic volumes and generalize seminal results of Lutwak and others. Two different approaches which refine previously employed techniques are…

2014-11-28abs ↗pdf ↗

We prove that the knot Floer homology of a fibered knot is nontrivial in its next-to-top Alexander grading. Immediate applications include new proofs of Krcatovich's result that knots with LL-space surgeries are prime and Hedden and Watson's result that the rank of knot Floer homology detects the trefoil among knots i…

2018-01-19abs ↗pdf ↗

We prove a geometric refinement of Alexander duality for certain 2-complexes, the so-called gropes, embedded into 4-space. This refinement can be roughly formulated as saying that 4-dimensional Alexander duality preserves the disjoint Dwyer filtration. In addition, we give new proofs and extended versions of two lemmas…

1997-05-28abs ↗pdf ↗

We first apply the method and results in the previous paper to give a new proof of a result (hold in C/Z {\bf C}/{\bf Z}) of Gilkey on the variation of h-invariants associated to non self-adjoint Dirac type operators. We then give an explicit local expression of certain h-invariant appearing in recent papers of Braverma…

2006-04-16abs ↗pdf ↗

New research shows label refinement and weak training have limitations for aligning LLMs.

problem Limitations of refinement methods for aligning large language models.
method Analyzed probabilistic assumptions and alternative approaches to label refinement and weak training.
result Label refinement and weak training suffer from irreducible error, leaving a performance gap.

Improved Nyström approximation for kernel quadrature with theoretical guarantees.

problem Efficiently approximating positive definite kernels for large datasets.
method Refined sampling and subspace selection in Nyström approximation.
result Novel theoretical guarantees for non-i.i.d. landmark points in kernel quadrature.

Debona improves neural network verification by faster and tighter bounds.

problem Proving adversarial robustness of neural networks is computationally hard.
method Decouples upper and lower bounds computation, re-implements Neurify.
result 94% reduction in runtime for proving robustness of max-pooling layers.

We study bounded pseudoconvex domains in complex Euclidean space. We define an index associated to the boundary and show this new index is equivalent to the Diederich-Fornæss index defined in 1977. This connects the Diederich-Fornæss index to boundary conditions and refines the Levi pseudoconvexity. We also prove the $…

2017-01-01abs ↗pdf ↗

Process mining is a research field focused on the analysis of event data with the aim of extracting insights in processes. Applying process mining techniques on data from smart home environments has the potential to provide valuable insights in (un)healthy habits and to contribute to ambient assisted living solutions. …

2016-09-12abs ↗pdf ↗

New proof of Giroux Correspondence for tight contact 3-manifolds.

problem Proving the Giroux Correspondence for tight contact 3-manifolds.
method Introducing tight Heegaard splittings, using refinement process, and translating moves between splittings to moves between open books.
result Proves the tight Giroux Correspondence for contact 3-manifolds.

Paper improves TD learning algorithm bounds with linear approx.

problem Sharp bounds for TD method performance in MDPs.
method Polyak-Ruppert averaging, universal step size, refined error bounds, stability of random matrices.
result Near-optimal variance and bias terms achieved.

We refine estimates introduced by Balogh and Bonk, to show that the boundary extensions of isometries between smooth strongly pseudoconvex domains in $\C^n$ are conformal with respect to the sub-Riemannian metric induced by the Levi form. As a corollary we obtain an alternative proof of a result of Fefferman on smooth …

2017-03-01abs ↗pdf ↗

We refine Osserman's argument on the exceptional values of the Gauss map of algebraic minimal surfaces. This gives an effective estimate for the number of exceptional values and the totally ramified value number for a wider class of complete minimal surfaces that includes algebraic minimal surfaces. It also provides a …

2005-11-22abs ↗pdf ↗

Study uses AI to refine loan assessments, improving credit default predictions.

problem Improving credit default prediction accuracy using AI-refined text.
method Comparative analysis of human-written and AI-refined loan assessments using deep learning techniques.
result AI-refined texts significantly enhance credit default predictions, especially when combined with structured data.

We proof existence theorems for the Dirichlet problem for hypersurfaces of constant special Lagrangian curvature in Hadamard manifolds. The first results are obtained using the continuity method and approximation and then refined using two iterations of the Perron method. The a-priori estimates used in the continuity m…

2009-08-25abs ↗pdf ↗

Bismut and Zhang computed the ratio of the Ray-Singer and the combinatorial torsions corresponding to non-unitary representations of the fundamental group. In this note we show that for representations which belong to a connected component containing a unitary representation the Bismut-Zhang formula follows rather easi…

2013-04-19abs ↗pdf ↗

Researchers prove existence of metrics maximizing Laplace eigenvalue on all closed surfaces.

problem Proving the existence of metrics maximizing the first Laplace eigenvalue on closed surfaces.
method By contradiction and refinement of techniques, proving strict monotonicity under surface modifications.
result Existence of metrics maximizing the area-normalized first eigenvalue on all closed surfaces.

New vanishing theorems for harmonic and pluriharmonic functions on Kähler and quaternionic Kähler manifolds.

problem Vanishing theorems for harmonic and pluriharmonic functions on Kähler and quaternionic Kähler manifolds.
method Utilized refined Kato type inequalities and Böchner technique to generalize results to LpL^p-integrable pluriharmonic functions and harmonic 1-forms.
result Proved vanishing property of pluriharmonic functions with finite LpL^p energy on complete Kähler manifolds.

This work generates synthetic 3D thermal facial data using 2D facial data and deep learning.

problem Creating large datasets for deep learning in computer vision.
method 3D facial modelling techniques and deep learning methodologies.
result Synthetic 3D thermal facial data created for deep learning applications.

New geometric proofs and interpretations of scattering diagrams and theta functions.

problem Analyzing the asymptotic behavior of Maurer-Cartan elements for differential graded Lie algebras.
method Asymptotic analytic approach and differential geometric proofs.
result Alternative proofs of consistent completion of scattering diagrams and geometric interpretations of theta functions.

Researchers refine local rigidity for marked length spectrum and introduce a new pressure metric.

problem Local rigidity of marked length spectrum and related metrics.
method Refined local rigidity result using geodesic stretch and Anosov flows, introduced new pressure metric.
result New pressure metric related to Weil-Peterson metric, reduces to it in Teichmüller space.

This article studies the achievable guarantees on the error rates of certain learning algorithms, with particular focus on refining logarithmic factors. Many of the results are based on a general technique for obtaining bounds on the error rates of sample-consistent classifiers with monotonic error regions, in the real…

2015-12-22abs ↗pdf ↗

We refine some classical estimates in Seiberg-Witten theory, and discuss an application to the spectral geometry of three-manifolds. In particular, we show that on a rational homology three-sphere YY, for any Riemannian metric the first eigenvalue of the laplacian on coexact one-forms is bounded above explicitly in te…

2017-05-24abs ↗pdf ↗

LARA forecasts financial asset trends by refining noisy labels and extracting profitable samples.

problem Low signal-to-noise ratio and stochastic nature of financial data lead to poor predictions.
method LARA combines LA-Attention and RA-Labeling to refine and extract profitable samples.
result LARA significantly outperforms existing methods on Qlib platform.