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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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4793140186 · May 202619922001200920172026
48 results for double orthogonal projection

CP-factorization for high-dimensional tensor time series and double projection iterations

problem Identifying and estimating factor loadings in CP decomposition for high-dimensional tensor time series
method One-pass estimation procedure using standard eigen-analysis for matrix constructed based on serial dependence
result Asymptotic properties established under general settings, adapt to sparsity, accommodates weak factors

Paper links set derivatives to its orthogonal projections.

problem Understanding the relationship between set derivatives and projections.
method Derives equations from topological link between Minkowski functional partial derivatives and set boundary.
result System of equations for orthogonal projections derived.

We establish Marstrand-type projection theorems for orthogonal projections along geodesics onto m-dimensional subspaces of hyperbolic nn-space by a geometric argument. Moreover, we obtain a Besicovitch-Federer type characterization of purely unrectifiable sets in terms of these hyperbolic orthogonal projections.

2018-07-30abs ↗pdf ↗

We construct a decomposition of the identity operator on a Riemannian manifold MM as a sum of smooth orthogonal projections subordinate to an open cover of MM. This extends a decomposition of the real line by smooth orthogonal projection due to Coifman, Meyer and Auscher, Weiss, Wickerhauser, and a similar decomposit…

2018-03-09abs ↗pdf ↗

Double descent observed in tree-based models for genomic prediction.

problem Understanding the generalization behavior of tree-based models in machine learning.
method Systematic variation of model complexity in a genomic prediction task using whole-genome sequencing data.
result Double descent emerges only when complexity is scaled jointly across learner capacity and ensemble size.

Paper introduces GDR-learners for estimating potential outcomes from observational data.

problem Lack of theoretical property of general Neyman-orthogonality in deep generative models.
method Develops flexible GDR-learners based on various deep generative models.
result GDR-learners possess quasi-oracle efficiency and rate double robustness, asymptotically optimal.

This paper tabulates prime knot projections up to eight double points.

problem Tabulating prime knot projections and their mirror images up to a certain number of double points.
method Systematic flypes and enumeration of tangles with at most four double points, using arrow diagrams.
result Complete table of prime knot projections with their mirror images up to eight double points.

Study shows double descent curve in high-dimensional linear regression with random projections.

problem Understanding the generalization performance in high-dimensional settings with random projections.
method Fixed prediction problem, ridge regression estimator, minimum norm least-squares fit, random matrix theory, asymptotic equivalents.
result Exhibit a double descent curve for high-dimensional linear regression with random projections.

Principal component analysis (PCA) is an unsupervised method for learning low-dimensional features with orthogonal projections. Multilinear PCA methods extend PCA to deal with multidimensional data (tensors) directly via tensor-to-tensor projection or tensor-to-vector projection (TVP). However, under the TVP setting, i…

2015-04-30abs ↗pdf ↗

New convergence guarantees for learning with unknown nuisance parameters.

problem Learning problems with unknown nuisance parameters.
method Stochastic gradient optimization with Neyman orthogonality and approximately orthogonalized updates.
result Stochastic gradient algorithms can converge under conditions of nuisance parameters.

Let n be aninteger>4. There is a smoothly knotted n-dimensional sphere in (n+2)-space such that the singular point set of its projection in (n+1)-space consists of double points and that the components of the singular point set are two. (The sphere is knotted in the sense that it does not bound any embedded (n+1)-ball …

2018-03-08abs ↗pdf ↗

The paper uses double machine learning to estimate dynamic treatment effects robustly.

problem Estimating causal effects of dynamic treatments with time-varying covariates.
method Double machine learning with Neyman-orthogonal score functions for robustness.
result Asymptotic normality and n\sqrt{n}-consistency of the estimators under specific conditions.

Double machine learning provides n\sqrt{n}-consistent estimates of parameters of interest even when high-dimensional or nonparametric nuisance parameters are estimated at an n1/4n^{-1/4} rate. The key is to employ Neyman-orthogonal moment equations which are first-order insensitive to perturbations in the nuisance param…

2017-11-01abs ↗pdf ↗

In this paper known results of symmetric orthogonality, as introduced by G. Birkhoff, and non-expansive nearest point projections are extended from the linear to the metric setting. If the space has non-positive curvature in the sense Busemann then it is shown that those concepts are actually equivalent. In the end it …

2016-04-07abs ↗pdf ↗

GOPO optimizes large models in Hilbert space, avoiding Kullback-Leibler's curvature.

problem Optimizing large language models with Kullback-Leibler divergence's curvature issues.
method GOPO uses Hilbert space L2(pi_k) with orthogonality constraints and a work-dissipation functional.
result GOPO achieves competitive generalization with stable gradient dynamics and entropy preservation.

Method constructs orthogonal curvilinear coordinates in constant curvature spaces.

problem Creating orthogonal coordinates in spaces of constant curvature.
method Modification of Krichever's method for Euclidean space, applied to constant curvature spaces.
result Examples of orthogonal coordinate systems on the sphere and hyperbolic plane constructed.

A method for interpreting SVMs using polynomial kernels, revealing model complexity.

problem Interpreting SVMs built with truncated orthogonal polynomial kernels.
method Orthogonal Representation Contribution Analysis (ORCA) with normalized Orthogonal Kernel Contribution (OKC) indices.
result The method reveals structural aspects of model complexity not captured by predictive accuracy.

We study the relations between the quaternion HH-type group and the boundary of the unit ball on two dimensional quaternionic space. The orthogonal projection of the space of square integrable functions defined on quaternion HH-type group into its subspace of boundary values of qq-holomorphic functions is consider. …

2006-10-02abs ↗pdf ↗

The paper studies geometric properties of Grassman manifolds within Euclidean spaces.

problem Understanding the geometric structure of Grassman manifolds.
method Analyzing Grassman manifold G(E)G(E) as a subset of Euclidean space EE and orthogonal projections.
result Explicit formulas for differential geometry of G(E)G(E) as a submanifold.

Soft-Radial Projection solves gradient saturation in constrained deep learning.

problem Gradient saturation in deep learning models when integrating hard constraints.
method Introduces Soft-Radial Projection, a differentiable layer that maps predictions onto constraint boundaries without rank-deficient Jacobians.
result Improves convergence and solution quality over state-of-the-art methods.

Let HH be a hypersurface in Rn\mathbb R^n and let ππ be an orthogonal projection in Rn\mathbb R^n restricted to HH. We say that HH satisfies the ArchimedeanArchimedean projectionprojection propertyproperty corresponding to ππ if there exists a constant CC such that Vol(π1(U))=CVol(U)Vol(π^{-1}(U)) = C \cdot Vol(U) for every measurable UU in the range…

2015-04-12abs ↗pdf ↗

It has long been known to mathematicians and physicists that while a full rotation in three-dimensional Euclidean space causes tangling, two rotations can be untangled. Formally, an untangling is a based nullhomotopy of the double-twist loop in the special orthogonal group of rotations. We study a particularly simple, …

2016-10-15abs ↗pdf ↗

A Margulis spacetime is a complete flat affine Lorentzian 3-manifold with free fundamental group. Associated to MM is a noncompact complete hyperbolic surface ΣΣ. We study double extensions of π1(M)π1(Σ)π_1 (M) \cong π_1 (Σ) when ΣΣ is homeomorphic to a projective plane minus two discs. We classify proper actions of this do…

2015-11-17abs ↗pdf ↗

We prove that the set of orthogonal separable coordinates on an arbitrary (pseudo-)Riemannian manifold carries a natural structure of a projective variety, equipped with an action of the isometry group. This leads us to propose a new, algebraic geometric approach to the classification of orthogonal separable coordinate…

2015-10-30abs ↗pdf ↗

First, we review the Dirac operator folklore about basic analytic and geometrical properties of operators of Dirac type on compact manifolds with smooth boundary and on closed partitioned manifolds and show how these properties depend on the construction of a canonical invertible double and are related to the concept o…

2008-03-28abs ↗pdf ↗

We prove that a foliation (M,F)(M, F) of codimension qq on a nn-dimen\-sio\-nal pseudo-Riemannian manifold is pseudo-Riemannian if and only if any geodesic that is orthogonal at one point to a leaf is orthogonal to every leaf it intersects. We show that on the graph G=G(F)G = G(F) of a pseudo-Riemannian foliation there exis…

2016-11-27abs ↗pdf ↗

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 ↗

The paper creates a deformation retraction for homeomorphisms of the projective plane.

problem Deformation retraction of homeomorphisms of the projective plane.
method Equivariant strong deformation retraction from homeomorphism group to special orthogonal group.
result Induces a SO(3)-equivariant strong deformation retraction from projective plane homeomorphisms to SO(3).

We develop new algebraic methods refining the Witt group of linking forms and Ranicki's torsion algebraic L-groups into double Witt groups and double L-groups. At each prime ideal of the underlying ring, our double Witt groups capture infinitely many more integral signatures of the linking form than the single Witt gro…

2015-03-24abs ↗pdf ↗