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.
LOFT separates subspace rotation and transformation for orthogonal fine-tuning.
An algorithm simplifies optimization with nonnegative and orthogonal constraints.
New findings on Kähler manifolds restrict orthogonal coordinates existence.
A method for interpreting SVMs using polynomial kernels, revealing model complexity.
Orthogonal random features approximate a Bessel kernel, offering sharper bounds than random Fourier features.
Calibration in 16D disproves Federer's product question.
Given a convex set and an interior point close to the boundary, we prove the existence of a supporting hyperplane whose distance to the point is controlled, in a dimensionally quantified way, by the thickness of the convex set in the orthogonal direction. This result has important applications in the regularity theory …
New design method improves Lasso performance in sparse regression.
Distributed-OMP recovers sparse vectors with low communication costs.
Optimizes SGD for anytime neural networks, improving accuracy.
Orthogonium offers unified, efficient layers for robust deep learning.
Cyclidic nets are introduced as discrete analogs of curvature line parametrized surfaces and orthogonal coordinate systems. A 2-dimensional cyclidic net is a piecewise smooth -surface built from surface patches of Dupin cyclides, each patch being bounded by curvature lines of the supporting cyclide. An explicit de…
OOMP selects features online for sparse linear regression.
Study curvature invariants in sub-Riemannian manifolds.
Method estimates heterogeneous causal effects on networks using orthogonal learning.
We consider the Orthogonal Least-Squares (OLS) algorithm for the recovery of a -dimensional -sparse signal from a low number of noisy linear measurements. The Exact Recovery Condition (ERC) in bounded noisy scenario is established for OLS under certain condition on nonzero elements of the signal. The new result a…
New method solves sparse PCA for multiple components efficiently.
Confocal quadrics lie at the heart of the system of confocal coordinates (also called elliptic coordinates, after Jacobi). We suggest a discretization which respects two crucial properties of confocal coordinates: separability and all two-dimensional coordinate subnets being isothermic surfaces (that is, allowing a con…
Paper analyzes adaptive Lasso for high-dimensional diffusion processes, improving support recovery and bias.
msPCA solves sparse PCA for multiple components efficiently.
Orthogonal matching pursuit (OMP) is a widely used algorithm for recovering sparse high dimensional vectors in linear regression models. The optimal performance of OMP requires \textit{a priori} knowledge of either the sparsity of regression vector or noise statistics. Both these statistics are rarely known \textit{a p…
We prove that all smooth sphere bundles that admit fiberwise 1/4-pinched metrics are induced bundles of vector bundles, so their structure groups reduce from the diffeomorphism group of the sphere to the orthogonal group. This result implies the existence of many smooth n-sphere bundles over a k-sphere that do not supp…
VAR-GPs solve continual learning by updating posteriors sequentially.
New PHO formula improves SSNs performance.
Paper characterizes umbilical hypersurfaces using a generalized overdetermined problem.
Synthetic construction of Hopf fibration in 4D space.
Spectral features of the empirical moment matrix constitute a resourceful tool for unveiling properties of a cloud of points, among which, density, support and latent structures. It is already well known that the empirical moment matrix encodes a great deal of subtle attributes of the underlying measure. Starting from …
MOBO-OSD optimizes multi-objective functions using orthogonal search directions.
Simultaneous orthogonal matching pursuit (SOMP) and block OMP (BOMP) are two widely used techniques for sparse support recovery in multiple measurement vector (MMV) and block sparse (BS) models respectively. For optimal performance, both SOMP and BOMP require \textit{a priori} knowledge of signal sparsity or noise vari…
Our aim is to support the choice of two remarkable connections with torsion in a 3-Sasakian manifold, proving that, in contrast to the Levi-Civita connection, the holonomy group in the homogeneous cases reduces to a proper subgroup of the special orthogonal group, of dimension considerably smaller. We realize the compu…
In this paper we solve support vector machines in reproducing kernel Banach spaces with reproducing kernels defined on nonsymmetric domains instead of the traditional methods in reproducing kernel Hilbert spaces. Using the orthogonality of semi-inner-products, we can obtain the explicit representations of the dual (nor…
Novel tensor perturbation bounds for orthogonal iteration methods.
Recovering the support of sparse vectors in underdetermined linear regression models, \textit{aka}, compressive sensing is important in many signal processing applications. High SNR consistency (HSC), i.e., the ability of a support recovery technique to correctly identify the support with increasing signal to noise rat…
The problem of estimating sparse eigenvectors of a symmetric matrix attracts a lot of attention in many applications, especially those with high dimensional data set. While classical eigenvectors can be obtained as the solution of a maximization problem, existing approaches formulate this problem by adding a penalty te…
In this paper, we propose the use of a black-box optimization method called deterministic Mesh Adaptive Direct Search (MADS) algorithm with orthogonal directions (Ortho-MADS) for the selection of hyperparameters of Support Vector Machines with a Gaussian kernel. Different from most of the methods in the literature that…
FoLDTree improves oblique decision trees with ULDA, enhancing accuracy and feature selection.
Given a geodesic inside a simply-connected, complete, non-positively curved Riemannian (NPCR) manifold M, we get an associated geodesic inside the asymptotic cone Cone(M). Under mild hypotheses, we show that if the latter is contained inside a bi-Lipschitz flat, then the original geodesic supports a non-trivial, orthog…
This paper provides estimation and inference methods for an identified set's boundary (i.e., support function) where the selection among a very large number of covariates is based on modern regularized tools. I characterize the boundary using a semiparametric moment equation. Combining Neyman-orthogonality and sample s…
New paradigm for Neural ODEs stabilizes training and improves model performance.
The study reveals how adversarial perturbations can include class features for generalization.
TSL learns separable models to avoid signal cancellation and off-support extrapolation.
The paper tackles CF in CL by analyzing NTK overlap matrix and proposing OGD.
The study extends Jacobi-orthogonality to indefinite scalar product spaces.
In this paper, we consider the problem of compressed sensing where the goal is to recover almost all the sparse vectors using a small number of fixed linear measurements. For this problem, we propose a novel partial hard-thresholding operator that leads to a general family of iterative algorithms. While one extreme of …
New characterization of Osserman tensors using Jacobi-orthogonality.
Study isotropy groups for complex orthogonal and skew-symmetric matrices.
In this paper we deal with relative normalizations of hypersurfaces in the (n+1)-dimensional Euclidean space . Considering a relative normalization of an hypersurface we decompose the corresponding Tchebychev vector in two components, one parallel to the Tchebychev vector $\bar…
Gradient-enhanced GSA uses Poincaré chaos expansions for accurate sensitivity analysis.