arXiv research

Optimizes sliding window approach for tracking Gaussian densities.

Estimates Gaussian mixtures from weighted samples efficiently.

Study on manifolds with density using modified Hessians for curvature comparison.

We consider the $\mathfrak{aff}(n|1)-$module structure on the spaces of differential bilinear operators acting on the superspaces of weighted densities. We classify $\mathfrak{aff}(n|1)-$invariant binary differential operators acting on the spaces of weighted densities. This result allows us to compute the first $\math…

A new method improves flow matching by dynamically weighting density estimates.

Study shows robust method for estimating density ratios even with heavy contamination.

Hyperplanes, hyperspheres and hypercylinders in $\Bbb R^n$ with suitable densities are proved to be weighted minimizing by a calibration argument. Also calibration method is used to prove a weighted minimal hypersurface is weighted area-minimizing locally.

Optimizes kernel density ratios for better predictions and information measures.

Proves positive mass theorem for non-spin weighted manifolds.

Enhances mixture models with classifier-defined weights.

Study of $n$-ary differential operators on weighted densities with canonical symbol and quantization maps.

While robust parameter estimation has been well studied in parametric density estimation, there has been little investigation into robust density estimation in the nonparametric setting. We present a robust version of the popular kernel density estimator (KDE). As with other estimators, a robust version of the KDE is u…

Paper tackles unbounded density ratio estimation for covariate shift adaptation.

Paper proposes a method to estimate truncated density models using Score Matching.

Let $M$ be a weighted manifold with boundary $\partial M$, i.e., a Riemannian manifold where a density function is used to weight the Riemannian Hausdorff measures. In this paper we compute the first and the second variational formulas of the interior weighted area for deformations by hypersurfaces with boundary in $\p…

Paper proposes a new method for estimating conditional densities using logistic regressions.

We prove existence and uniqueness of weighted ambient metric for manifolds with density.

Over the $(1,n)$-dimensional real superspace, $n>1$, we classify $\mathcal{K}(n)$-invariant binary differential operators acting on the superspaces of weighted densities, where $\mathcal{K}(n)$ is the Lie superalgebra of contact vector fields. This result allows us to compute the first differential cohomology of %the L…

Paper proposes approximate Stein classes for efficient truncated density estimation.

Bayesian approach for multivariate density regression of complex data.

Proposes a new method for kernel density estimation using stagewise minimization and a simple dictionary.

Proposes a robust method for predicting missing outcomes in covariate shift adaptation.

New Stein operator improves robustness in model inference.

Spectral algorithms improve under covariate shift with novel weighted techniques.

Diffusion models adapt to low-dimensional structures for nonparametric density estimation.

In this paper we study sectional curvature bounds for Riemannian manifolds with density from the perspective of a weighted torsion free connection introduced recently by the last two authors. We develop two new tools for studying weighted sectional curvature bounds: a new weighted Rauch comparison theorem and a modifie…

New geometric analysis of PWSPDs balances density and geometry in high-dimensional data.

In this paper, we classify the class of constant weighted curvature curves in the plane with a log-linear density, or in other words, classify all traveling curved fronts with a constant forcing term in $\Bbb R^2.$ The classification gives some interesting phenomena and consequences including: the family of curves conv…

Paper proposes a robust LPR method using similarity kernels.

A new modularity density measure improves community detection in heterogeneous networks.

New isoperimetric inequalities in the plane with radial weights identified.

We consider a smooth Euclidean solid cone endowed with a smooth homogeneous density function used to weight Euclidean volume and hypersurface area. By assuming convexity of the cone and a curvature-dimension condition we prove that the unique compact, orientable, second order minima of the weighted area under variation…

A new method detects small holes in noisy data.

Develops conformal Bayes for two-sided censored Gaussian regression under label shift.

The paper proves new inequalities in hyperbolic space using Euclidean methods.

Generative model prices basket options efficiently.

TDX predicts evolving distributions from streaming data.

On a manifold with a projective connection we canonically assign a second order differential operator acting on the algebra of all densities to any tensor density $S^{ij}$ of fixed weight $λ$. In particular, this implies that on any projectively connected manifold, a `bracket' (symmetric biderivation) on the algebra of…

The study proves optimal isoperimetric regions in manifolds with density.

Study online monotone density estimation with expert aggregation and log-optimal calibration.

We consider odd Laplace operators acting on densities of various weight on an odd Poisson (= Schouten) manifold $M$. We prove that the case of densities of weight 1/2 (half-densities) is distinguished by the existence of a unique odd Laplace operator depending only on a point of an ``orbit space'' of volume forms. This…

The paper proves stability of certain graph types in Euclidean space with specific densities.

One computes the cohomology of the projective embedding of sl(m+1,R) acting on the differential operators on densities on R^m of various weights. This cohomology is non vanishing only for some special critical values of the weights. This allows us first to explain some strange feature pointed out by Gargoubi in his cla…

We prove the existence and the uniqueness of a conformally equivariant symbol calculus and quantization on any conformally flat pseudo-Riemannian manifold $(M,\rg)$. In other words, we establish a canonical isomorphism between the spaces of polynomials on $T^*M$ and of differential operators on tensor densities over $M…

A new tensor ring mixture model improves density estimation efficiency.

We consider differential operators acting on densities of arbitrary weights on manifold $M$ identifying pencils of such operators with operators on algebra of densities of all weights. This algebra can be identified with the special subalgebra of functions on extended manifold $\hat M$. On one hand there is a canonical…

Let $Δ$ be a linear differential operator acting on the space of densities of a given weight $\lo$ on a manifold $M$. One can consider a pencil of operators $\hPi(Δ)=\{Δ_ł\}$ passing through the operator $Δ$ such that any $Δ_ł$ is a linear differential operator acting on densities of weight $ł$. This pencil can be iden…

Neural networks have been widely used as predictive models to fit data distribution, and they could be implemented through learning a collection of samples. In many applications, however, the given dataset may contain noisy samples or outliers which may result in a poor learner model in terms of generalization. This pa…