arXiv research

Study exact minimax rates for density estimation over convex classes, extending previous work.

As one type of efficient unsupervised learning methods, clustering algorithms have been widely used in data mining and knowledge discovery with noticeable advantages. However, clustering algorithms based on density peak have limited clustering effect on data with varying density distribution (VDD), equilibrium distribu…

The volume density of a hyperbolic link is defined as the ratio of hyperbolic volume to crossing number. We study its properties and a closely-related invariant called the determinant density. It is known that the sets of volume densities and determinant densities of links are dense in the interval [0,v_{oct}]. We cons…

Study finds a linear lower bound on conformal dimension for random hyperbolic groups.

Chia and Nakano (2009) introduced the concept of M-decomposability of probability densities in one-dimension. In this paper, we generalize M-decomposability to any dimension. We prove that all elliptical unimodal densities are M-undecomposable. We also derive an inequality to show that it is better to represent an M-de…

Defines hierarchical clustering axioms for various densities.

Quantum computers outperform classical methods in density modeling.

We examine the vertical component of surface area in the warped product of a Euclidean interval and a fiber manifold with product density. We determine general conditions under which vertical fibers minimize vertical surface area among regions bounding the same volume and use these results to conclude that in many such…

New density estimator from Markov Chains outperforms KDE.

We propose a novel approach for density estimation with exponential families for the case when the true density may not fall within the chosen family. Our approach augments the sufficient statistics with features designed to accumulate probability mass in the neighborhood of the observed points, resulting in a non-para…

New binary loss functions improve density ratio estimation accuracy.

Quantum method improves neural density estimation in high dimensions.

Paper develops estimators for unbounded density ratios with applications in error control.

Paper introduces MoM-KDE for robust density estimation robust to anomalous data.

Study on stable Hamiltonian topology finds non-density of certain structures.

This paper focuses on density-based clustering, particularly the Density Peak (DP) algorithm and the one based on density-connectivity DBSCAN; and proposes a new method which takes advantage of the individual strengths of these two methods to yield a density-based hierarchical clustering algorithm. Our investigation be…

Study refracted skew Brownian motion, find densities and asymptotics.

New model for density estimation using tensor trains.

Estimates copula density for complex data distributions.

We study the isoperimetric problem for Euclidean space endowed with a continuous density. In dimension one, we characterize isoperimetric regions for a unimodal density. In higher dimensions, we prove existence results and we derive stability conditions, which lead to the conjecture that for a radial log-convex density…

Combines coarse learners for nonparametric probabilistic regression.

Paper improves speech separation by using deep neural networks for more accurate density priors.

The paper analyzes kNN density estimation's convergence rates under different conditions.

New method efficiently interpolates nonparametric density estimators.

Proposes a new method for high-dimensional density estimation.

Given a nonlinear model, a probabilistic forecast may be obtained by Monte Carlo simulations. At a given forecast horizon, Monte Carlo simulations yield sets of discrete forecasts, which can be converted to density forecasts. The resulting density forecasts will inevitably be downgraded by model mis-specification. In o…

Density destructors simplify complex PDFs to maximize entropy, linking to information theory.

We investigate the ability of popular flow based methods to capture tail-properties of a target density by studying the increasing triangular maps used in these flow methods acting on a tractable source density. We show that the density quantile functions of the source and target density provide a precise characterizat…

Proposes SD-KDE for density estimation using debiased kernel density with score-based adjustments.

Transforms conditional density estimation into a nonparametric regression problem.

A normalizing flow models a complex probability density as an invertible transformation of a simple density. The invertibility means that we can evaluate densities and generate samples from a flow. In practice, autoregressive flow-based models are slow to invert, making either density estimation or sample generation sl…

This paper presents a method for efficient density estimation in nonlinear systems.

Paper tackles unbounded density ratio estimation for covariate shift adaptation.

Mean shift clustering finds the modes of the data probability density by identifying the zero points of the density gradient. Since it does not require to fix the number of clusters in advance, the mean shift has been a popular clustering algorithm in various application fields. A typical implementation of the mean shi…

Paper tackles privacy-preserving data density issues using deconvolution.

This paper studies sparse density estimation via $\ell_1$ penalization (SPADES). We focus on estimation in high-dimensional mixture models and nonparametric adaptive density estimation. We show, respectively, that SPADES can recover, with high probability, the unknown components of a mixture of probability densities an…

We show a general relation between the spatially disjoint product of probability density functions and the sum of their Fisher information metric tensors. We then utilise this result to give a method for constructing the probability density functions for an arbitrary Riemannian Fisher information metric tensor. We note…

In $\mathbb{R}^n$ with a density $e^ψ$, we study the mean curvature flow associated to the density ($ψ$-mean curvature flow or $ψ$MCF) of a hypersurface. The main results concern with the description of the evolution under $ψ$MCF of a closed embedded curve in the plane with a radial density, and with a statement of sub…

The paper classifies biharmonic quadratic maps between spheres, proving their energy density properties.

In this work, we propose new objective functions to train deep neural network based density ratio estimators and apply it to a change point detection problem. Existing methods use linear combinations of kernels to approximate the density ratio function by solving a convex constrained minimization problem. Approximating…

In this paper, we are concerned with the problem of creating flattening maps of simply-connected open surfaces in $\mathbb{R}^3$. Using a natural principle of density diffusion in physics, we propose an effective algorithm for computing density-equalizing flattening maps with any prescribed density distribution. By var…

New tractable density models from squaring neural networks.

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

We study the asymptotic behavior of distribution densities arising in stock price models with stochastic volatility. The main objects of our interest in the present paper are the density of time averages of the squared volatility process and the density of the stock price process in the Stein-Stein and the Heston model…

A genetic algorithm improves multivariate kernel density estimation.

Paper bridges score estimation to parameter and density estimation in DDPMs.

We develop a new loss function for estimating quasiprobabilistic density ratios.

Study non-stationary distributions, proving risk bounds for density estimation.