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.
The study examines Fisher-Riemann geodesics for nonparametric probability densities.
Paper proposes MMC to avoid high-density bias in clustering.
We prove that capillary surfaces converge to a specific energy density as the angle approaches zero.
TRE improves density-ratio estimation for highly dissimilar densities.
Accurate approximations to density functionals have recently been obtained via machine learning (ML). By applying ML to a simple function of one variable without any random sampling, we extract the qualitative dependence of errors on hyperparameters. We find universal features of the behavior in extreme limits, includi…
DAEDL improves EDL's OOD detection and classification performance by integrating feature space density.
This paper studies a limit order book (LOB) model, in which the order dynamics depend on both, the current best available prices and the current volume density functions. For the joint dynamics of the best bid price, the best ask price, and the standing volume densities on both sides of the LOB we derive a weak law of …
We define a stochastic model of a two-sided limit order book in terms of its key quantities \textit{best bid [ask] price} and the \textit{standing buy [sell] volume density}. For a simple scaling of the discreteness parameters, that keeps the expected volume rate over the considered price interval invariant, we prove a…
Study language generation with limited memory, showing different impacts on achievable densities and convergence.
A new method inflates and deflates data manifolds to estimate densities without losing universality.
Alternative model predicts health insurance reimbursement based on contract limitations.
Samplets and multiwavelets constructed from scattered data converge to specific densities in the limit.
Given a data set and a subset of labels the problem of semi-supervised learning on point clouds is to extend the labels to the entire data set. In this paper we extend the labels by minimising the constrained discrete -Dirichlet energy. Under suitable conditions the discrete problem can be connected, in the large da…
The accurate detection of small deviations in given density matrices is important for quantum information processing. Here we propose a new method based on the concept of data mining. We demonstrate that the proposed method can more accurately detect small erroneous deviations in reconstructed density matrices, which c…
Kernel Density Machines learn probability densities without structural assumptions.
Probability density estimation is a classical and well studied problem, but standard density estimation methods have historically lacked the power to model complex and high-dimensional image distributions. More recent generative models leverage the power of neural networks to implicitly learn and represent probability …
Let G be a finitely generated group with a given word metric. The asymptotic density of elements in G that have a particular property P is defined to be the limit, as r goes to infinity, of the proportion of elements in the ball of radius r which have the property P. We obtain a formula to compute the asymptotic densit…
The density conjecture of Bers, Sullivan and Thurston predicts that each complete hyperbolic 3-manifold M with finitely generated fundamental group is an algebraic limit of geometrically finite hyperbolic 3-manifolds. We prove that the conjecture obtains for each complete hyperbolic 3-manifold with no cusps and incompr…
We define Type I singularities for the mean curvature flow associated to a density (MCF) and describe the blow-up at singular time of these singularities. Special attention is paid to the case where the singularity come from the part of the -curvature due to the density. We describe a family of curves whose e…
The need to estimate smooth probability distributions (a.k.a. probability densities) from finite sampled data is ubiquitous in science. Many approaches to this problem have been described, but none is yet regarded as providing a definitive solution. Maximum entropy estimation and Bayesian field theory are two such appr…
We prove a Weyl Law for the phase transition spectrum based on the techniques of Liokumovich-Marques-Neves. As an application we give phase transition adaptations of the proofs of the density and equidistribution of minimal hypersufaces for generic metrics by Irie-Marques-Neves and Marques-Neves-Song, respectively. We …
We investigate the class of tempered stable distributions and their associated processes. Our analysis of tempered stable distributions includes limit distributions, parameter estimation and the study of their densities. Regarding tempered stable processes, we deal with density transformations and compute their -var…
Generalized score matching for densities on general domains.
Machine learning model predicts DFT total energy to complete basis set limit.
In this paper, a nonparametric maximum likelihood (ML) estimator for band-limited (BL) probability density functions (pdfs) is proposed. The BLML estimator is consistent and computationally efficient. To compute the BLML estimator, three approximate algorithms are presented: a binary quadratic programming (BQP) algorit…
Proposes a new feature preprocessing method using kernel density integral transformation.
MESSY estimation recovers symbolic density functions from samples using maximum entropy.
Proposes a neural density estimator that adapts to low-dimensional structures and integrates into generative models.
Triangular map is a recent construct in probability theory that allows one to transform any source probability density function to any target density function. Based on triangular maps, we propose a general framework for high-dimensional density estimation, by specifying one-dimensional transformations (equivalently co…
Neural density estimators are flexible families of parametric models which have seen widespread use in unsupervised machine learning in recent years. Maximum-likelihood training typically dictates that these models be constrained to specify an explicit density. However, this limitation can be overcome by instead using …
The paper analyzes Kernel Density Estimation in high dimensions with varying data and dimensionality.
Proposes a new method for generating synthetic data using copula flows.
Paper characterizes optimal graph clustering limits under a new model.
NeuroPMD estimates densities on complex product manifolds.
Generative model prices basket options efficiently.
We prove that a minimal disc in a CAT(0) space is a local embedding away from a finite set of "branch points". On the way we establish several basic properties of minimal surfaces: monotonicity of area densities, density bounds, limit theorems and the existence of tangent maps. As an application, we prove Fary-Milnor's…
We investigate the position of the Buchen-Kelly density in a family of entropy maximising densities which all match European call option prices for a given maturity observed in the market. Using the Legendre transform which links the entropy function and the cumulant generating function, we show that it is both the uni…
Meta-learning improves relative density-ratio estimation from limited data.
Adaptive neural network approximates stochastic system densities.
New examples of embeddings defy Anosov representation limits.
This paper develops a new method for online density estimation from noisy data.
Normalizing flows can now estimate densities on unknown manifolds.
Symmetry of neural network densities can be determined from correlation functions.
Transforms conditional density estimation into a nonparametric regression problem.
BMTI method estimates densities without bins, outperforming traditional estimators.
MBORE optimizes multi-objective problems using density-ratio estimation.
BO method improved by density-ratio estimation for better efficiency and scalability.
Kernel methods are popular in clustering due to their generality and discriminating power. However, we show that many kernel clustering criteria have density biases theoretically explaining some practically significant artifacts empirically observed in the past. For example, we provide conditions and formally prove the…