arXiv research

Meta-learning improves relative density-ratio estimation from limited data.

Divergence estimators based on direct approximation of density-ratios without going through separate approximation of numerator and denominator densities have been successfully applied to machine learning tasks that involve distribution comparison such as outlier detection, transfer learning, and two-sample homogeneity…

Researchers classify invariant operators on weighted densities.

This paper uses normalizing flows to approximate transport maps between densities.

A new local outlier detection method using KDE and RDOS.

REGS samples from unnormalized distributions using gradient flow and neural networks.

We study the problem of finding probability densities that match given European call option prices. To allow prior information about such a density to be taken into account, we generalise the algorithm presented in Neri and Schneider (2011) to find the maximum entropy density of an asset price to the relative entropy c…

Stable and consistent model alignment for language models without assuming human preference models.

dtSNE preserves local densities in low-dimensional embeddings.

We consider on-line density estimation with a parameterized density from the exponential family. The on-line algorithm receives one example at a time and maintains a parameter that is essentially an average of the past examples. After receiving an example the algorithm incurs a loss which is the negative log-likelihood…

The quotient of normal variables has power-law decay, applied to asset price tails.

Researchers explore gauge freedom in entropies of $q$-Gaussian measures.

The study finds dense orbits and absolute period leaves for complex flows.

Proves well-posedness for Einstein equations with specific boundary conditions.

The equity risk premium is derived from SPX option chains using a model-light approach.

Improves logistic regression performance on imbalanced data.

A new perspective on GANs using density ratio estimation.

The objective of change-point detection is to discover abrupt property changes lying behind time-series data. In this paper, we present a novel statistical change-point detection algorithm based on non-parametric divergence estimation between time-series samples from two retrospective segments. Our method uses the rela…

The paper explores recovery thresholds in heterogeneous SBM with varying community sizes and densities.

Bayesian optimization improves efficiency with semi-supervised learning.

Neural net reconstructs dark matter density from halo velocities.

Generic loxodromic elements are common in hyperbolic groups and grow linearly.

Conditional density estimation generalizes regression by modeling a full density f(yjx) rather than only the expected value E(yjx). This is important for many tasks, including handling multi-modality and generating prediction intervals. Though fundamental and widely applicable, nonparametric conditional density estimat…

New method estimates density-derivative-ratios directly for clustering and ridge estimation.

Starting from a relativistic phenomenology of anyons in crystals, we discuss the concept of relativistic interaction and the need to unify electromagnetism and gravitation within the Spencer cohomology of Lie equations. Then, from the sophisticated non-linear Spencer complex of the Poincaré and conformal Lie pseudogrou…

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

Paper tackles unbounded density ratio estimation for covariate shift adaptation.

In stochastic portfolio theory, a relative arbitrage is an equity portfolio which is guaranteed to outperform a benchmark portfolio over a finite horizon. When the market is diverse and sufficiently volatile, and the benchmark is the market or a buy-and-hold portfolio, functionally generated portfolios introduced by Fe…

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…

Kernel density estimation (KDE) is a popular statistical technique for estimating the underlying density distribution with minimal assumptions. Although they can be shown to achieve asymptotic estimation optimality for any input distribution, cross-validating for an optimal parameter requires significant computation do…

A model-free framework extracts risk-neutral densities from short-dated options.

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

LGKDE learns graph density using neural networks and perturbations.

Proposes a method for approximating transition densities of SDEs driven by gamma processes.

When working with asymptotically hyperbolic initial data sets for general relativity it is convenient to assume certain simplifying properties. We prove that the subset of initial data sets with such properties is dense in the set of physically reasonable asymptotically hyperbolic initial data sets. More specifically, …

Study quantized extremal Kähler metrics for algebro-geometric stability.

Felix visualizes cosmic filaments using topology.

The paper establishes minimax bounds for estimating low-rank quantum density matrices.

We study graph estimation and density estimation in high dimensions, using a family of density estimators based on forest structured undirected graphical models. For density estimation, we do not assume the true distribution corresponds to a forest; rather, we form kernel density estimates of the bivariate and univaria…

The paper develops a theory of conformal density at infinity for groups with contracting elements.

Machine learning predicts molecule properties using invariant scattering coefficients.

DO-EM framework for quantum models improves generative tasks.

Two new algorithms unwrap manifolds using density ridges.

A new method for density estimation using nearest neighbor Dirichlet mixtures.

Study finds optimal martingale coupling between two distributions with minimal entropy.

Deep density methods improve filtering in high-dimensional systems.

We formulate and analyze an inverse problem using derivatives prices to obtain an implied filtering density on volatility's hidden state. Stochastic volatility is the unobserved state in a hidden Markov model (HMM) and can be tracked using Bayesian filtering. However, derivative data can be considered as conditional ex…

EagleEye detects localized density anomalies in multivariate data.