arXiv research

Study continuity and Hölder estimates for solutions on Stein spaces.

New research sets the minimax lower bound for KSD estimation at sqrt(n).

Paper proposes approximate Stein classes for efficient truncated density estimation.

Stein shrinkage improves BN robustness against adversarial attacks.

When maximum likelihood estimation is infeasible, one often turns to score matching, contrastive divergence, or minimum probability flow to obtain tractable parameter estimates. We provide a unifying perspective of these techniques as minimum Stein discrepancy estimators, and use this lens to design new diffusion kerne…

Lower bounds on private estimation of Gaussian covariance matrices.

New Stein identity for q-Gaussians reduces gradient variance in machine learning.

Stein variational gradient descent (SVGD) is a non-parametric inference algorithm that evolves a set of particles to fit a given distribution of interest. We analyze the non-asymptotic properties of SVGD, showing that there exists a set of functions, which we call the Stein matching set, whose expectations are exactly …

New method improves sample diversity and efficiency from complex distributions.

SteinGen generates diverse graph samples from a single example.

We propose an improved LASSO estimation technique based on Stein-rule. We shrink classical LASSO estimator using preliminary test, shrinkage, and positive-rule shrinkage principle. Simulation results have been carried out for various configurations of correlation coefficients ($r$), size of the parameter vector ($β$), …

Improved kernel Stein discrepancy for large-scale data.

Proposes a method to stabilize Black Box Variational Inference using the James-Stein estimator.

Study optimizes KSD estimation from samples, revealing Hilbert-Schmidt vs trace scales.

Uniform-in-time analysis for Stein Variational Gradient Descent across various metrics.

Paper proves conditions for estimating precision matrices with Laplacian constraints.

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…

Estimates true Sharpe ratio of selected assets with various methods.

Stein importance sampling is a widely applicable technique based on kernelized Stein discrepancy, which corrects the output of approximate sampling algorithms by reweighting the empirical distribution of the samples. A general analysis of this technique is conducted for the previously unconsidered setting where samples…

This paper considers a multiple regression model and compares, under full model hypothesis, analytically as well as by simulation, the performance characteristics of some popular penalty estimators such as ridge regression, LASSO, adaptive LASSO, SCAD, and elastic net versus Least Squares Estimator, restricted estimato…

CSD improves goodness-of-fit testing for higher-order dependence.

The paper studies Stein-Weiss operators on symmetric tensors, extending previous work.

This work improves texture segmentation by automatically tuning hyperparameters for Total-Variation.

A new method de-randomizes MCMC dynamics using the Stein operator.

In this paper, we obtain asymptotic formulas with error estimates for the implied volatility associated with a European call pricing function. We show that these formulas imply Lee's moment formulas for the implied volatility and the tail-wing formulas due to Benaim and Friz. In addition, we analyze Pareto-type tails o…

New method estimates model discrepancy without sampling for unnormalized models.

A mean function in reproducing kernel Hilbert space, or a kernel mean, is an important part of many applications ranging from kernel principal component analysis to Hilbert-space embedding of distributions. Given finite samples, an empirical average is the standard estimate for the true kernel mean. We show that this e…

JSRT improves regression tree performance by incorporating global node information.

We define a diffeomorphism invariant of smooth 4-manifolds which we can estimate for many smoothings of R^4 and other smooth 4-manifolds. Using this invariant we can show that uncountably many smoothings of R^4 support no Stein structure. (Gompf has constructed uncountably many smoothings of R^4 which do support Stein …

StAD predicts divergence of diffusion and flow models without Jacobian computation.

Stein's method improves probabilistic inference and learning.

The paper computes a tau-invariant for holomorphic curves in Stein domains and links.

ASVGD accelerates SVGD for efficient sampling.

We present a procedure for effective estimation of entropy and mutual information from small-sample data, and apply it to the problem of inferring high-dimensional gene association networks. Specifically, we develop a James-Stein-type shrinkage estimator, resulting in a procedure that is highly efficient statistically …

We consider estimating the parametric components of semi-parametric multiple index models in a high-dimensional and non-Gaussian setting. Such models form a rich class of non-linear models with applications to signal processing, machine learning and statistics. Our estimators leverage the score function based first and…

New method reduces computational cost for learning stationary diffusions.

New method learns from noisy data without knowing noise level.

Stein variational neural network ensembles improve diversity and uncertainty estimation.

Unified view of score estimators for flexible densities.

Improves gradient estimation for discrete distributions with variance reduction techniques.

Novel MBRL method for large-scale RL with reduced posterior complexity.

We consider the problem of efficiently computing the maximum likelihood estimator in Generalized Linear Models (GLMs) when the number of observations is much larger than the number of coefficients ($n \gg p \gg 1$). In this regime, optimization algorithms can immensely benefit from approximate second order information.…

SMI uses mixture models to improve SVGD's performance in Bayesian inference.

Efficient event generation for collider phenomenology using parallel Langevin sampling and learned Stein diagnostics.

We study the parameter estimation problem for a varying index coefficient model in high dimensions. Unlike the most existing works that iteratively estimate the parameters and link functions, based on the generalized Stein's identity, we propose computationally efficient estimators for the high-dimensional parameters w…

The paper analyzes rates for a modified gradient descent method using Stein variational gradients.

GC Stein manifolds characterized with embeddings and functions.

New estimates show spectral gap stability in RCD spaces, close to Beta distribution.