arXiv research

Regularized Stein thinning improves MCMC output approximations.

Stochastic Stein Discrepancies improve inference efficiency.

Sliced kernelized Stein discrepancy improves goodness-of-fit tests and model learning in high dimensions.

NOVI improves deep Gaussian process inference with neural generators and regularized Stein discrepancy.

Paper develops a minimax optimal test for goodness-of-fit using kernel Stein discrepancy.

Much of machine learning relies on comparing distributions with discrepancy measures. Stein's method creates discrepancy measures between two distributions that require only the unnormalized density of one and samples from the other. Stein discrepancies can be combined with kernels to define kernelized Stein discrepanc…

The paper introduces a novel method for training neural network Stein critics with staged $L^2$-regularization.

Stein's method improves probabilistic inference and learning.

A new framework improves kernel Stein discrepancy tests for validating distributions.

Improving scalability and stability of Stein discrepancies for scalable goodness-of-fit testing

Inequalities linking entropy, Fisher info, Stein discrepancy, and Wasserstein distance on Riemannian manifolds.

A new measure helps compute suboptimality in entropy-regularized methods.

Stein discrepancy improves UDA performance in low-data scenarios.

Bayesian inference uses Stein discrepancy for robustness in intractable likelihoods.

Computable Stein discrepancies have been deployed for a variety of applications, ranging from sampler selection in posterior inference to approximate Bayesian inference to goodness-of-fit testing. Existing convergence-determining Stein discrepancies admit strong theoretical guarantees but suffer from a computational co…

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…

New method uses kernel Stein discrepancy for measure transport without strict continuity constraints.

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

Improved kernel Stein discrepancy for large-scale data.

Paper proposes kernelized Stein tests for time-to-event data with censoring.

Optimizes kernel discrepancies by selecting subsets efficiently.

Improved convergence rates for Stein Variational Gradient Descent in finite-particle settings.

A new sequential test for unnormalized densities.

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…

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

Paper proposes approximate Stein classes for efficient truncated density estimation.

SVGD algorithm converges at rate 1/sqrt(log log n) for sub-Gaussian distributions.

New method reduces computational cost for learning stationary diffusions.

AgraSSt assesses graph generators using Stein operators and kernel discrepancies.

A new test assesses how well observed networks fit a specified ERGM model.

New conditions ensure MMDs separate and converge to target distributions.

A new method for assessing Bayesian sampling quality, PSD, is proposed and shown to be more powerful and efficient.

KSD Descent uses KSD to sample from a target distribution efficiently.

This work interprets diffusion score matching using normalizing flows for better model training and evaluations.

Improved KSD test for better detection of differences in distributions.

A new test for IRG models using KSD for small networks.

New KSDs control moments in approximations, improving diagnostics and tests.

Paper analyzes SVGD algorithm for non-asymptotic convergence.

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

Approximate Markov chain Monte Carlo (MCMC) offers the promise of more rapid sampling at the cost of more biased inference. Since standard MCMC diagnostics fail to detect these biases, researchers have developed computable Stein discrepancy measures that provably determine the convergence of a sample to its target dist…

A new kernel Stein test assesses fit for variable-length sequential data.

Stein's method for measuring convergence to a continuous target distribution relies on an operator characterizing the target and Stein factor bounds on the solutions of an associated differential equation. While such operators and bounds are readily available for a diversity of univariate targets, few multivariate targ…

New method uses multiple kernels to improve SVGD performance.

We present a new method for evaluating and training unnormalized density models. Our approach only requires access to the gradient of the unnormalized model's log-density. We estimate the Stein discrepancy between the data density $p(x)$ and the model density $q(x)$ defined by a vector function of the data. We paramete…

Fourier representation improves KSD for infinite-dimensional data.

We derive a new discrepancy statistic for measuring differences between two probability distributions based on combining Stein's identity with the reproducing kernel Hilbert space theory. We apply our result to test how well a probabilistic model fits a set of observations, and derive a new class of powerful goodness-o…

New algorithm tunes SGMCMC hyperparameters for scalable Bayesian inference.

A new method for kernel tests without data splitting increases power.