arXiv research

The article introduces practical estimators for kernel discrepancies.

A new test statistic measures discrepancy between conditional distributions.

New discrepancy function compares discrete probability measures considering space geometry.

A new method uses neural tangent kernel to efficiently compute MMD statistic.

Kernel tests assess equivalence between distributions without assuming specific moments.

Efficient tests for various statistical problems using incomplete U-statistics.

Proposes TFDF to learn transferable and discriminative features for unsupervised domain adaptation.

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

This thesis improves kernel-based distances for statistical inference and integration.

We show in this note that the Sobolev Discrepancy introduced in Mroueh et al in the context of generative adversarial networks, is actually the weighted negative Sobolev norm $||.||_{\dot{H}^{-1}(ν_q)}$, that is known to linearize the Wasserstein $W_2$ distance and plays a fundamental role in the dynamic formulation of…

SRRM improves recursive transport surrogates in the small-discrepancy regime.

We propose a framework for synthesis of geological images based on an exemplar image. We synthesize new realizations such that the discrepancy in the patch distribution between the realizations and the exemplar image is minimized. Such discrepancy is quantified using a kernel method for two-sample test called maximum m…

Study on discrepancy principle for learning algorithms in nonparametric regression.

A method to select validation data from a dataset using statistical criteria.

A new measure scales MMD to assess distribution closeness.

Improved kernel Stein discrepancy for large-scale data.

A new method for density estimation using mixture discrepancy and moments.

New algorithms minimize MMD to approximate probability measures efficiently.

Approximate Bayesian computation (ABC) has become an essential part of the Bayesian toolbox for addressing problems in which the likelihood is prohibitively expensive or entirely unknown, making it intractable. ABC defines a pseudo-posterior by comparing observed data with simulated data, traditionally based on some su…

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

Method learns statistics of return distributions via neural networks and maximum mean discrepancy.

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

sig-MMD tests compare path distributions using kernel methods.

Unified method for MMD variance estimation improves accuracy and computational efficiency.

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

A new ensemble filter uses transport maps and MMD optimization for high-dimensional data assimilation.

Paper uses optimal transport-based statistics for change point detection.

AgraSSt assesses graph generators using Stein operators and kernel discrepancies.

New method for MMD with unequal sample sizes improves test power.

Expands Bayesian experiment design framework to account for model discrepancies.

Our paper deals with inferring simulator-based statistical models given some observed data. A simulator-based model is a parametrized mechanism which specifies how data are generated. It is thus also referred to as generative model. We assume that only a finite number of parameters are of interest and allow the generat…

MMD test detects adversarial attacks by addressing kernel limitations and non-independence issues.

Measuring divergence between two distributions is essential in machine learning and statistics and has various applications including binary classification, change point detection, and two-sample test. Furthermore, in the era of big data, designing divergence measure that is interpretable and can handle high-dimensiona…

Improved KSD test for faster GoF testing.

Paper develops a unified framework for measuring differences between conditional distributions.

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

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…

We consider training a deep neural network to generate samples from an unknown distribution given i.i.d. data. We frame learning as an optimization minimizing a two-sample test statistic---informally speaking, a good generator network produces samples that cause a two-sample test to fail to reject the null hypothesis. …

A new strategy selects k in k-NN regression without hold-out data.

This work proposes a new method to match distributions across different spaces using cycle-consistent maps.

Unified framework to bridge human and LLM judgments.

New MMD estimators detect differences in missing paired data.

Study on kernel tests for high-dimensional data, focusing on MMD and CLT.

Proposes a new method to analyze the distributional effects of treatments.

While likelihood-based inference and its variants provide a statistically efficient and widely applicable approach to parametric inference, their application to models involving intractable likelihoods poses challenges. In this work, we study a class of minimum distance estimators for intractable generative models, tha…

Efficiently tests two distributions using Nyström approximation of MMD.

A new model-free subsampling method using uniform designs is proposed.

Copula Discrepancy benchmarks sample dependence structure against known families.