arXiv research

A new family of nonparametric statistics, the r-statistics, is introduced. It consists of counting the number of records of the cumulative sum of the sample. The single-sample r-statistic is almost as powerful as Student's t-statistic for Gaussian and uniformly distributed variables, and more powerful than the sign and…

Active inference framework improves $U$-statistic estimation efficiency.

Improved statistical computation through efficient matrix sampling.

A method uses neural networks to approximate sampling distributions of test statistics.

Two statistical tasks are shown to have equivalent sample complexity.

In this paper, we consider a statistical problem of learning a linear model from noisy samples. Existing work has focused on approximating the least squares solution by using leverage-based scores as an importance sampling distribution. However, no finite sample statistical guarantees and no computationally efficient o…

Improved statistical inference for adaptive Thompson Sampling.

Study on U-statistics with heavy-tailed samples, providing tail bounds and LDP.

This work uses statistical mechanics to explain AI learning.

New method improves active statistical inference by reducing noise.

GANs improve event generation in physics experiments.

The paper evaluates variational auto-encoders using model criticism methods.

This paper develops dimension-agnostic inference methods for high-dimensional data.

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

Diffusion models learn simple statistics before complex ones, revealing a sample complexity exponent.

This work studies the statistical performance of Sinkhorn iterations in estimating Schrödinger bridges.

Efficiently transforms samples from various statistical models.

We analyze SGAs for statistical inference via asymptotics, improving tuning methods.

This guide explains statistical distances for evaluating generative models.

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

This paper reviews various sampling methods from statistics and machine learning.

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…

Method estimates model performance on external samples from limited statistical characteristics.

Triangular flows ensure statistical consistency and fast rates in generative modeling.

The paper examines extreme value statistics of high-dimensional sample covariances, with applications in finance and image analysis.

Optimizes ICA performance in high dimensions with computational constraints.

Efficiently approximates statistical leverage scores for faster KRR.

We consider statistical and algorithmic aspects of solving large-scale least-squares (LS) problems using randomized sketching algorithms. Prior results show that, from an \emph{algorithmic perspective}, when using sketching matrices constructed from random projections and leverage-score sampling, if the number of sampl…

We consider a two-sample hypothesis testing problem, where the distributions are defined on the space of undirected graphs, and one has access to only one observation from each model. A motivating example for this problem is comparing the friendship networks on Facebook and LinkedIn. The practical approach to such prob…

The paper analyzes a neural network two-sample test using kernel analysis.

Polyak step size GD reaches final radius of convergence after log iterations.

This paper strengthens the central limit theorem for order statistics using relative entropy.

E-C2ST uses E-values for high-dimensional data two-sample tests.

In this comment on "Solving Statistical Mechanics Using Variational Autoregressive Networks" by Wu et al., we propose a subtle yet powerful modification of their approach. We show that the inherent sampling error of their method can be corrected by using neural network-based MCMC or importance sampling which leads to a…

A new test statistic speeds up MMD while maintaining power.

We propose a two-sample testing procedure based on learned deep neural network representations. To this end, we define two test statistics that perform an asymptotic location test on data samples mapped onto a hidden layer. The tests are consistent and asymptotically control the type-1 error rate. Their test statistics…

Improves survey sampling with unbiased machine learning methods.

A nonparametric two-sample test using a parametric integral probability metric

New estimator stabilizes higher-order influence functions for stable statistical inference.

Computing equilibrium states in condensed-matter many-body systems, such as solvated proteins, is a long-standing challenge. Lacking methods for generating statistically independent equilibrium samples in "one shot", vast computational effort is invested for simulating these system in small steps, e.g., using Molecular…

Kernel two-sample testing is a useful statistical tool in determining whether data samples arise from different distributions without imposing any parametric assumptions on those distributions. However, raw data samples can expose sensitive information about individuals who participate in scientific studies, which make…

Study replicability in high-dimensional statistics, resolving open problems.

New method allows generating independent data matrices from summary statistics.

Efficient method for tensor linear form inference with noisy incomplete data.

New method samples DPPs efficiently without downsampling or low-rank approximations.

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. …

Sharp statistical theory for conditional diffusion models.

This study improves audit sampling by using sequential procedures with statistical guarantees.