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Found 11 papers, 5 papers with code
Semi-Supervised U-statistics
Semi-supervised datasets are ubiquitous across diverse domains where obtaining fully labeled data is costly or time-consuming.
Differentially Private Permutation Tests: Applications to Kernel Methods
The proposed framework extends classical non-private permutation tests to private settings, maintaining both finite-sample validity and differential privacy in a rigorous manner.
A Permutation-Free Kernel Independence Test
In nonparametric independence testing, we observe i. i. d.\ data $\{(X_i, Y_i)\}_{i=1}^n$, where $X \in \mathcal{X}, Y \in \mathcal{Y}$ lie in any general spaces, and we wish to test the null that $X$ is independent of $Y$.
A Permutation-free Kernel Two-Sample Test
The usual kernel-MMD test statistic is a degenerate U-statistic under the null, and thus it has an intractable limiting distribution.
The Projected Covariance Measure for assumption-lean variable significance testing
In this work we study the problem of testing the model-free null of conditional mean independence, i. e. that the conditional mean of $Y$ given $X$ and $Z$ does not depend on $X$.
Efficient Aggregated Kernel Tests using Incomplete $U$-statistics
We derive non-asymptotic uniform separation rates for MMDAggInc and HSICAggInc, and quantify exactly the trade-off between computational efficiency and the attainable rates: this result is novel for tests based on incomplete $U$-statistics, to our knowledge.
MMD Aggregated Two-Sample Test
In practice, this parameter is unknown and, hence, the optimal MMD test with this particular kernel cannot be used.
Randomized tests for high-dimensional regression: A more efficient and powerful solution
We investigate the problem of testing the global null in the high-dimensional regression models when the feature dimension $p$ grows proportionally to the number of observations $n$.
Dimension-agnostic inference using cross U-statistics
Classical asymptotic theory for statistical inference usually involves calibrating a statistic by fixing the dimension $d$ while letting the sample size $n$ increase to infinity.
Validation of Approximate Likelihood and Emulator Models for Computationally Intensive Simulations
At the heart of our approach is a two-sample test that quantifies the quality of the fit at fixed parameter values, and a global test that assesses goodness-of-fit across simulation parameters.
Classification accuracy as a proxy for two sample testing
We prove two results that hold for all classifiers in any dimensions: if its true error remains $\epsilon$-better than chance for some $\epsilon>0$ as $d, n \to \infty$, then (a) the permutation-based test is consistent (has power approaching to one), (b) a computationally efficient test based on a Gaussian approximation of the null distribution is also consistent.
