arXiv Machine Learning
Jul 31
HOMER: Huber-of-Means for Efficient and Robust Estimation in Hilbert Spaces
arXiv:2607. 27532v1 Announce Type: cross Abstract: Heavy tails weaken high-confidence control for the empirical mean.
On Median of Incomplete U-Statistics
arXiv:2606. 00661v1 Announce Type: cross Abstract: We establish the finite-sample concentration rate for the Median-of-Incomplete-U-Statistics (MIU), an efficient robust estimator for the expectation of symmetric kernels.
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arXiv Machine Learning
Jul 10
Maximum Mean Discrepancy with Unequal Sample Sizes via Generalized U-Statistics
arXiv:2512. 13997v2 Announce Type: replace-cross Abstract: Existing two-sample testing techniques, particularly those based on choosing a kernel for the Maximum Mean Discrepancy (MMD), often assume equal sample sizes from the two distributions.
arXiv Machine Learning
Jul 28
Minimax Lower Bounds of Kernel Discrepancy Estimation: MMD, HSIC, KSD
arXiv:2607. 24235v1 Announce Type: cross Abstract: Over the past 20 years, kernel discrepancies have been leveraged as a highly powerful tool for quantifying the disagreement of distributions, with numerous successful applications in two-sample, goodness-of-fit, and independence testing, among others.
arXiv Machine Learning
Jun 25
Robust Linear Predictions: Analyses of Uniform Concentration, Fast Rates and Model Misspecification
arXiv:2201. 01973v3 Announce Type: replace-cross Abstract: The problem of linear predictions has been extensively studied for the past century under pretty generalized frameworks.
arXiv Machine Learning
Jul 23
Directional Kernel Mean Difference: A Fast Signed Statistic for Univariate Distribution Comparison
arXiv:2607. 20119v1 Announce Type: cross Abstract: We introduce the Directional Kernel Mean Difference (DKMD), a signed statistic for univariate distribution comparison that preserves the direction of distributional shifts.
arXiv Machine Learning
Jun 24
A Private Approximation of the 2nd-Moment Matrix of Any Subsamplable Input
arXiv:2505. 14251v2 Announce Type: replace Abstract: We study the problem of differentially private second moment estimation and present a new algorithm that achieve strong privacy-utility trade-offs even for worst-case inputs under subsamplability assumptions on the data.
arXiv Machine Learning
Jul 14
Sharp Concentration Bounds for Bundle-Valued Statistics on Manifolds
arXiv:2607. 10592v1 Announce Type: new Abstract: Many geometric statistics and manifold learning pipelines routinely produce observations -- such as tangent vectors or local frames -- whose natural home is a varying family of fibers attached to different points of a base manifold, rather than a single shared vector space.
arXiv Machine Learning
Jul 10
Distributionally Faithful Imputation via Positive Semi-Definite Kernel Density Estimation
arXiv:2607. 07767v1 Announce Type: cross Abstract: Missing values undermine statistical inference and machine learning pipelines, yet most imputation methods rely on heuristics or restrictive parametric assumptions that ignore the joint data distribution.
arXiv Machine Learning
Jun 15
Recovery thresholds for hidden weighted sparse graphs
arXiv:2606. 14335v1 Announce Type: cross Abstract: Recovering structural information from noisy high-dimensional data is a fundamental task in statistical inference.
arXiv Machine Learning
Jun 30
Highly Data Parallelizable Estimation of the Sliced-Wasserstein Distance Using Cumulative Distribution Functions
arXiv:2606. 30310v1 Announce Type: cross Abstract: The Sliced Wasserstein (SW) distance has emerged as a computationally attractive alternative to the Wasserstein distance by leveraging one-dimensional optimal transport along random projections.
arXiv Machine Learning
Jun 2
How Accurately Can a Gaussian Approximate Stochastic Approximation Iterates?
arXiv:2602. 13906v2 Announce Type: replace-cross Abstract: Stochastic approximation (SA) is a method for finding the root of an operator perturbed by noise.
arXiv Machine Learning
Jul 15
High-Dimensional Gaussian Mean Estimation under Realizable Contamination
arXiv:2603. 16798v2 Announce Type: replace Abstract: We study mean estimation for a Gaussian distribution with identity covariance in $\mathbb{R}^d$ under a missing data scheme termed realizable $\epsilon$-contamination model.