arXiv:2507. 12843v3 Announce Type: replace Abstract: Are two distributions close to each other with statistical significance?
By Zhijian Zhou, Liuhua Peng, Xunye Tian, Mingming Gong, Feng Liu
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.
By Jose Cribeiro-Ramallo, Florian Kalinke, Zolt\'an Szab\'o
arXiv:2607. 15645v1 Announce Type: cross Abstract: Motivated by the challenge of testing distributions over high-dimensional or continuous domains, we study distribution testing with respect to bounded classes of distinguishers.
By Mark Bun, Rathin Desai, Renato Ferreira Pinto Jr
arXiv:2605. 09916v2 Announce Type: replace-cross Abstract: We introduce the observable Wasserstein distance, a framework for deriving lower bounds on the Wasserstein distance between probability measures on Polish metric spaces, designed to bypass the computational intractability of exact optimal transport in large-scale, non-Euclidean datasets.
By Edivaldo Lopes dos Santos, Leandro Vicente Mauri, Washington Mio, Tom Needham
The article introduces PTED, a Python implementation of a permutation test based on the Energy Distance for two-sample testing in multiple dimensions. PTED uses pairwise distances to compute a test statistic that works in high dimensions, on learned feature representations, and for any data type where a distance can be defined. The authors demonstrate that PTED scales linearly with dimensions and sample size while retaining strong discriminative power, and show it outperforms other multi‑dimensional tests in sensitivity.
By Connor Stone
arXiv:2606. 29665v1 Announce Type: cross Abstract: This paper examines how metric adjustments to Multidimensional Scaling (MDS) can enhance its effectiveness as a visual tool for pattern recognition.
By Flor Martinez-Sermeno, Arturo Jaramillo, Johan Van Horebeek