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This article deals with the problem of testing conditional independence between two random vectors ${\bf X}$ and ${\bf Y}$ given a confounding random vector ${\bf Z}$. Several authors have considered this problem for multivariate data.…

Statistics Theory · Mathematics 2025-09-16 Bilol Banerjee

A $d$-dimensional simplicial complex $X$ is said to support a direct product tester if any locally consistent function defined on its $k$-faces (where $k\ll d$) necessarily come from a function over its vertices. More precisely, a direct…

Computational Complexity · Computer Science 2024-02-02 Mitali Bafna , Dor Minzer

We study the multifractal analysis of self-similar measures arising from random homogeneous iterated function systems. Under the assumption of the uniform strong separation condition, we see that this analysis parallels that of the…

Dynamical Systems · Mathematics 2019-12-23 Kathryn E. Hare , Kevin G. Hare , Sascha Troscheit

We consider testing regression coefficients in high dimensional generalized linear models. An investigation of the test of Goeman et al. (2011) is conducted, which reveals that if the inverse of the link function is unbounded, the high…

Methodology · Statistics 2014-02-21 Song Xi Chen , Bin Guo

Context: Software Engineering (SE) experiments suffer from threats to validity that may impact their results. Replication allows researchers building on top of previous experiments' weaknesses and increasing the reliability of the findings.…

Software Engineering · Computer Science 2020-04-14 Adrian Santos , Sira Vegas , Fernando Uyaguari , Oscar Dieste , Burak Turhan , Natalia Juristo

Verification of higher-order probabilistic programs is a challenging problem. We present a verification method that supports several quantitative properties of higher-order probabilistic programs. Usually, extending verification methods to…

Logic in Computer Science · Computer Science 2024-07-04 Satoshi Kura , Hiroshi Unno

In this paper, we consider testing the correlation coefficient matrix between two subsets of high-dimensional variables. We produce a test statistic by using the extended cross-data-matrix (ECDM) methodology and show the unbiasedness of…

Methodology · Statistics 2015-03-24 Kazuyoshi Yata , Makoto Aoshima

Dimensionality is one of the most important properties of complex physical systems. However, only recently this concept has been considered in the context of complex networks. In this paper we further develop the previously introduced…

Physics and Society · Physics 2013-08-19 Filipi Nascimento Silva , Luciano da Fontoura Costa

In this paper, we further explore the local-to-global approach for expansion of simplicial complexes that we call local spectral expansion. Specifically, we prove that local expansion in the links imply the global expansion phenomena of…

Combinatorics · Mathematics 2018-03-06 Izhar Oppenheim

Invariance with respect to linear or affine transformations of the domain is arguably the most common symmetry exhibited by natural algebraic properties. In this work, we show that any low complexity affine-invariant property of…

Computational Complexity · Computer Science 2012-10-09 Arnab Bhattacharyya , Eldar Fischer , Shachar Lovett

Assessing the validity of a real-world system with respect to given quality criteria is a common yet costly task in industrial applications due to the vast number of required real-world tests. Validating such systems by means of simulation…

Machine Learning · Computer Science 2024-09-06 David Reeb , Kanil Patel , Karim Barsim , Martin Schiegg , Sebastian Gerwinn

We introduce a framework of layered subsets, and give a sufficient condition for when a set system supports an agreement test. Agreement testing is a certain type of property testing that generalizes PCP tests such as the plane vs. plane…

Computational Complexity · Computer Science 2019-09-04 Yotam Dikstein , Irit Dinur

We investigate the problem of safety verification of infinite-state parameterized programs that are formed based on a rich class of topologies. We introduce a new proof system, called parametric proof spaces, which exploits the underlying…

Logic in Computer Science · Computer Science 2026-01-27 Ruotong Cheng , Azadeh Farzan

Estimation of the high-dimensional banded covariance matrix is widely used in multivariate statistical analysis. To ensure the validity of estimation, we aim to test the hypothesis that the covariance matrix is banded with a certain…

Methodology · Statistics 2022-04-26 Xiaoyi Wang , Gongjun Xu , Shurong Zheng

We propose a methodology for testing linear hypothesis in high-dimensional linear models. The proposed test does not impose any restriction on the size of the model, i.e. model sparsity or the loading vector representing the hypothesis.…

Methodology · Statistics 2019-07-09 Yinchu Zhu , Jelena Bradic

The problem of testing low-degree polynomials has received significant attention over the years due to its importance in theoretical computer science, and in particular in complexity theory. The problem is specified by three parameters:…

Computational Complexity · Computer Science 2022-02-18 Tali Kaufman , Dor Minzer

We propose a robust inferential procedure for assessing uncertainties of parameter estimation in high-dimensional linear models, where the dimension $p$ can grow exponentially fast with the sample size $n$. Our method combines the…

Machine Learning · Statistics 2015-03-19 Tianqi Zhao , Mladen Kolar , Han Liu

A local system H on a complex manifold M can be viewed in two ways--either as a locally free sheaf, or as a union of covering spaces T = T(H). When M is an open set in a bigger manifold, the local system will generally not extend, because…

Algebraic Geometry · Mathematics 2007-10-16 Christian Schnell

In this Letter we compute an analogue of Tsirelson's bound for Hardy's test of nonlocality, that is, the maximum violation of locality constraints allowed by the quantum formalism, irrespective of the dimension of the system. The value is…

Quantum Physics · Physics 2015-06-05 Rafael Rabelo , Law Yun Zhi , Valerio Scarani

In this paper, we propose Diffusion Domain Expansion (DDE), a method that efficiently extends pre-trained diffusion models to generate larger objects and handle more complex conditioning beyond their original capabilities. Our method…

Machine Learning · Computer Science 2026-05-25 Egor Lifar , Semyon Savkin , Timur Garipov , Shangyuan Tong , Tommi Jaakkola