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Related papers: Strong dependence, weight, and measure

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We show that the following are consistent with ZFC: 1. Strongly meager sets form an ideal with the same additivity as the ideal of meager sets. 2. There exists a strong measure zero set of size > d (dominating number).

Logic · Mathematics 2007-05-23 Tomek Bartoszynski , Saharon Shelah

In this paper we establish sharp weighted bounds (Buckley type theorems) for one{sided maximal and fractional integral operators in terms of one{sided $A_p$ characteristics. Appropriate sharp bounds for strong maximal functions, multiple…

Functional Analysis · Mathematics 2014-03-04 Vakhtang Kokilashvili , Alexander Meskhi , Muhammad Asad Zaighum

We analyze a natural function definable from a scale at a singular cardinal, and using this function we are able to obtain quite strong negative square-brackets partition relations at successors of singular cardinals. The proof of our main…

Logic · Mathematics 2008-06-02 Todd Eisworth

We introduce the notion of a tight cofinitary group, which captures forcing indestructibility of maximal cofinitary groups for a long list of partial orders, including Cohen, Sacks, Miller, Miller partition forcing and Shelah's poset for…

Logic · Mathematics 2025-05-08 Vera Fischer , Lukas Schembecker , David Schrittesser

The power of multiple testing procedures can be increased by using weighted p-values (Genovese, Roeder and Wasserman 2005). We derive the optimal weights and we show that the power is remarkably robust to misspecification of these weights.…

Statistics Theory · Mathematics 2007-06-13 Larry Wasserman , Kathryn Roeder

We introduce SNAP (Self-coNsistent Agreement Principle), a self-supervised framework for robust computation based on mutual agreement. Based on an Agreement-Reliability Hypothesis SNAP assigns weights that quantify agreement, emphasizing…

Machine Learning · Computer Science 2026-02-03 Xiaoyi Jiang , Andreas Nienkötter

For analysis of a high-dimensional dataset, a common approach is to test a null hypothesis of statistical independence on all variable pairs using a non-parametric measure of dependence. However, because this approach attempts to identify…

Statistics Theory · Mathematics 2015-05-14 Yakir A. Reshef , David N. Reshef , Pardis C. Sabeti , Michael M. Mitzenmacher

Measuring strength or degree of statistical dependence between two random variables is a common problem in many domains. Pearson's correlation coefficient $\rho$ is an accurate measure of linear dependence. We show that $\rho$ is a…

Statistics Theory · Mathematics 2018-04-24 Priyantha Wijayatunga

Recent work in the area of interdependent networks has focused on interactions between two systems of the same type. However, an important and ubiquitous class of systems are those involving monitoring and control, an example of…

Disordered Systems and Neural Networks · Physics 2013-09-27 Richard G. Morris , Marc Barthelemy

We give a category-theoretic construction of simple and NSOP$_1$-like independence relations in locally finitely presentable categories, and in the more general locally finitely multipresentable categories. We do so by identifying…

Category Theory · Mathematics 2025-06-24 Mark Kamsma , Jiří Rosický

We propose and axiomatize preferences on a product state space in light of uncertainty regarding the dependency of different payoff-relevant factors. Dependence structures allow to decompose probabilities and allow to pin down behavior…

Theoretical Economics · Economics 2026-05-28 Gerrit Bauch , Lorenz Hartmann

We develop the theory of Kim-independence in the context of NSOP$_{1}$ theories satsifying the existence axiom. We show that, in such theories, Kim-independence is transitive and that $\ind^{K}$-Morley sequences witness Kim-dividing. As…

Logic · Mathematics 2023-06-05 Artem Chernikov , Byunghan Kim , Nicholas Ramsey

We introduce the class of {\em strongly Rayleigh} probability measures by means of geometric properties of their generating polynomials that amount to the stability of the latter. This class covers important models such as determinantal…

Probability · Mathematics 2009-02-03 Julius Borcea , Petter Brändén , Thomas M. Liggett

We introduce a framework for robust uncertainty quantification in situations where labeled training data are corrupted, through noisy or missing labels. We build on conformal prediction, a statistical tool for generating prediction sets…

Machine Learning · Computer Science 2026-02-27 Shai Feldman , Stephen Bates , Yaniv Romano

Combating bias in NLP requires bias measurement. Bias measurement is almost always achieved by using lexicons of seed terms, i.e. sets of words specifying stereotypes or dimensions of interest. This reproducibility study focuses on the…

Computation and Language · Computer Science 2022-06-07 Jille van der Togt , Lea Tiyavorabun , Matteo Rosati , Giulio Starace

In this short note, using results of Bourgain, Fremlin, and Talagrand \cite{BFT}, we show that for a countable structure $M$, a saturated elementary extension $M^*$ of $M$ and a formula $\phi(x,y)$ the following are equivalent: (i)…

Logic · Mathematics 2017-04-04 Karim Khanaki

Probabilistic independence is a useful concept for describing the result of random sampling---a basic operation in all probabilistic languages---and for reasoning about groups of random variables. Nevertheless, existing verification methods…

Programming Languages · Computer Science 2020-07-21 Gilles Barthe , Justin Hsu , Kevin Liao

Representations of probability measures in reproducing kernel Hilbert spaces provide a flexible framework for fully nonparametric hypothesis tests of independence, which can capture any type of departure from independence, including…

Computation · Statistics 2018-06-11 Qinyi Zhang , Sarah Filippi , Arthur Gretton , Dino Sejdinovic

Answering a special case of a question of Chernikov and Simon, we show that any non-dividing formula over a model M in a distal NIP theory is a member of a consistent definable family, definable over M.

Logic · Mathematics 2017-01-23 Gareth Boxall , Charlotte Kestner

In this paper, we present a rich semantic network based on a differential analysis. We then detail implemented measures that take into account common and differential features between words. In a last section, we describe some industrial…

Artificial Intelligence · Computer Science 2007-05-23 Dominique Dutoit , Thierry Poibeau
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