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Conditional independence (CI) tests underlie many approaches to model testing and structure learning in causal inference. Most existing CI tests for categorical and ordinal data stratify the sample by the conditioning variables, perform…

Machine Learning · Statistics 2023-07-06 Ankur Ankan , Johannes Textor

Drawbacks of ignoring the causal mechanisms when performing imitation learning have recently been acknowledged. Several approaches both to assess the feasibility of imitation and to circumvent causal confounding and causal misspecifications…

Machine Learning · Computer Science 2023-06-13 Fateme Jamshidi , Sina Akbari , Negar Kiyavash

Compositional data (i.e., data comprising random variables that sum up to a constant) arises in many applications including microbiome studies, chemical ecology, political science, and experimental designs. Yet when compositional data serve…

Methodology · Statistics 2025-01-03 Ritwik Bhaduri , Siyuan Ma , Lucas Janson

We propose a method for inferring the conditional independence graph (CIG) of a high-dimensional Gaussian vector time series (discrete-time process) from a finite-length observation. By contrast to existing approaches, we do not rely on a…

Machine Learning · Statistics 2015-10-28 Alexander Jung

Latent variable discovery is a central problem in data analysis with a broad range of applications in applied science. In this work, we consider data given as an invertible mixture of two statistically independent components and assume that…

Machine Learning · Statistics 2023-03-08 Uri Shaham , Jonathan Svirsky , Ori Katz , Ronen Talmon

A theorem of Mandel allows to determine the covector set of an oriented matroid from its set of topes by using the composition condition. We provide a generalization of that result, stating that the covector set of a conditional oriented…

Combinatorics · Mathematics 2023-09-20 Hery Randriamaro

The logic of Dependence and Independence Bunched Implications (DIBI) is a logic to reason about conditional independence (CI); for instance, DIBI formulas can characterise CI in probability distributions and relational databases, using the…

Logic in Computer Science · Computer Science 2024-01-12 Tao Gu , Jialu Bao , Justin Hsu , Alexandra Silva , Fabio Zanasi

We present an encoding of a polynomial system into vanishing and non-vanishing constraints on almost-principal minors of a symmetric, principally regular matrix, such that the solvability of the system over some field is equivalent to the…

Statistics Theory · Mathematics 2021-03-04 Tobias Boege

In some significant applications such as data forecasting, the locations of missing entries cannot obey any non-degenerate distributions, questioning the validity of the prevalent assumption that the missing data is randomly chosen…

Information Theory · Computer Science 2019-09-09 Guangcan Liu , Qingshan Liu , Xiao-Tong Yuan , Meng Wang

The Catalan numbers form a sequence that counts over 200 combinatorial objects. A remarkable property of the Catalan numbers, which extends to these objects, is its recursive definition; that is, we can determine the $n^{th}$ object from…

Combinatorics · Mathematics 2022-03-09 Jan Tracy Camacho

Learning the cumulative distribution function (CDF) of an outcome variable conditional on a set of features remains challenging, especially in high-dimensional settings. Conditional transformation models provide a semi-parametric approach…

Machine Learning · Computer Science 2021-10-05 Philipp F. M. Baumann , Torsten Hothorn , David Rügamer

How can we learn generative models to sample data with arbitrary logical compositions of statistically independent attributes? The prevailing solution is to sample from distributions expressed as a composition of attributes' conditional…

Machine Learning · Computer Science 2025-03-04 Sachit Gaudi , Gautam Sreekumar , Vishnu Boddeti

Self-organization of complex morphological patterns from local interactions is a fascinating phenomenon in many natural and artificial systems. In the artificial world, typical examples of such morphogenetic systems are cellular automata.…

Machine Learning · Computer Science 2021-09-06 Mayalen Etcheverry , Clement Moulin-Frier , Pierre-Yves Oudeyer

Decidability and synthesis of inductive invariants ranging in a given domain play an important role in many software and hardware verification systems. We consider here inductive invariants belonging to an abstract domain $A$ as defined in…

Programming Languages · Computer Science 2020-07-14 Francesco Ranzato

Bayesian persuasion is a model for understanding strategic information revelation: an agent with an informational advantage, called a sender, strategically discloses information by sending signals to another agent, called a receiver. In…

Computer Science and Game Theory · Computer Science 2021-12-14 Kaito Fujii , Shinsaku Sakaue

We deal with the problem of learning the underlying disentangled latent factors that are shared between the paired bi-modal data in cross-modal retrieval. Our assumption is that the data in both modalities are complex, structured, and high…

Machine Learning · Computer Science 2020-12-02 Minyoung Kim , Ricardo Guerrero , Vladimir Pavlovic

We study higher order determinantal varieties obtained by considering generic $m\times n$ ($m \le n$) matrices over rings of the form $F[t]/(t^k)$, and for some fixed $r$, setting the coefficients of powers of $t$ of all $r \times r$ minors…

Algebraic Geometry · Mathematics 2007-05-23 Tomaz Kosir , B. A. Sethuraman

The vanishing ideal of a set of points $X\subseteq \mathbb{R}^n$ is the set of polynomials that evaluate to $0$ over all points $\mathbf{x} \in X$ and admits an efficient representation by a finite set of polynomials called generators. To…

Machine Learning · Computer Science 2024-02-15 Elias Wirth , Sebastian Pokutta

Implicit heterogeneous metaprogramming (a.k.a. offshoring) is an attractive approach for generating C with some correctness guarantees: generate OCaml code, where the correctness guarantees are easier to establish, and then map that code to…

Programming Languages · Computer Science 2022-11-09 Oleg Kiselyov

We investigate the problem of detecting dependencies between the components of a high-dimensional vector. Our approach advances the existing literature in two important respects. First, we consider the problem under privacy constraints.…

Statistics Theory · Mathematics 2026-03-24 Patrick Bastian , Holger Dette , Martin Dunsche