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Learning Kolmogorov Models for Binary Random Variables

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Paper breakdownThe authors' claims, conditions and measurements, as JSON Signed receiptProof this review has not been altered
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Hadi Ghauch, Mikael Skoglund, Hossein Shokri-Ghadikolaei, Carlo Fischione, Ali H. Sayed

Machine Learning · Submitted 6 Jun 2018

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim We believe that the work is a significant step toward interpretable machine learning.

how it is stated · hedged possibility 0.45 read the record →

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arXiv ID1806.02322 (version 1)
Pith IDSH7TOMH5, Pith's permanent record of this paper and its review
CitationAli H. Sayed, Carlo Fischione, Hadi Ghauch, Hossein Shokri-Ghadikolaei, Mikael Skoglund. "Learning Kolmogorov Models for Binary Random Variables." arXiv:1806.02322 (2018). https://pith.science/paper/SH7TOMH5
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The compiled record

The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 4,135 input tokens.

What the abstract itself states, as probabilities: claims first 0.06 · states limitation 0.30 · human participants 0.07 · controlled comparison 0.03 · randomized assignment 0.02 · states quantitative result 0.08 · compares with prior results 0.07 · mentions code or data availability 0.03.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

We summarize our recent findings, where we proposed a framework for learning a Kolmogorov model, for a collection of binary random variables. More specifically, we derive conditions that link outcomes of specific random variables, and extract valuable relations from the data. We also propose an algorithm for computing the model and show its first-order optimality, despite the combinatorial nature of the learning problem. We apply the proposed algorithm to recommendation systems, although it is applicable to other scenarios. We believe that the work is a significant step toward interpretable machine learning.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 We summarize our recent findings, where we proposed a framework for learning a Kolmogorov model, for a collection of binary random variables. contribution 0.99 0.91 0.17 asserted 0.98
s1 More specifically, we derive conditions that link outcomes of specific random variables, and extract valuable relations from the data. method 0.62 0.94 0.11 asserted 1.00
s2 We also propose an algorithm for computing the model and show its first-order optimality, despite the combinatorial nature of the learning problem. method 0.53 0.96 0.18 asserted 1.00
s3 We apply the proposed algorithm to recommendation systems, although it is applicable to other scenarios. contribution 0.63 0.91 0.05 asserted 0.96
s4 We believe that the work is a significant step toward interpretable machine learning. interpretation 1.00 0.15 0.49 hedged possibility 0.45

What kind of paper the classifier reports this is: method or tool 0.99 · evidence from proof or derivation 0.85.

What was examined, and what was not

sources read
  • arXiv listing abstract v1
not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
levels run
  • P abstract
not run
  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

We summarize our recent findings, where we proposed a framework for learning a Kolmogorov model, for a collection of binary random variables. More specifically, we derive conditions that link outcomes of specific random variables, and extract valuable relations from the data. We also propose an algorithm for computing the model and show its first-order optimality, despite the combinatorial nature of the learning problem. We apply the proposed algorithm to recommendation systems, although it is applicable to other scenarios. We believe that the work is a significant step toward interpretable machine learning.

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