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New research explores learning distributions from multiple data providers

By PulseAugur Editorial · [1 sources] ·

A new research paper published on arXiv introduces a theoretical framework for learning distributions from multiple, potentially overlapping data providers. The study focuses on a stylized model where a learner aims to reconstruct an unknown distribution by querying specific sets of data. The paper establishes that the learnability and sample complexity are directly influenced by the co-occurrence graph of these queryable sets, with a connected graph being necessary for consistency and a complete graph for PAC learning. The research also details optimal sample complexities ranging from nearly linear to quadratic, depending on the structure of the query family. AI

IMPACT Provides a theoretical foundation for more robust data integration in machine learning models.

RANK_REASON The item is an academic paper on arXiv detailing a theoretical model for data distribution learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New research explores learning distributions from multiple data providers

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The item is an academic paper on arXiv detailing a theoretical model for data distribution learning. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Jon Kleinberg, Amin Saberi, Xizhi Tan, Grigoris Velegkas ·

    Learning Distributions from Multiple Data Providers

    arXiv:2607.24732v1 Announce Type: cross Abstract: Motivated by learning from heterogeneous and overlapping data providers, we study a stylized model of distribution learning from restricted conditional samples. The goal is to learn an unknown distribution $p$ on a finite domain $…

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