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New Splice system optimizes data source selection for ML tasks

By PulseAugur Editorial · [1 sources] ·

Researchers have developed Splice, a novel system for selecting optimal data sources for machine learning tasks. Splice aims to improve downstream task utility by identifying subsets of discovered data sources that avoid introducing noise or distribution shifts. Two faster variants, Splice-DP and Splice-Grad, were also introduced, which leverage meta-learning and gradient alignment to estimate source contributions, achieving significant speedups with comparable or improved results on classification and regression tasks. AI

IMPACT This research could lead to more efficient and accurate machine learning model training by improving data source selection.

RANK_REASON This is a research paper detailing a new system for machine learning tasks. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

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

New Splice system optimizes data source selection for ML tasks

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This is a research paper detailing a new system for machine learning tasks. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Ambarish Singh, Romila Pradhan ·

    Scalable Source Selection for Machine Learning Tasks

    arXiv:2507.22186v3 Announce Type: replace-cross Abstract: Given a query table, existing data lake discovery techniques seek to identify data sources related to the query table through schema similarity and joinability. The discovered sources, however, are not always useful for do…

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