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ZeroFolio uses text embeddings for algorithm selection without domain knowledge

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a novel feature-free approach to algorithm selection called ZeroFolio, which utilizes pretrained text embeddings to distinguish problem instances without requiring domain-specific knowledge. This method involves serializing the raw instance file as plain text, embedding it using a pretrained model, and then selecting an algorithm via weighted k-nearest neighbors. Evaluations on 11 ASlib scenarios demonstrated that ZeroFolio outperformed random forests trained on hand-crafted features in most cases and closely matched AutoFolio's performance without extensive tuning. AI

IMPACT This approach could enable more general-purpose algorithm selection tools that require less manual feature engineering.

RANK_REASON The cluster contains a research paper detailing a new method for algorithm selection. [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 →

ZeroFolio uses text embeddings for algorithm selection without domain knowledge

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The cluster contains a research paper detailing a new method for algorithm selection. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Stefan Szeider ·

    Algorithm Selection with Zero Domain Knowledge via Text Embeddings

    arXiv:2604.19753v2 Announce Type: replace Abstract: We propose a feature-free approach to algorithm selection: instead of hand-crafted instance features, we use pretrained text embeddings. Our method, ZeroFolio, proceeds in three steps. First, it reads the raw instance file as pl…

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