New PRISMS framework uses pairwise rankings to accelerate scientific discovery

A new framework called PRISMS has been developed to address the challenge of "validation congestion" in scientific discovery, where the number of potential designs far exceeds the capacity for experimental evaluation. PRISMS utilizes expert pairwise rankings, which are easier to obtain than absolute scores, to identify promising candidates. It can incorporate expertise from various sources, including computational tools and human input across different fidelity levels. The framework escalates queries from lower to higher fidelity rankers based on a Fisher information criterion when experts have differing levels of expertise and cost. AI

IMPACT Accelerates the identification of promising scientific designs by leveraging expert pairwise rankings over traditional regression models.

RANK_REASON The cluster contains a research paper detailing a new framework for scientific discovery. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• arXiv
• Bayesian optimization
• Connected Papers
• Fisher information criterion
• Hugging Face
• Litmaps
• PRISMS
• scite Smart Citations

• paper
• other

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