Authors: Xingyou Song, Oscar Li, Chansoo Lee, Bangding, Yang, Daiyi Peng, Sagi Perel, Yutian Chen

Published on: February 22, 2024

Impact Score: 7.6

Arxiv code: Arxiv:2402.14547

Summary

• What is new: Introducing OmniPred, a framework that trains language models to perform regression across diverse real-world experiments.
• Why this is important: Existing regression methods are confined to specific tasks, limiting their applicability.
• What the research proposes: OmniPred uses language models trained on textual representations of parameters and values from diverse datasets for universal regression.
• Results: Experiments using Google Vizier data show that OmniPred outperforms traditional regression models in accuracy over multiple tasks.

Technical Details

Technological frameworks used: OmniPred

Models used: Language models

Data used: Google Vizier

Potential Impact

Data analytics, AI-driven decision-making platforms, and any industry relying on regression for predictions could benefit or face disruption.

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