OMEGA: Optimizing Machine Learning by Evaluating Generated Algorithms

Abstract: In order to automate AI research we introduce a full, end-to-end framework, OMEGA: Optimizing Machine learning by Evaluating Generated Algorithms, that starts at idea generation and ends with executable code.

摘要： 为了实现人工智能研究的自动化，我们引入了一个完整的端到端框架——OMEGA（通过评估生成的算法来优化机器学习），该框架从创意生成开始，最终产出可执行代码。

Our system combines structured meta-prompt engineering with executable code generation to create new ML classifiers.

我们的系统结合了结构化元提示工程（meta-prompt engineering）与可执行代码生成技术，旨在创建全新的机器学习分类器。

The OMEGA framework has been utilized to generate several novel algorithms that outperform scikit-learn baselines across a robust selection of 20 benchmark datasets (infinity-bench).

OMEGA 框架已被用于生成多种新颖算法，这些算法在 20 个精选基准数据集（infinity-bench）上的表现均优于 scikit-learn 的基准模型。

You can access models discussed in this paper and more in the python package: pip install omega-models.

您可以通过 Python 包 pip install omega-models 获取本文讨论的模型及更多相关内容。