Towards Automated Machine Learning Research

• URL: http://arxiv.org/abs/2409.05258v1
• Date: Mon, 9 Sep 2024 00:47:30 GMT
• Title: Towards Automated Machine Learning Research
• Authors: Shervin Ardeshir,
• Abstract summary: This paper explores a top-down approach to automating incremental advances in machine learning research through component-level innovation. Our framework systematically generates novel components, validates their feasibility, and evaluates their performance against existing baselines. By incorporating a reward model to prioritize promising hypotheses, we aim to improve the efficiency of the hypothesis generation and evaluation process.
• Score: 4.169915659794567
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper explores a top-down approach to automating incremental advances in machine learning research through component-level innovation, facilitated by Large Language Models (LLMs). Our framework systematically generates novel components, validates their feasibility, and evaluates their performance against existing baselines. A key distinction of this approach lies in how these novel components are generated. Unlike traditional AutoML and NAS methods, which often rely on a bottom-up combinatorial search over predefined, hardcoded base components, our method leverages the cross-domain knowledge embedded in LLMs to propose new components that may not be confined to any hard-coded predefined set. By incorporating a reward model to prioritize promising hypotheses, we aim to improve the efficiency of the hypothesis generation and evaluation process. We hope this approach offers a new avenue for exploration and contributes to the ongoing dialogue in the field.

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