SELA: Tree-Search Enhanced LLM Agents for Automated Machine Learning (2410.17238v1)

Abstract: Automated Machine Learning (AutoML) approaches encompass traditional methods that optimize fixed pipelines for model selection and ensembling, as well as newer LLM-based frameworks that autonomously build pipelines. While LLM-based agents have shown promise in automating machine learning tasks, they often generate low-diversity and suboptimal code, even after multiple iterations. To overcome these limitations, we introduce Tree-Search Enhanced LLM Agents (SELA), an innovative agent-based system that leverages Monte Carlo Tree Search (MCTS) to optimize the AutoML process. By representing pipeline configurations as trees, our framework enables agents to conduct experiments intelligently and iteratively refine their strategies, facilitating a more effective exploration of the machine learning solution space. This novel approach allows SELA to discover optimal pathways based on experimental feedback, improving the overall quality of the solutions. In an extensive evaluation across 20 machine learning datasets, we compare the performance of traditional and agent-based AutoML methods, demonstrating that SELA achieves a win rate of 65% to 80% against each baseline across all datasets. These results underscore the significant potential of agent-based strategies in AutoML, offering a fresh perspective on tackling complex machine learning challenges.

• The paper presents a novel integration of Monte Carlo Tree Search with LLM agents to systematically explore and refine machine learning pipeline configurations.
• The paper reports that SELA achieves win rates between 65% and 80% across 20 diverse datasets, outperforming comparable AutoML methods.
• The paper introduces a flexible, stage-wise experimentation method that iteratively adapts configurations based on empirical insights and cost-effective computation.

An Analytical Overview of SELA: Tree-Search Enhanced LLM Agents for AutoML

The paper presents an innovative approach to Automated Machine Learning (AutoML) with the integration of Tree-Search Enhanced LLM Agents, termed SELA. This work addresses notable limitations in existing LLM-based AutoML systems, such as their propensity for generating low-diversity, suboptimal code and their lack of an iterative refinement process. The authors propose SELA as a solution that leverages Monte Carlo Tree Search (MCTS) to optimize the exploration and selection of machine learning pipeline configurations, advancing the capabilities of AutoML systems.

Key Contributions

1. Novel Integration of MCTS and LLMs: SELA utilizes MCTS to model the AutoML process as a tree structure, where each path represents a potential pipeline configuration. This approach enables systematic exploration of the search space and allows SELA to iteratively refine solutions based on experimental feedback.
2. Improved AutoML Performance: The paper reports that SELA achieves a win rate of 65% to 80% against comparable methods across 20 diverse datasets. The results suggest that SELA's iterative design not only enhances performance but also adapts effectively to varied machine learning tasks.
3. Flexible, Stage-Wise Experimentation: SELA combines stage-wise planning and insight exploration, akin to human problem-solving tactics. It simulates new configurations iteratively, adjusting strategies based on empirical results using task-specific insights for exploratory data analysis, feature engineering, model training, and more.

Empirical Results and Analysis

Across multiple benchmarks, SELA demonstrates superior performance in terms of average normalized score (NS) and ranking compared to traditional agent-based and LLM-centric AutoML frameworks. The adaptability of SELA, attributed to its integration of MCTS, manifests through its leading position in seven of the 20 datasets evaluated, highlighting its robustness and practical utility.

Theoretical and Practical Implications

Theoretically, SELA raises the bar for the integration of traditional AI techniques with modern LLMs, paving the way for future research in marrying MCTS with LLMs for diverse AI-driven tasks. The approach not only enhances the exploration-exploitation dilemmas inherent in such systems but also introduces a structured methodology adaptable to various decision-making contexts.

Practically, the use of SELA in AutoML presents several advantages:

• Dynamic Exploration: Facilitates discovery of novel solutions by comprehensively navigating the machine learning solution space.
• Efficiency in Computation: Employs a cost-effective state-saving mechanism, ensuring minimal resource consumption during iterative runs.
• Versatility in Application: Although demonstrated in AutoML, the framework's underlying principles can potentially extend to other domains requiring sequential decision-making.

Future Trajectories

The paper opens avenues for enhancement in both the scalability and interpretability of ML strategies within AutoML frameworks. Future research may focus on:

• Expanding Domain Applications: Extending the SELA framework to areas such as robotics, game playing, and scientific discovery.
• Improving Scalability: Further optimizing the tree search algorithms for larger datasets and more complex problem spaces.
• Enhancing Transparency: Developing methods to interpret and explain the decision-making processes and outcomes produced by SELA.

In conclusion, SELA represents a significant advancement in the field of AutoML by effectively integrating MCTS with LLMs to augment automated machine learning capabilities. Its comprehensive evaluation demonstrates both its theoretical richness and practical utility, setting a precedence for future explorations in automating complex decision-making processes.