Reasoning LLMs with minimal tools for tree construction and analysis induce decision trees that outperform CART, compete with ensembles on low-resource tabular data, and provide human-readable reasoning traces.
Context-aware automated feature engineering (caafe)
4 Pith papers cite this work, alongside 7 external citations. Polarity classification is still indexing.
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UNVERDICTED 4representative citing papers
An LLM-orchestrated multi-agent framework for end-to-end BDaaS automation with drift awareness is proposed and evaluated on tabular benchmarks for improved lifecycle reliability over baselines.
AgentGA optimizes agent seeds with genetic algorithms and parent-archive inheritance to improve autonomous code generation, beating a baseline on 15 of 16 Kaggle competitions.
PS-PFN extends posterior sampling to the max k-armed bandit setup using PFNs for in-context posterior estimation of maximal pipeline performance, outperforming other bandit and AutoML strategies on benchmarks.
citing papers explorer
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Talking Trees: Reasoning-Assisted Induction of Decision Trees for Tabular Data
Reasoning LLMs with minimal tools for tree construction and analysis induce decision trees that outperform CART, compete with ensembles on low-resource tabular data, and provide human-readable reasoning traces.
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Trustworthy Self-Composable Big-Data-as-a-Service: An LLM-Orchestrated Multi-Agent Framework for Automated Data Engineering, AutoML, MLOps Deployment, and Drift-Aware Lifecycle Optimization
An LLM-orchestrated multi-agent framework for end-to-end BDaaS automation with drift awareness is proposed and evaluated on tabular benchmarks for improved lifecycle reliability over baselines.
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AgentGA: Evolving Code Solutions in Agent-Seed Space
AgentGA optimizes agent seeds with genetic algorithms and parent-archive inheritance to improve autonomous code generation, beating a baseline on 15 of 16 Kaggle competitions.
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In-Context Decision Making for Optimizing Complex AutoML Pipelines
PS-PFN extends posterior sampling to the max k-armed bandit setup using PFNs for in-context posterior estimation of maximal pipeline performance, outperforming other bandit and AutoML strategies on benchmarks.