#automated-ml News & Analysis

5 articles tagged with #automated-ml. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

5 articles

AIBullisharXiv – CS AI · 4d ago7/10

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AIBuildAI-2: A Knowledge-Enhanced Agent for Automatically Building AI Models

AIBuildAI-2 introduces a knowledge-enhanced AI agent that automatically builds machine learning models by combining large language models with an external, evolving knowledge system. The system achieves state-of-the-art performance, ranking first on MLE-Bench and placing in the top 6.6% of human teams in a predictive competition, democratizing AI model development for non-specialists.

AIBullisharXiv – CS AI · May 97/10

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LLM-AutoDP: Automatic Data Processing via LLM Agents for Model Fine-tuning

Researchers introduce LLM-AutoDP, a framework that uses large language models as autonomous agents to automatically optimize data processing strategies for fine-tuning without human intervention or direct data exposure. The system achieves over 80% win rates against baseline models and reduces search time by up to 10x through novel acceleration techniques, addressing critical challenges in domain-specific model training and data privacy.

AINeutralarXiv – CS AI · May 126/10

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Budget-Efficient Automatic Algorithm Design via Code Graph

Researchers propose a budget-efficient automatic algorithm design framework using large language models that operates on code graphs rather than full algorithms. The approach uses LLMs to generate compact corrections—code modifications that add, replace, or remove blocks—which compose into new algorithms, reducing computational waste and improving fitness outcomes on combinatorial optimization problems.

AINeutralarXiv – CS AI · Apr 136/10

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Structured Exploration and Exploitation of Label Functions for Automated Data Annotation

Researchers introduce EXPONA, an automated framework for generating label functions that improve weak label quality in machine learning datasets. The system balances exploration across surface, structural, and semantic levels with reliability filtering, achieving up to 98.9% label coverage and 46% downstream performance improvements across diverse classification tasks.

AINeutralarXiv – CS AI · Mar 24/106

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Heterogeneous Multi-Agent Reinforcement Learning with Attention for Cooperative and Scalable Feature Transformation

Researchers propose a new multi-agent reinforcement learning framework that uses three cooperative agents with attention mechanisms to automate feature transformation for machine learning models. The approach addresses key limitations in existing automated feature engineering methods, including dynamic feature expansion instability and insufficient agent cooperation.