Image de couverture de livre

AutoML-Agent: A Multi-Agent LLM Framework for Full-Pipeline AutoML

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Trirat, Patara, Jeong, Wonyong, Hwang, Sung Ju
Format:	Preprint
Publié:	2024
Sujets:	Machine Learning Artificial Intelligence Computation and Language Multiagent Systems
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Multi-View Encoders for Performance Prediction in LLM-Based Agentic Workflows
par: Trirat, Patara, et autres
Publié: (2025)

Towards Reliable ML Feature Engineering via Planning in Constrained-Topology of LLM Agents
par: Thakur, Himanshu, et autres
Publié: (2026)

UniAutoML: A Human-Centered Framework for Unified Discriminative and Generative AutoML with Large Language Models
par: Guo, Jiayi, et autres
Publié: (2024)

Problem-oriented AutoML in Clustering
par: da Silva, Matheus Camilo, et autres
Publié: (2024)

In-Context Decision Making for Optimizing Complex AutoML Pipelines
par: Balef, Amir Rezaei, et autres
Publié: (2025)

TS-Debate: Multimodal Collaborative Debate for Zero-Shot Time Series Reasoning
par: Trirat, Patara, et autres
Publié: (2026)

Hyperparameter Importance Analysis for Multi-Objective AutoML
par: Theodorakopoulos, Daphne, et autres
Publié: (2024)

AMQ: Enabling AutoML for Mixed-precision Weight-Only Quantization of Large Language Models
par: Lee, Sangjun, et autres
Publié: (2025)

Robustness of AutoML on Dirty Categorical Data
par: Bueno, Marcos L. P., et autres
Publié: (2026)

NNGPT: Rethinking AutoML with Large Language Models
par: Kochnev, Roman, et autres
Publié: (2025)

AutoMind: Adaptive Knowledgeable Agent for Automated Data Science
par: Ou, Yixin, et autres
Publié: (2025)

LLM Agents Making Agent Tools
par: Wölflein, Georg, et autres
Publié: (2025)

AutoML for Multi-Class Anomaly Compensation of Sensor Drift
par: Schaller, Melanie, et autres
Publié: (2025)

Exploring the impact of fairness-aware criteria in AutoML
par: Simões, Joana, et autres
Publié: (2026)

AutoSurrogate: An LLM-Driven Multi-Agent Framework for Autonomous Construction of Deep Learning Surrogate Models in Subsurface Flow
par: Liu, Jiale, et autres
Publié: (2026)

$\textit{Agents Under Siege}$: Breaking Pragmatic Multi-Agent LLM Systems with Optimized Prompt Attacks
par: Khan, Rana Muhammad Shahroz, et autres
Publié: (2025)

OpenAutoNLU: Open Source AutoML Library for NLU
par: Arshinov, Grigory, et autres
Publié: (2026)

AutoAct: Automatic Agent Learning from Scratch for QA via Self-Planning
par: Qiao, Shuofei, et autres
Publié: (2024)

Can Agents Judge Systematic Reviews Like Humans? Evaluating SLRs with LLM-based Multi-Agent System
par: Mushtaq, Abdullah, et autres
Publié: (2025)

Symphony: A Decentralized Multi-Agent Framework for Scalable Collective Intelligence
par: Wang, Ji, et autres
Publié: (2025)

TrustAgent: Towards Safe and Trustworthy LLM-based Agents
par: Hua, Wenyue, et autres
Publié: (2024)

MASPO: Joint Prompt Optimization for LLM-based Multi-Agent Systems
par: Wang, Zhexuan, et autres
Publié: (2026)

An Adversary-Resistant Multi-Agent LLM System via Credibility Scoring
par: Ebrahimi, Sana, et autres
Publié: (2025)

Opponent Shaping in LLM Agents
par: Segura, Marta Emili Garcia, et autres
Publié: (2025)

Budget-aware Query Tuning: An AutoML Perspective
par: Wu, Wentao, et autres
Publié: (2024)

GAMBIT: A Three-Mode Benchmark for Adversarial Robustness in Multi-Agent LLM Collectives
par: Mercier, Alexandre Le, et autres
Publié: (2026)

Dive into the Agent Matrix: A Realistic Evaluation of Self-Replication Risk in LLM Agents
par: Zhang, Boxuan, et autres
Publié: (2025)

AutoGluon-Multimodal (AutoMM): Supercharging Multimodal AutoML with Foundation Models
par: Tang, Zhiqiang, et autres
Publié: (2024)

Multi-Agent Design: Optimizing Agents with Better Prompts and Topologies
par: Zhou, Han, et autres
Publié: (2025)

Collaborative Memory: Multi-User Memory Sharing in LLM Agents with Dynamic Access Control
par: Rezazadeh, Alireza, et autres
Publié: (2025)

ZeroML: A Next Generation AutoML Language
par: Mahmud, Monirul Islam
Publié: (2025)

AutoML-guided Fusion of Entity and LLM-based Representations for Document Classification
par: Koloski, Boshko, et autres
Publié: (2024)

A Reproducible Log-Driven AutoML Framework for Interpretable Pipeline Optimization in Healthcare Risk Prediction
par: Huang, Rui, et autres
Publié: (2026)

Self-Organized Agents: A LLM Multi-Agent Framework toward Ultra Large-Scale Code Generation and Optimization
par: Ishibashi, Yoichi, et autres
Publié: (2024)

Deciphering AutoML Ensembles: cattleia's Assistance in Decision-Making
par: Kozak, Anna, et autres
Publié: (2024)

Harnessing Multi-Agent LLMs for Complex Engineering Problem-Solving: A Framework for Senior Design Projects
par: Mushtaq, Abdullah, et autres
Publié: (2025)

Towards Efficient LLM Grounding for Embodied Multi-Agent Collaboration
par: Zhang, Yang, et autres
Publié: (2024)

MAC: Multi-Agent Constitution Learning
par: Thareja, Rushil, et autres
Publié: (2026)

MAEBE: Multi-Agent Emergent Behavior Framework
par: Erisken, Sinem, et autres
Publié: (2025)

SPIO: Ensemble and Selective Strategies via LLM-Based Multi-Agent Planning in Automated Data Science
par: Seo, Wonduk, et autres
Publié: (2025)