Cover Image

AutoML in the Age of Large Language Models: Current Challenges, Future Opportunities and Risks

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Tornede, Alexander, Deng, Difan, Eimer, Theresa, Giovanelli, Joseph, Mohan, Aditya, Ruhkopf, Tim, Segel, Sarah, Theodorakopoulos, Daphne, Tornede, Tanja, Wachsmuth, Henning, Lindauer, Marius
Format:	Preprint
Published:	2023
Subjects:	Machine Learning Computation and Language
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Interactive Hyperparameter Optimization in Multi-Objective Problems via Preference Learning
by: Giovanelli, Joseph, et al.
Published: (2023)

Towards Leveraging AutoML for Sustainable Deep Learning: A Multi-Objective HPO Approach on Deep Shift Neural Networks
by: Hennig, Leona, et al.
Published: (2024)

Hyperparameter Importance Analysis for Multi-Objective AutoML
by: Theodorakopoulos, Daphne, et al.
Published: (2024)

Position: A Call to Action for a Human-Centered AutoML Paradigm
by: Lindauer, Marius, et al.
Published: (2024)

DeepCAVE: A Visualization and Analysis Tool for Automated Machine Learning
by: Segel, Sarah, et al.
Published: (2025)

Dynamic Hyperparameter Importance for Efficient Multi-Objective Optimization
by: Theodorakopoulos, Daphne, et al.
Published: (2026)

AutoML for Multi-Class Anomaly Compensation of Sensor Drift
by: Schaller, Melanie, et al.
Published: (2025)

carps: A Framework for Comparing N Hyperparameter Optimizers on M Benchmarks
by: Benjamins, Carolin, et al.
Published: (2025)

Growing with Experience: Growing Neural Networks in Deep Reinforcement Learning
by: Fehring, Lukas, et al.
Published: (2025)

Revisiting Learning Rate Control
by: Henheik, Micha, et al.
Published: (2025)

Best Practices For Empirical Meta-Algorithmic Research: Guidelines from the COSEAL Research Network
by: Eimer, Theresa, et al.
Published: (2025)

Optimizing Time Series Forecasting Architectures: A Hierarchical Neural Architecture Search Approach
by: Deng, Difan, et al.
Published: (2024)

Neural Attention Search
by: Deng, Difan, et al.
Published: (2025)

AutoRL Hyperparameter Landscapes
by: Mohan, Aditya, et al.
Published: (2023)

Moments Matter:Stabilizing Policy Optimization using Return Distributions
by: Jabs, Dennis, et al.
Published: (2026)

Structure in Deep Reinforcement Learning: A Survey and Open Problems
by: Mohan, Aditya, et al.
Published: (2023)

Can Fairness be Automated? Guidelines and Opportunities for Fairness-aware AutoML
by: Weerts, Hilde, et al.
Published: (2023)

AutoML-Agent: A Multi-Agent LLM Framework for Full-Pipeline AutoML
by: Trirat, Patara, et al.
Published: (2024)

AutoIntent: AutoML for Text Classification
by: Alekseev, Ilya, et al.
Published: (2025)

When Are RL Hyperparameters Benign? A Study in Offline Goal-Conditioned RL
by: Töpperwien, Jan Malte, et al.
Published: (2026)

Problem-oriented AutoML in Clustering
by: da Silva, Matheus Camilo, et al.
Published: (2024)

AutoML in Cybersecurity: An Empirical Study
by: Saad, Sherif, et al.
Published: (2025)

The Potential of AutoML for Recommender Systems
by: Vente, Tobias, et al.
Published: (2024)

AutoML Systems For Medical Imaging
by: Jidney, Tasmia Tahmida, et al.
Published: (2023)

Dynamic Priors in Bayesian Optimization for Hyperparameter Optimization
by: Fehring, Lukas, et al.
Published: (2025)

AutoML in The Wild: Obstacles, Workarounds, and Expectations
by: Sun, Yuan, et al.
Published: (2023)

Robustness of AutoML on Dirty Categorical Data
by: Bueno, Marcos L. P., et al.
Published: (2026)

X Hacking: The Threat of Misguided AutoML
by: Sharma, Rahul, et al.
Published: (2024)

DREAM: Debugging and Repairing AutoML Pipelines
by: Zhang, Xiaoyu, et al.
Published: (2023)

ML2B: Multi-Lingual ML Benchmark For AutoML
by: Trofimova, Ekaterina, et al.
Published: (2025)

ZeroML: A Next Generation AutoML Language
by: Mahmud, Monirul Islam
Published: (2025)

ARLBench: Flexible and Efficient Benchmarking for Hyperparameter Optimization in Reinforcement Learning
by: Becktepe, Jannis, et al.
Published: (2024)

OpenAutoNLU: Open Source AutoML Library for NLU
by: Arshinov, Grigory, et al.
Published: (2026)

Exploring the impact of fairness-aware criteria in AutoML
by: Simões, Joana, et al.
Published: (2026)

NNGPT: Rethinking AutoML with Large Language Models
by: Kochnev, Roman, et al.
Published: (2025)

Budget-aware Query Tuning: An AutoML Perspective
by: Wu, Wentao, et al.
Published: (2024)

Optimal Pricing for Data-Augmented AutoML Marketplaces
by: Han, Minbiao, et al.
Published: (2023)

An AutoML-based approach for Network Intrusion Detection
by: Gyimah, Nana Kankam, et al.
Published: (2024)

AutoGluon-Multimodal (AutoMM): Supercharging Multimodal AutoML with Foundation Models
by: Tang, Zhiqiang, et al.
Published: (2024)

Explaining AutoClustering: Uncovering Meta-Feature Contribution in AutoML for Clustering
by: da Silva, Matheus Camilo, et al.
Published: (2026)