Imagen de Portada

CoFEH: LLM-driven Feature Engineering Empowered by Collaborative Bayesian Hyperparameter Optimization

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Xu, Beicheng, Ding, Keyao, Liu, Wei, Lu, Yupeng, Cui, Bin
Formato:	Preprint
Publicado:	2026
Materias:	Machine Learning
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

PSEO: Optimizing Post-hoc Stacking Ensemble Through Hyperparameter Tuning
por: Xu, Beicheng, et al.
Publicado: (2025)

Tree-Structured Synergy of Large Language Models and Bayesian Optimization for Efficient CASH
por: Xu, Beicheng, et al.
Publicado: (2026)

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

In-Context Freeze-Thaw Bayesian Optimization for Hyperparameter Optimization
por: Rakotoarison, Herilalaina, et al.
Publicado: (2024)

ORTHOBO: Orthogonal Bayesian Hyperparameter Optimization
por: Schröder, Maresa, et al.
Publicado: (2026)

OpenBox: A Python Toolkit for Generalized Black-box Optimization
por: Jiang, Huaijun, et al.
Publicado: (2023)

Differentially Private Hyperparameter Tuning using Local Bayesian Optimization
por: Sopa, Getoar, et al.
Publicado: (2025)

Collaborative Contextual Bayesian Optimization
por: Chang, Chih-Yu, et al.
Publicado: (2026)

Human-LLM Collaborative Feature Engineering for Tabular Data
por: Li, Zhuoyan, et al.
Publicado: (2026)

Bayesian Optimization for Hyperparameters Tuning in Neural Networks
por: Onorato, Gabriele
Publicado: (2024)

Provably Efficient Bayesian Optimization with Unknown Gaussian Process Hyperparameter Estimation
por: Ha, Huong, et al.
Publicado: (2023)

Cost-Sensitive Freeze-thaw Bayesian Optimization for Efficient Hyperparameter Tuning
por: Lee, Dong Bok, et al.
Publicado: (2025)

Generative Bayesian Hyperparameter Tuning
por: Lopes, Hedibert, et al.
Publicado: (2025)

Overtuning in Hyperparameter Optimization
por: Schneider, Lennart, et al.
Publicado: (2025)

Hyperparameter Optimization for Randomized Algorithms: A Case Study on Random Features
por: Dunbar, Oliver R. A., et al.
Publicado: (2024)

Human-Algorithm Collaborative Bayesian Optimization for Engineering Systems
por: Savage, Tom, et al.
Publicado: (2024)

Hyperparameter Tuning Through Pessimistic Bilevel Optimization
por: Ustun, Meltem Apaydin, et al.
Publicado: (2024)

Bayesian Optimization for Simultaneous Selection of Machine Learning Algorithms and Hyperparameters on Shared Latent Space
por: Ishikawa, Kazuki, et al.
Publicado: (2025)

Hyperparameter Optimization in Machine Learning
por: Franceschi, Luca, et al.
Publicado: (2024)

Co-Learning Bayesian Optimization
por: Guo, Zhendong, et al.
Publicado: (2025)

Default Machine Learning Hyperparameters Do Not Provide Informative Initialization for Bayesian Optimization
por: Prieto, Nicolás Villagrán, et al.
Publicado: (2026)

CRoPE: Efficient Parametrization of Rotary Positional Embedding
por: Lou, Beicheng, et al.
Publicado: (2026)

The Sensitivity of Variational Bayesian Neural Network Performance to Hyperparameters
por: Koermer, Scott, et al.
Publicado: (2025)

A Meta-Knowledge-Augmented LLM Framework for Hyperparameter Optimization in Time-Series Forecasting
por: Saadallah, Ons, et al.
Publicado: (2026)

A Trajectory-Based Bayesian Approach to Multi-Objective Hyperparameter Optimization with Epoch-Aware Trade-Offs
por: Wang, Wenyu, et al.
Publicado: (2024)

Analyzing the Role of Permutation Invariance in Linear Mode Connectivity
por: Zhan, Keyao, et al.
Publicado: (2025)

Towards Fair and Rigorous Evaluations: Hyperparameter Optimization for Top-N Recommendation Task with Implicit Feedback
por: Fang, Hui, et al.
Publicado: (2024)

Enhancing Performance and Calibration in Quantile Hyperparameter Optimization
por: Doyle, Riccardo
Publicado: (2025)

Adaptive Hyperparameter Optimization for Continual Learning Scenarios
por: Semola, Rudy, et al.
Publicado: (2024)

Shapley-Inspired Feature Weighting in $k$-means with No Additional Hyperparameters
por: Fawley, Richard J., et al.
Publicado: (2025)

Revisiting Hyperparameter Tuning with Differential Privacy
por: Ding, Youlong, et al.
Publicado: (2022)

Bayesian Optimisation with Unknown Hyperparameters: Regret Bounds Logarithmically Closer to Optimal
por: Ziomek, Juliusz, et al.
Publicado: (2024)

Universally Empowering Zeroth-Order Optimization via Adaptive Layer-wise Sampling
por: Wang, Fei, et al.
Publicado: (2026)

Calibrated Dataset Condensation for Faster Hyperparameter Search
por: Ding, Mucong, et al.
Publicado: (2024)

On Optimizing Hyperparameters for Quantum Neural Networks
por: Herbst, Sabrina, et al.
Publicado: (2024)

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

Multi-Objective Hyperparameter Optimization in Machine Learning -- An Overview
por: Karl, Florian, et al.
Publicado: (2022)

Practitioner Motives to Use Different Hyperparameter Optimization Methods
por: Kannengießer, Niclas, et al.
Publicado: (2022)

HyperQ-Opt: Q-learning for Hyperparameter Optimization
por: Hasan, Md. Tarek
Publicado: (2024)

The Silent Hyperparameter: Quantifying the Impact of Inference Backends on LLM Reproducibility
por: Pape, David, et al.
Publicado: (2026)