LeJOT-AutoML: LLM-Driven Feature Engineering for Job Execution Time Prediction in Databricks Cost Optimization
Fuente:
arXiv
Saved in:
| Main Authors: | Ma, Lizhi, Hu, Yi-Xiang, Ren, Yihui, Wu, Feng, Li, Xiang-Yang |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
LeJOT: An Intelligent Job Cost Orchestration Solution for Databricks Platform
by: Ma, Lizhi, et al.
Published: (2025)
by: Ma, Lizhi, et al.
Published: (2025)
iML: Executable, Problem-Grounded, and Broadly Exploratory Code-Driven AutoML
by: Le, Dat, et al.
Published: (2026)
by: Le, Dat, et al.
Published: (2026)
Assessing the Use of AutoML for Data-Driven Software Engineering
by: Calefato, Fabio, et al.
Published: (2023)
by: Calefato, Fabio, et al.
Published: (2023)
AutoML-Agent: A Multi-Agent LLM Framework for Full-Pipeline AutoML
by: Trirat, Patara, et al.
Published: (2024)
by: Trirat, Patara, et al.
Published: (2024)
AMC: AutoML for Model Compression and Acceleration on Mobile Devices
by: He, Yihui, et al.
Published: (2018)
by: He, Yihui, et al.
Published: (2018)
DREAM: Debugging and Repairing AutoML Pipelines
by: Zhang, Xiaoyu, et al.
Published: (2023)
by: Zhang, Xiaoyu, et al.
Published: (2023)
AutoML in The Wild: Obstacles, Workarounds, and Expectations
by: Sun, Yuan, et al.
Published: (2023)
by: Sun, Yuan, et al.
Published: (2023)
AutoIntent: AutoML for Text Classification
by: Alekseev, Ilya, et al.
Published: (2025)
by: Alekseev, Ilya, et al.
Published: (2025)
A Reproducible Log-Driven AutoML Framework for Interpretable Pipeline Optimization in Healthcare Risk Prediction
by: Huang, Rui, et al.
Published: (2026)
by: Huang, Rui, et al.
Published: (2026)
Explaining AutoClustering: Uncovering Meta-Feature Contribution in AutoML for Clustering
by: da Silva, Matheus Camilo, et al.
Published: (2026)
by: da Silva, Matheus Camilo, et al.
Published: (2026)
Problem-oriented AutoML in Clustering
by: da Silva, Matheus Camilo, et al.
Published: (2024)
by: da Silva, Matheus Camilo, et al.
Published: (2024)
AutoML in Cybersecurity: An Empirical Study
by: Saad, Sherif, et al.
Published: (2025)
by: Saad, Sherif, et al.
Published: (2025)
The Potential of AutoML for Recommender Systems
by: Vente, Tobias, et al.
Published: (2024)
by: Vente, Tobias, et al.
Published: (2024)
AutoML Systems For Medical Imaging
by: Jidney, Tasmia Tahmida, et al.
Published: (2023)
by: Jidney, Tasmia Tahmida, et al.
Published: (2023)
Confidence Interval Estimation of Predictive Performance in the Context of AutoML
by: Paraschakis, Konstantinos, et al.
Published: (2024)
by: Paraschakis, Konstantinos, et al.
Published: (2024)
In-Context Decision Making for Optimizing Complex AutoML Pipelines
by: Balef, Amir Rezaei, et al.
Published: (2025)
by: Balef, Amir Rezaei, et al.
Published: (2025)
AutoProteinEngine: A Large Language Model Driven Agent Framework for Multimodal AutoML in Protein Engineering
by: Liu, Yungeng, et al.
Published: (2024)
by: Liu, Yungeng, et al.
Published: (2024)
Predicting Mortgage Default with Machine Learning: AutoML, Class Imbalance, and Leakage Control
by: Hu, Xianghong, et al.
Published: (2026)
by: Hu, Xianghong, et al.
Published: (2026)
Robustness of AutoML on Dirty Categorical Data
by: Bueno, Marcos L. P., et al.
Published: (2026)
by: Bueno, Marcos L. P., et al.
Published: (2026)
X Hacking: The Threat of Misguided AutoML
by: Sharma, Rahul, et al.
Published: (2024)
by: Sharma, Rahul, et al.
Published: (2024)
ML2B: Multi-Lingual ML Benchmark For AutoML
by: Trofimova, Ekaterina, et al.
Published: (2025)
by: Trofimova, Ekaterina, et al.
Published: (2025)
Auto-ADMET: An Effective and Interpretable AutoML Method for Chemical ADMET Property Prediction
by: de Sá, Alex G. C., et al.
Published: (2025)
by: de Sá, Alex G. C., et al.
Published: (2025)
Auto-Unrolled Proximal Gradient Descent: An AutoML Approach to Interpretable Waveform Optimization
by: Kaplan, Ahmet
Published: (2026)
by: Kaplan, Ahmet
Published: (2026)
ZeroML: A Next Generation AutoML Language
by: Mahmud, Monirul Islam
Published: (2025)
by: Mahmud, Monirul Islam
Published: (2025)
AutoGluon-Multimodal (AutoMM): Supercharging Multimodal AutoML with Foundation Models
by: Tang, Zhiqiang, et al.
Published: (2024)
by: Tang, Zhiqiang, et al.
Published: (2024)
Engineering Trustworthy Automation: Design Principles and Evaluation for AutoML Tools for Novices
by: Thys, Jarne, et al.
Published: (2025)
by: Thys, Jarne, et al.
Published: (2025)
AutoML‐Driven Soft Sensors for Real‐Time Monitoring of Amino Acids in Mammalian Perfusion Cultures
by: Sun‐Jong Kim, et al.
Published: (2025)
by: Sun‐Jong Kim, et al.
Published: (2025)
AutoML-guided Fusion of Entity and LLM-based Representations for Document Classification
by: Koloski, Boshko, et al.
Published: (2024)
by: Koloski, Boshko, et al.
Published: (2024)
OpenAutoNLU: Open Source AutoML Library for NLU
by: Arshinov, Grigory, et al.
Published: (2026)
by: Arshinov, Grigory, et al.
Published: (2026)
Exploring the impact of fairness-aware criteria in AutoML
by: Simões, Joana, et al.
Published: (2026)
by: Simões, Joana, et al.
Published: (2026)
NNGPT: Rethinking AutoML with Large Language Models
by: Kochnev, Roman, et al.
Published: (2025)
by: Kochnev, Roman, et al.
Published: (2025)
Budget-aware Query Tuning: An AutoML Perspective
by: Wu, Wentao, et al.
Published: (2024)
by: Wu, Wentao, et al.
Published: (2024)
Optimal Pricing for Data-Augmented AutoML Marketplaces
by: Han, Minbiao, et al.
Published: (2023)
by: Han, Minbiao, et al.
Published: (2023)
Hyperparameter Importance Analysis for Multi-Objective AutoML
by: Theodorakopoulos, Daphne, et al.
Published: (2024)
by: Theodorakopoulos, Daphne, et al.
Published: (2024)
An AutoML-based approach for Network Intrusion Detection
by: Gyimah, Nana Kankam, et al.
Published: (2024)
by: Gyimah, Nana Kankam, et al.
Published: (2024)
Predicting delays in Indian lower courts using AutoML and Decision Forests
by: Bhatnagar, Mohit, et al.
Published: (2023)
by: Bhatnagar, Mohit, et al.
Published: (2023)
Bifurcation-Based Hyperparameter Optimization: A Dynamical Systems Approach to AutoML
by: Kodsi, Adil
Published: (2025)
by: Kodsi, Adil
Published: (2025)
Enabling AutoML for Zero-Touch Network Security: Use-Case Driven Analysis
by: Yang, Li, et al.
Published: (2025)
by: Yang, Li, et al.
Published: (2025)
Leveraging Ethical Narratives to Enhance LLM‐AutoML Generated Machine Learning Models
by: Jordan Nelson, et al.
Published: (2025)
by: Jordan Nelson, et al.
Published: (2025)
Explainable AutoML (xAutoML) with adaptive modeling for yield enhancement in semiconductor smart manufacturing
by: Zhai, Weihong, et al.
Published: (2024)
by: Zhai, Weihong, et al.
Published: (2024)
Similar Items
-
LeJOT: An Intelligent Job Cost Orchestration Solution for Databricks Platform
by: Ma, Lizhi, et al.
Published: (2025) -
iML: Executable, Problem-Grounded, and Broadly Exploratory Code-Driven AutoML
by: Le, Dat, et al.
Published: (2026) -
Assessing the Use of AutoML for Data-Driven Software Engineering
by: Calefato, Fabio, et al.
Published: (2023) -
AutoML-Agent: A Multi-Agent LLM Framework for Full-Pipeline AutoML
by: Trirat, Patara, et al.
Published: (2024) -
AMC: AutoML for Model Compression and Acceleration on Mobile Devices
by: He, Yihui, et al.
Published: (2018)