LLM-FE is a framework that treats feature engineering as LLM-driven program search with data feedback, reporting consistent gains over baselines on classification and regression tabular tasks.
Optuna: A next-generation hyperparameter optimization framework
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2025 2verdicts
UNVERDICTED 2representative citing papers
MORL generates more diverse requirement-violation scenarios while SORL produces higher-severity violations when testing interdependent requirements in an end-to-end AV controller.
citing papers explorer
-
LLM-FE: Automated Feature Engineering for Tabular Data with LLMs as Evolutionary Optimizers
LLM-FE is a framework that treats feature engineering as LLM-driven program search with data feedback, reporting consistent gains over baselines on classification and regression tabular tasks.
-
Reinforcement Learning for Testing Interdependent Requirements in Autonomous Vehicles: An Empirical Study
MORL generates more diverse requirement-violation scenarios while SORL produces higher-severity violations when testing interdependent requirements in an end-to-end AV controller.