29 citations · 64 across the 17 of their papers we have counts for
5 papers · 1 filter
MLE-STAR: Machine Learning Engineering Agent via Search and Targeted Refinement
Jaehyun Nam, Jinsung Yoon, Jiefeng Chen +3
Agents based on large language models (LLMs) for machine learning engineering (MLE) can automatically implement ML models via code generation. However, existing approaches to build…
Learned Feature Importance Scores for Automated Feature Engineering
Yihe Dong, Sercan Arik, Nathanael Yoder +1
Feature engineering has demonstrated substantial utility for many machine learning workflows, such as in the small data regime or when distribution shifts are severe. Thus automati…
Large Language Models Can Automatically Engineer Features for Few-Shot Tabular Learning
Sungwon Han, Jinsung Yoon, Sercan O Arik +1
Large Language Models (LLMs), with their remarkable ability to tackle challenging and unseen reasoning problems, hold immense potential for tabular learning, that is vital for many…
COSTAR: Improved Temporal Counterfactual Estimation with Self-Supervised Learning
Chuizheng Meng, Yihe Dong, Sercan Ö. Arık +2
Estimation of temporal counterfactual outcomes from observed history is crucial for decision-making in many domains such as healthcare and e-commerce, particularly when randomized…
Search-Adaptor: Embedding Customization for Information Retrieval
Jinsung Yoon, Sercan O Arik, Yanfei Chen +1
Embeddings extracted by pre-trained Large Language Models (LLMs) have significant potential to improve information retrieval and search. Beyond the zero-shot setup in which they ar…