{"total":28,"rows":[{"id":"2109.01528","title":"LightAutoML: AutoML Solution for a Large Financial Services Ecosystem","created":"2021-09-03","cited_by_count":32,"area":null},{"id":"2111.05850","title":"Towards Green Automated Machine Learning: Status Quo and Future Directions","created":"2021-11-10","cited_by_count":25,"area":null},{"id":"2411.10478","title":"Large Language Models for Constructing and Optimizing Machine Learning Workflows: A Survey","created":"2024-11-11","cited_by_count":19,"area":null},{"id":"2206.02511","title":"Transfer Learning based Search Space Design for Hyperparameter Tuning","created":"2022-06-06","cited_by_count":13,"area":null},{"id":"2208.12433","title":"Towards Automated Imbalanced Learning with Deep Hierarchical Reinforcement Learning","created":"2022-08-26","cited_by_count":9,"area":null},{"id":"2407.01300","title":"Collaborative Performance Prediction for Large Language Models","created":"2024-07-01","cited_by_count":9,"area":null},{"id":"2111.14514","title":"Naive Automated Machine Learning","created":"2021-11-29","cited_by_count":8,"area":null},{"id":"2303.07669","title":"AutoTransfer: AutoML with Knowledge Transfer -- An Application to Graph Neural Networks","created":"2023-03-14","cited_by_count":8,"area":null},{"id":"2111.00083","title":"A Scalable AutoML Approach Based on Graph Neural Networks","created":"2021-10-29","cited_by_count":7,"area":null},{"id":"2208.12754","title":"Task Selection for AutoML System Evaluation","created":"2022-08-26","cited_by_count":7,"area":null},{"id":"2204.07615","title":"TabNAS: Rejection Sampling for Neural Architecture Search on Tabular Datasets","created":"2022-04-15","cited_by_count":6,"area":null},{"id":"2312.13038","title":"AutoXPCR: Automated Multi-Objective Model Selection for Time Series Forecasting","created":"2023-12-20","cited_by_count":6,"area":null},{"id":"2506.19540","title":"Overtuning in Hyperparameter Optimization","created":"2025-06-24","cited_by_count":5,"area":null},{"id":"2106.09179","title":"Amortized Auto-Tuning: Cost-Efficient Bayesian Transfer Optimization for Hyperparameter Recommendation","created":"2021-06-17","cited_by_count":4,"area":null},{"id":"2106.13743","title":"Privileged Zero-Shot AutoML","created":"2021-06-25","cited_by_count":4,"area":null},{"id":"2203.08399","title":"Privacy-preserving Online AutoML for Domain-Specific Face Detection","created":"2022-03-16","cited_by_count":4,"area":null},{"id":"2202.10451","title":"SapientML: Synthesizing Machine Learning Pipelines by Learning from Human-Written Solutions","created":"2022-02-18","cited_by_count":3,"area":null},{"id":"2402.04453","title":"The Potential of AutoML for Recommender Systems","created":"2024-02-06","cited_by_count":3,"area":null},{"id":"2106.09686","title":"How Low Can We Go: Trading Memory for Error in Low-Precision Training","created":"2021-06-17","cited_by_count":2,"area":null},{"id":"2305.14009","title":"Deep Pipeline Embeddings for AutoML","created":"2023-05-23","cited_by_count":2,"area":null},{"id":"2403.10318","title":"pTNAS: Progressive Neural Architecture Search for Tabular Data","created":"2024-03-15","cited_by_count":2,"area":null},{"id":"2103.10496","title":"Naive Automated Machine Learning -- A Late Baseline for AutoML","created":"2021-03-18","cited_by_count":1,"area":null},{"id":"2104.10746","title":"Automatic model training under restrictive time constraints","created":"2021-04-21","cited_by_count":1,"area":null},{"id":"2504.06207","title":"An experimental survey and Perspective View on Meta-Learning for Automated Algorithms Selection and Parametrization","created":"2025-04-08","cited_by_count":1,"area":null},{"id":"2109.14271","title":"(Machine) Learning to Improve the Empirical Performance of Discrete Algorithms","created":"2021-09-29","cited_by_count":0,"area":null},{"id":"2208.11792","title":"A Survey of Open Source Automation Tools for Data Science Predictions","created":"2022-08-24","cited_by_count":0,"area":null},{"id":"2407.12445","title":"A Comprehensive Sustainable Framework for Machine Learning and Artificial Intelligence","created":"2024-07-17","cited_by_count":0,"area":null},{"id":"2606.24679","title":"FlowPipe: LLM-Enhanced Conditional Generative Flow Networks for Data Preparation Pipeline Construction","created":"2026-06-23","cited_by_count":0,"area":null}],"next_cursor":null}