{"total":33,"rows":[{"id":"2310.05736","title":"LLMLingua: Compressing Prompts for Accelerated Inference of Large Language Models","created":"2023-10-09","cited_by_count":287,"area":null},{"id":"2410.20424","title":"AutoKaggle: A Multi-Agent Framework for Autonomous Data Science Competitions","created":"2024-10-27","cited_by_count":70,"area":null},{"id":"2504.17192","title":"Paper2Code: Automating Code Generation from Scientific Papers in Machine Learning","created":"2025-04-24","cited_by_count":70,"area":null},{"id":"2307.02779","title":"Large Language Models Empowered Autonomous Edge AI for Connected Intelligence","created":"2023-07-06","cited_by_count":56,"area":null},{"id":"2407.00981","title":"VisEval: A Benchmark for Data Visualization in the Era of Large Language Models","created":"2024-07-01","cited_by_count":45,"area":null},{"id":"2505.24785","title":"EXP-Bench: Can AI Conduct AI Research Experiments?","created":"2025-05-30","cited_by_count":37,"area":null},{"id":"2306.08107","title":"AutoML in the Age of Large Language Models: Current Challenges, Future Opportunities and Risks","created":"2023-06-13","cited_by_count":35,"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":"2411.17284","title":"AutoElicit: Using Large Language Models for Expert Prior Elicitation in Predictive Modelling","created":"2024-11-26","cited_by_count":15,"area":null},{"id":"2405.03727","title":"Large Language Models Synergize with Automated Machine Learning","created":"2024-05-06","cited_by_count":9,"area":null},{"id":"2412.02205","title":"DataLab: A Unified Platform for LLM-Powered Business Intelligence","created":"2024-12-03","cited_by_count":8,"area":null},{"id":"2503.01424","title":"From Hypothesis to Publication: A Comprehensive Survey of AI-Driven Research Support Systems","created":"2025-03-03","cited_by_count":8,"area":null},{"id":"2407.18690","title":"Collaborative Evolving Strategy for Automatic Data-Centric Development","created":"2024-07-26","cited_by_count":6,"area":null},{"id":"2312.14647","title":"Towards Message Brokers for Generative AI: Survey, Challenges, and Opportunities","created":"2023-12-22","cited_by_count":5,"area":null},{"id":"2412.06831","title":"TransitGPT: A Generative AI-based framework for interacting with GTFS data using Large Language Models","created":"2024-12-07","cited_by_count":5,"area":null},{"id":"2602.24288","title":"DARE-bench: Evaluating Modeling and Instruction Fidelity of LLMs in Data Science","created":"2026-02-27","cited_by_count":4,"area":null},{"id":"2606.30911","title":"Why Solve It Twice? Hierarchical Accumulation of Skills for Transfer-Efficient ML Engineering","created":"2026-06-29","cited_by_count":3,"area":null},{"id":"2504.15080","title":"Empowering AI to Generate Better AI Code: Guided Generation of Deep Learning Projects with LLMs","created":"2025-04-21","cited_by_count":2,"area":null},{"id":"2509.15561","title":"Small LLMs with Expert Blocks Are Good Enough for Hyperparamter Tuning","created":"2025-09-19","cited_by_count":2,"area":null},{"id":"2408.06717","title":"Proficient Graph Neural Network Design by Accumulating Knowledge on Large Language Models","created":"2024-08-13","cited_by_count":1,"area":null},{"id":"2409.03402","title":"Game On: Towards Language Models as RL Experimenters","created":"2024-09-05","cited_by_count":1,"area":null},{"id":"2410.22553","title":"ML Research Benchmark","created":"2024-10-29","cited_by_count":1,"area":null},{"id":"2601.15385","title":"VegaChat: A Robust Framework for LLM-Based Chart Generation and Assessment","created":"2026-01-21","cited_by_count":1,"area":null},{"id":"2604.14655","title":"AgentGA: Evolving Code Solutions in Agent-Seed Space","created":"2026-04-16","cited_by_count":1,"area":null},{"id":"2310.11249","title":"Leveraging Large Language Model for Automatic Evolving of Industrial Data-Centric R&D Cycle","created":"2023-10-17","cited_by_count":0,"area":null},{"id":"2405.16159","title":"A Declarative Query Language for Scientific Machine Learning","created":"2024-05-25","cited_by_count":0,"area":null},{"id":"2510.01842","title":"Pre-Hoc Predictions in AutoML: Leveraging LLMs to Enhance Model Selection and Benchmarking for Tabular datasets","created":"2025-10-02","cited_by_count":0,"area":null},{"id":"2601.21410","title":"Learning When to Trust LLM Priors: A Validated Framework for Semantic Prior Integration","created":"2026-01-29","cited_by_count":0,"area":null},{"id":"2602.13937","title":"iML: Executable, Problem-Grounded, and Broadly Exploratory Code-Driven AutoML","created":"2026-02-15","cited_by_count":0,"area":null},{"id":"2603.08181","title":"AutoAdapt: An Automated Domain Adaptation Framework for LLMs","created":"2026-03-09","cited_by_count":0,"area":null},{"id":"2608.16416","title":"Evolving Executable Pipeline Programs for AutoML with Language Models","created":"2026-08-17","cited_by_count":0,"area":null},{"id":"2609.22257","title":"Strategy Accumulation and Guided Execution for Automated LLM Fine-Tuning","created":"2026-09-06","cited_by_count":0,"area":null},{"id":"2609.35335","title":"Large Language Models for Automated Cross-Domain Machine Learning Task Type Identification: A Benchmark Dataset and Evaluation","created":"2026-09-28","cited_by_count":0,"area":null}],"next_cursor":null}