{"total":80,"rows":[{"id":"2405.01147","title":"Why Tabular Foundation Models Should Be a Research Priority","created":"2024-05-02","cited_by_count":106,"area":null},{"id":"2312.08782","title":"Toward General-Purpose Robots via Foundation Models: A Survey and Meta-Analysis","created":"2023-12-14","cited_by_count":103,"area":null},{"id":"2503.14434","title":"LLM-FE: Automated Feature Engineering for Tabular Data with LLMs as Evolutionary Optimizers","created":"2025-03-18","cited_by_count":38,"area":null},{"id":"2404.06209","title":"Elephants Never Forget: Memorization and Learning of Tabular Data in Large Language Models","created":"2024-04-09","cited_by_count":36,"area":null},{"id":"2504.16109","title":"Representation Learning for Tabular Data: A Comprehensive Survey","created":"2025-04-17","cited_by_count":36,"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":"2505.18125","title":"TabSTAR: A Tabular Foundation Model for Tabular Data with Text Fields","created":"2025-05-23","cited_by_count":18,"area":null},{"id":"2407.02112","title":"A Data-Centric Perspective on Evaluating Machine Learning Models for Tabular Data","created":"2024-07-02","cited_by_count":15,"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":"2312.09634","title":"Vectorizing string entries for data processing on tables: when are larger language models better?","created":"2023-12-15","cited_by_count":10,"area":null},{"id":"2410.17787","title":"Large Language Models Engineer Too Many Simple Features For Tabular Data","created":"2024-10-23","cited_by_count":10,"area":null},{"id":"2504.02767","title":"How Deep Do Large Language Models Internalize Scientific Literature and Citation Practices?","created":"2025-04-03","cited_by_count":10,"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":"2410.01795","title":"Knowledge-Driven Feature Selection and Engineering for Genotype Data with Large Language Models","created":"2024-10-02","cited_by_count":9,"area":null},{"id":"2511.01716","title":"SemBench: A Benchmark for Semantic Query Processing Engines","created":"2025-11-03","cited_by_count":9,"area":null},{"id":"2402.18609","title":"ICE-SEARCH: A Language Model-Driven Feature Selection Approach","created":"2024-02-28","cited_by_count":8,"area":null},{"id":"2407.09502","title":"From Text to Life: On the Reciprocal Relationship between Artificial Life and Large Language Models","created":"2024-06-14","cited_by_count":8,"area":null},{"id":"2606.26859","title":"AgentX: Towards Agent-Driven Self-Iteration of Industrial Recommender Systems","created":"2026-06-25","cited_by_count":8,"area":null},{"id":"2501.15282","title":"AutoG: Towards automatic graph construction from tabular data","created":"2025-01-25","cited_by_count":7,"area":null},{"id":"2502.18530","title":"IMPROVE: Iterative Model Pipeline Refinement and Optimization Leveraging LLM Experts","created":"2025-02-25","cited_by_count":7,"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":"2409.18475","title":"Data Analysis in the Era of Generative AI","created":"2024-09-27","cited_by_count":6,"area":null},{"id":"2601.12906","title":"Gated Differentiable Working Memory for Long-Context Language Modeling","created":"2026-01-19","cited_by_count":6,"area":null},{"id":"2609.37959","title":"TabFM: A Zero-Shot Foundation Model for Tabular Data","created":"2026-09-29","cited_by_count":6,"area":null},{"id":"2503.20394","title":"FastFT: Accelerating Reinforced Feature Transformation via Advanced Exploration Strategies","created":"2025-03-26","cited_by_count":5,"area":null},{"id":"2506.09085","title":"LLM-ML Teaming: Integrated Symbolic Decoding and Gradient Search for Valid and Stable Generative Feature Transformation","created":"2025-06-10","cited_by_count":5,"area":null},{"id":"2605.10906","title":"DataMaster: Data-Centric Autonomous AI Research","created":"2026-05-11","cited_by_count":5,"area":null},{"id":"2502.02672","title":"Transformers Boost the Performance of Decision Trees on Tabular Data across Sample Sizes","created":"2025-02-04","cited_by_count":4,"area":null},{"id":"2502.16395","title":"AIRepr: An Analyst-Inspector Framework for Evaluating Reproducibility of LLMs in Data Science","created":"2025-02-23","cited_by_count":4,"area":null},{"id":"2602.17641","title":"FAMOSE: A ReAct Approach to Automated Feature Discovery","created":"2026-02-19","cited_by_count":4,"area":null},{"id":"2508.14667","title":"ELATE: Evolutionary Language model for Automated Time-series Engineering","created":"2025-08-20","cited_by_count":3,"area":null},{"id":"2510.26510","title":"LLMs as In-Context Meta-Learners for Model and Hyperparameter Selection","created":"2025-10-30","cited_by_count":3,"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":"2410.23331","title":"Can Models Help Us Create Better Models? Evaluating LLMs as Data Scientists","created":"2024-10-30","cited_by_count":2,"area":null},{"id":"2504.17355","title":"Collaborative Multi-Agent Reinforcement Learning for Automated Feature Transformation with Graph-Driven Path Optimization","created":"2025-04-24","cited_by_count":2,"area":null},{"id":"2508.09263","title":"LLM Empowered Prototype Learning for Zero and Few-Shot Tasks on Tabular Data","created":"2025-08-12","cited_by_count":2,"area":null},{"id":"2601.12355","title":"Tree-Structured Synergy of Large Language Models and Bayesian Optimization for Efficient CASH","created":"2026-01-18","cited_by_count":2,"area":null},{"id":"2602.11089","title":"DataChef: Cooking Up Optimal Data Recipes for LLM Adaptation via Reinforcement Learning","created":"2026-02-11","cited_by_count":2,"area":null},{"id":"2410.12841","title":"UniAutoML: A Human-Centered Framework for Unified Discriminative and Generative AutoML with Large Language Models","created":"2024-10-09","cited_by_count":1,"area":null},{"id":"2412.04404","title":"Federated Automated Feature Engineering","created":"2024-12-05","cited_by_count":1,"area":null},{"id":"2508.13932","title":"How Usable is Automated Feature Engineering for Tabular Data?","created":"2025-08-19","cited_by_count":1,"area":null},{"id":"2509.05772","title":"Decision-Focused Learning Enhanced by Automated Feature Engineering for Energy Storage Optimisation","created":"2025-09-06","cited_by_count":1,"area":null},{"id":"2509.09245","title":"Jupiter: Enhancing LLM Data Analysis Capabilities via Notebook and Inference-Time Value-Guided Search","created":"2025-09-11","cited_by_count":1,"area":null},{"id":"2509.25207","title":"Multi-level Diagnosis and Evaluation for Robust Tabular Feature Engineering with Large Language Models","created":"2025-09-20","cited_by_count":1,"area":null},{"id":"2601.10457","title":"NSR-Boost: A Neuro-Symbolic Residual Boosting Framework for Industrial Legacy Models","created":"2026-01-15","cited_by_count":1,"area":null},{"id":"2601.21060","title":"Human-LLM Collaborative Feature Engineering for Tabular Data","created":"2026-01-28","cited_by_count":1,"area":null},{"id":"2603.15713","title":"Embedding-Aware Feature Discovery: Bridging Latent Representations and Interpretable Features in Event Sequences","created":"2026-03-16","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":"2604.22534","title":"FeatEHR-LLM: Leveraging Large Language Models for Feature Engineering in Electronic Health Records","created":"2026-04-24","cited_by_count":1,"area":null},{"id":"2609.21894","title":"LLMs as Feature Engineers for Text-and-Tabular Prediction","created":"2026-09-18","cited_by_count":1,"area":null},{"id":"2407.02203","title":"Automatic Adaptation Rule Optimization via Large Language Models","created":"2024-07-02","cited_by_count":0,"area":null},{"id":"2411.01023","title":"Capturing and Anticipating User Intents in Data Analytics via Knowledge Graphs","created":"2024-11-01","cited_by_count":0,"area":null},{"id":"2507.17979","title":"SIFOTL: A Principled, Statistically-Informed Fidelity-Optimization Method for Tabular Learning","created":"2025-07-23","cited_by_count":0,"area":null},{"id":"2508.04279","title":"Mockingbird: How does LLM perform in general machine learning tasks?","created":"2025-08-06","cited_by_count":0,"area":null},{"id":"2508.13657","title":"In-Context Decision Making for Optimizing Complex AutoML Pipelines","created":"2025-08-19","cited_by_count":0,"area":null},{"id":"2512.08379","title":"DeepFeature: LLM-Empowered Context-aware Feature Generation for Wearable Biosignals","created":"2025-12-09","cited_by_count":0,"area":null},{"id":"2602.09851","title":"CoFEH: LLM-driven Feature Engineering Empowered by Collaborative Bayesian Hyperparameter Optimization","created":"2026-02-10","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":"2603.02221","title":"MedFeat: Model-Aware and Explainability-Driven Feature Engineering with LLMs for Tabular Prediction","created":"2026-02-10","cited_by_count":0,"area":null},{"id":"2603.06991","title":"Adaptive Discovery of Interpretable Audio Attributes with Multimodal LLMs for Low-Resource Classification","created":"2026-03-07","cited_by_count":0,"area":null},{"id":"2603.07897","title":"LeJOT-AutoML: LLM-Driven Feature Engineering for Job Execution Time Prediction in Databricks Cost Optimization","created":"2026-03-09","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":"2605.14761","title":"AI Outperforms Humans in Personalized Image Aesthetics Assessment via LLM-Based Interviews and Semantic Feature Extraction","created":"2026-05-14","cited_by_count":0,"area":null},{"id":"2605.18759","title":"Interoceptive Divergence in Aesthetic Evaluation and Implications for Human-AI Alignment","created":"2026-04-05","cited_by_count":0,"area":null},{"id":"2606.09004","title":"LATTEArena: An Evaluation Framework for LLM-powered Tabular Feature Engineering (Extended Version)","created":"2026-06-08","cited_by_count":0,"area":null},{"id":"2606.23533","title":"POTracker: Optimizing Large Language Models for Standard-Compliant Power Outage Report Generation","created":"2026-06-22","cited_by_count":0,"area":null},{"id":"2607.01548","title":"Evolutionary Feature Engineering for Structured Data","created":"2026-07-02","cited_by_count":0,"area":null},{"id":"2607.23286","title":"Adaptive Graph-of-Islands Evolution for Automatic Feature Engineering with LLMs","created":"2026-07-25","cited_by_count":0,"area":null},{"id":"2607.27389","title":"FunL2O: LLM-Guided Feature Function Design for Learning to Optimize","created":"2026-07-29","cited_by_count":0,"area":null},{"id":"2608.00042","title":"Trustworthiness Costs of Domain Adaptation in Small Language Models:A Cross-Architecture Empirical Study","created":"2026-07-23","cited_by_count":0,"area":null},{"id":"2608.07881","title":"GRACE: LLM-Grounded Semantic Metric Spaces for Scalable Mixed-Data Clustering","created":"2026-08-08","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":"2608.17948","title":"SIGMA: SHAP-Guided Implicit-Trajectory Generation for Metadata-Free LLM-Based AutoFE","created":"2026-08-18","cited_by_count":0,"area":null},{"id":"2608.28408","title":"SymboLLM-FE: LLM-Accelerated Symbolic Regression for Automated Feature Engineering on Tabular Data","created":"2026-08-28","cited_by_count":0,"area":null},{"id":"2609.12455","title":"SAGE-Loop: Reliable Closed-Loop LLM-Driven AutoML with Trial-and-Correction and Adaptive Ensembling","created":"2026-09-11","cited_by_count":0,"area":null},{"id":"2609.22196","title":"EvoRank: LLM-Guided Evolution of Multi-Objective Learning-to-Rank Pipelines","created":"2026-08-30","cited_by_count":0,"area":null},{"id":"2609.23097","title":"An LLM-Assisted AutoML Framework for Intrusion Detection in IoT Networks","created":"2026-09-19","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},{"id":"2610.01627","title":"What Makes Something Hard(er)? Explaining Question Difficulty in Natural Language","created":"2026-10-01","cited_by_count":0,"area":null},{"id":"2610.03951","title":"Evolving LLM-Generated Features for Interpretable Classification","created":"2026-10-02","cited_by_count":0,"area":null}],"next_cursor":null}