mlsanityData to 7 Oct 2026

FLAML: A Fast and Lightweight AutoML Library

arXiv 1911.04706 ↗First posted 12 Nov 2019cs.LG stat.MLcited by 104

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Abstract (arXiv)

We study the problem of using low computational cost to automate the choices of learners and hyperparameters for an ad-hoc training dataset and error metric, by conducting trials of different configurations on the given training data. We investigate the joint impact of multiple factors on both trial cost and model error, and propose several design guidelines. Following them, we build a fast and lightweight library FLAML which optimizes for low computational resource in finding accurate models. FLAML integrates several simple but effective search strategies into an adaptive system. It significantly outperforms top-ranked AutoML libraries on a large open source AutoML benchmark under equal, or sometimes orders of magnitude smaller budget constraints.

Key facts. Cited by 104 papers in the map (data through 7 Oct 2026). First posted on arXiv 12 Nov 2019.

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Citing papers per year: 2021: 4; 2022: 17; 2023: 18; 2024: 23; 2025: 19; 2026: 23.

104 papers.

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  2. 2023MLCopilot: Unleashing the Power of Large Language Models in Solving Machine Learning Taskscited by 66
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  4. 2022AMLB: an AutoML Benchmarkcited by 55
  5. 2025Do-PFN: In-Context Learning for Causal Effect Estimationcited by 46
  6. 2025CausalPFN: Amortized Causal Effect Estimation via In-Context Learningcited by 43
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  9. 2024Unleashing the Power of Data Tsunami: A Comprehensive Survey on Data Assessment and Selection for Instruction Tuning of Language Modelscited by 29
  10. 2022Empirical Analysis of Model Selection for Heterogeneous Causal Effect Estimationcited by 25
  11. 2024SELA: Tree-Search Enhanced LLM Agents for Automated Machine Learningcited by 23
  12. 2023Can Fairness be Automated? Guidelines and Opportunities for Fairness-aware AutoMLcited by 16
38 more
  1. 2024Automated data processing and feature engineering for deep learning and big data applications: a surveycited by 14
  2. 2024Position: A Call to Action for a Human-Centered AutoML Paradigmcited by 14
  3. 2024AutoRAG-HP: Automatic Online Hyper-Parameter Tuning for Retrieval-Augmented Generationcited by 13
  4. 2024AutoM3L: An Automated Multimodal Machine Learning Framework with Large Language Modelscited by 13
  5. 2025TabPFN: One Model to Rule Them All?cited by 10
  6. 2022XAutoML: A Visual Analytics Tool for Understanding and Validating Automated Machine Learningcited by 8
  7. 2022SubStrat: A Subset-Based Strategy for Faster AutoMLcited by 8
  8. 2023AutoML in The Wild: Obstacles, Workarounds, and Expectationscited by 8
  9. 2023Q(D)O-ES: Population-based Quality (Diversity) Optimisation for Post Hoc Ensemble Selection in AutoMLcited by 8
  10. 2021A Scalable AutoML Approach Based on Graph Neural Networkscited by 7
  11. 2021FairAutoML: Embracing Unfairness Mitigation in AutoMLcited by 6
  12. 2023DivBO: Diversity-aware CASH for Ensemble Learningcited by 6
  13. 2024Bag of Tricks for Multimodal AutoML with Image, Text, and Tabular Datacited by 6
  14. 2026Closed-loop Auto Research for Molecular Property Prediction: Discovering and Certifying Generalizable Improvementscited by 6
  15. 2022Meta-Learning for Unsupervised Outlier Detection with Optimal Transportcited by 5
  16. 2024X Hacking: The Threat of Misguided AutoMLcited by 5
  17. 2024Structure-agnostic Optimality of Doubly Robust Learning for Treatment Effect Estimationcited by 5
  18. 2025Min-Max Optimisation for Nonconvex-Nonconcave Functions Using a Random Zeroth-Order Extragradient Algorithmcited by 5
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  20. 2025We Need to Rethink Benchmarking in Anomaly Detectioncited by 4
  21. 2021ChaCha for Online AutoMLcited by 3
  22. 2022AutoDES: AutoML Pipeline Generation of Classification with Dynamic Ensemble Strategy Selectioncited by 3
  23. 2022AutoMLBench: A Comprehensive Experimental Evaluation of Automated Machine Learning Frameworkscited by 3
  24. 2024The Potential of AutoML for Recommender Systemscited by 3
  25. 2022Missing Data Infill with Automungecited by 2
  26. 2022Mining Robust Default Configurations for Resource-constrained AutoMLcited by 2
  27. 2022Automatically Categorising GitHub Repositories by Application Domaincited by 2
  28. 2023Scalable End-to-End ML Platforms: from AutoML to Self-servecited by 2
  29. 2023KGLiDS: A Platform for Semantic Abstraction, Linking, and Automation of Data Sciencecited by 2
  30. 2023EvolveMT: an Ensemble MT Engine Improving Itself with Usage Onlycited by 2
  31. 2023Roulette: A Semantic Privacy-Preserving Device-Edge Collaborative Inference Framework for Deep Learning Classification Taskscited by 2
  32. 2024Unveiling the Potential of Robustness in Selecting Conditional Average Treatment Effect Estimatorscited by 2
  33. 2021Federated Data Science to Break Down Silos [Vision]cited by 1
  34. 2022Practitioner Motives to Use Different Hyperparameter Optimization Methodscited by 1
  35. 2022SIBILA: A novel interpretable ensemble of general-purpose machine learning models applied to medical contextscited by 1
  36. 2022ACE: Adaptive Constraint-aware Early Stopping in Hyperparameter Optimizationcited by 1
  37. 2022Arbitrarily Large Labelled Random Satisfiability Formulas for Machine Learning Trainingcited by 1
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