mlsanityData to 14 Oct 2026

LightAutoML: AutoML Solution for a Large Financial Services Ecosystem

arXiv 2109.01528 ↗First posted 3 Sep 2021cs.LG stat.MLcited by 32

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Abstract (arXiv)

We present an AutoML system called LightAutoML developed for a large European financial services company and its ecosystem satisfying the set of idiosyncratic requirements that this ecosystem has for AutoML solutions. Our framework was piloted and deployed in numerous applications and performed at the level of the experienced data scientists while building high-quality ML models significantly faster than these data scientists. We also compare the performance of our system with various general-purpose open source AutoML solutions and show that it performs better for most of the ecosystem and OpenML problems. We also present the lessons that we learned while developing the AutoML system and moving it into production.

Key facts. Cited by 32 papers in the map (data through 14 Oct 2026). Builds on 15 of its 43 references that are in the map. First posted on arXiv 3 Sep 2021.

Builds on

15 of 43 references.

References · 15

  1. 2017A Unified Approach to Interpreting Model Predictionscited by 6,555
  2. 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,166
  3. 2019Optuna: A Next-generation Hyperparameter Optimization Frameworkcited by 2,501
  4. 2020AutoGluon-Tabular: Robust and Accurate AutoML for Structured Datacited by 383
  5. 2018CatBoost: gradient boosting with categorical features supportcited by 208
  6. 2018Auto-Keras: An Efficient Neural Architecture Search Systemcited by 171
  7. 2019An Open Source AutoML Benchmarkcited by 85
  8. 2019Sherlock: A Deep Learning Approach to Semantic Data Type Detectioncited by 74
  9. 2020Auto-PyTorch Tabular: Multi-Fidelity MetaLearning for Efficient and Robust AutoDLcited by 67
  10. 2019Evaluating time series forecasting models: An empirical study on performance estimation methodscited by 53
  11. 2018OBOE: Collaborative Filtering for AutoML Model Selectioncited by 31
  12. 2020Amazon SageMaker Autopilot: a white box AutoML solution at scalecited by 20
3 more
  1. 2019Visus: An Interactive System for Automatic Machine Learning Model Building and Curationcited by 9
  2. 2018Automatic Gradient Boostingcited by 6
  3. 2017Multiple Adaptive Bayesian Linear Regression for Scalable Bayesian Optimization with Warm Startcited by 4

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 4; 2023: 6; 2024: 8; 2025: 7; 2026: 7.

32 papers.

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  1. 2022AMLB: an AutoML Benchmarkcited by 55
  2. 2023TabRepo: A Large Scale Repository of Tabular Model Evaluations and its AutoML Applicationscited by 33
  3. 2024AutoGluon-Multimodal (AutoMM): Supercharging Multimodal AutoML with Foundation Modelscited by 30
  4. 2024Automated data processing and feature engineering for deep learning and big data applications: a surveycited by 14
  5. 2024Multimodal Banking Dataset: Understanding Client Needs through Event Sequencescited by 9
  6. 2023Q(D)O-ES: Population-based Quality (Diversity) Optimisation for Post Hoc Ensemble Selection in AutoMLcited by 8
  7. 2025KompeteAI: Accelerated Autonomous Multi-Agent System for End-to-End Pipeline Generation for Machine Learning Problemscited by 8
  8. 2023EmotiEffNet Facial Features in Uni-task Emotion Recognition in Video at ABAW-5 competitioncited by 7
  9. 2024Bag of Tricks for Multimodal AutoML with Image, Text, and Tabular Datacited by 7
  10. 2024Don't Waste Your Time: Early Stopping Cross-Validationcited by 6
  11. 2022AutoMLBench: A Comprehensive Experimental Evaluation of Automated Machine Learning Frameworkscited by 3
  12. 2022Consolidated learning -- a domain-specific model-free optimization strategy with examples for XGBoost and MIMIC-IVcited by 2
20 more
  1. 2026Tree-Structured Synergy of Large Language Models and Bayesian Optimization for Efficient CASHcited by 2
  2. 2026Financial Transaction Retrieval and Contextual Evidence for Knowledge-Grounded Reasoningcited by 2
  3. 2022The Long Tail of Context: Does it Exist and Matter?cited by 1
  4. 2023Improvement of Computational Performance of Evolutionary AutoML in a Heterogeneous Environmentcited by 1
  5. 2023Integration Of Evolutionary Automated Machine Learning With Structural Sensitivity Analysis For Composite Pipelinescited by 1
  6. 2024UniAutoML: A Human-Centered Framework for Unified Discriminative and Generative AutoML with Large Language Modelscited by 1
  7. 2025Imbalanced Regression Pipeline Recommendationcited by 1
  8. 2025LightAutoDS-Tab: Multi-AutoML Agentic System for Tabular Datacited by 1
  9. 2025How Usable is Automated Feature Engineering for Tabular Data?cited by 1
  10. 2026Embedding-Aware Feature Discovery: Bridging Latent Representations and Interpretable Features in Event Sequencescited by 1
  11. 2023STREAMLINE: An Automated Machine Learning Pipeline for Biomedicine Applied to Examine the Utility of Photography-Based Phenotypes for OSA Prediction Across International Sleep Centerscited by 0
  12. 2024Linguacodus: A Synergistic Framework for Transformative Code Generation in Machine Learning Pipelinescited by 0
  13. 2024Machine Learning for Two-Sample Testing under Right-Censored Data: A Simulation Studycited by 0
  14. 2025Interpretable by Design: MH-AutoML for Transparent and Efficient Android Malware Detection without Compromising Performancecited by 0
  15. 2025ML2B: Benchmarking LLMs on Cross-Lingual ML Pipeline Generationcited by 0
  16. 2025Automated Evolutionary Optimization for Resource-Efficient Neural Network Trainingcited by 0
  17. 2026Automated Machine Learning in Radiomics: A Comparative Evaluation of Performance, Efficiency and Accessibilitycited by 0
  18. 2026CoFEH: LLM-driven Feature Engineering Empowered by Collaborative Bayesian Hyperparameter Optimizationcited by 0
  19. 2026OpenAutoNLU: Open Source AutoML Library for NLUcited by 0
  20. 2026Pre-AF 13: An Interpretable Atrial Fibrillation Risk Score Mined from Discharge Reportscited by 0