mlsanityData to 14 Oct 2026

CatBoost: gradient boosting with categorical features support

arXiv 1810.11363 ↗First posted 24 Oct 2018cs.LG cs.MS stat.MLcited by 208

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Abstract (arXiv)

In this paper we present CatBoost, a new open-sourced gradient boosting library that successfully handles categorical features and outperforms existing publicly available implementations of gradient boosting in terms of quality on a set of popular publicly available datasets. The library has a GPU implementation of learning algorithm and a CPU implementation of scoring algorithm, which are significantly faster than other gradient boosting libraries on ensembles of similar sizes.

Key facts. Cited by 208 papers in the map (data through 14 Oct 2026). First posted on arXiv 24 Oct 2018.

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Citing papers per year: 2021: 33; 2022: 48; 2023: 37; 2024: 34; 2025: 37; 2026: 19.

208 papers.

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  1. 2021A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantificationcited by 792
  2. 2021Tabular Data: Deep Learning is Not All You Needcited by 535
  3. 2021SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Trainingcited by 319
  4. 2025TabICL: A Tabular Foundation Model for In-Context Learning on Large Datacited by 289
  5. 2022FOSTER: Feature Boosting and Compression for Class-Incremental Learningcited by 229
  6. 2022TransTab: Learning Transferable Tabular Transformers Across Tablescited by 132
  7. 2023XTab: Cross-table Pretraining for Tabular Transformerscited by 81
  8. 2024CARTE: Pretraining and Transfer for Tabular Learningcited by 76
  9. 2023Benchmarking Distribution Shift in Tabular Data with TableShiftcited by 66
  10. 2023Effectively Modeling Time Series with Simple Discrete State Spacescited by 54
  11. 2022DDG-DA: Data Distribution Generation for Predictable Concept Drift Adaptationcited by 36
  12. 2023TabMT: Generating tabular data with masked transformerscited by 36
38 more
  1. 2021LightAutoML: AutoML Solution for a Large Financial Services Ecosystemcited by 32
  2. 2023A review of ensemble learning and data augmentation models for class imbalanced problems: combination, implementation and evaluationcited by 25
  3. 2021NTIRE 2021 Challenge on Perceptual Image Quality Assessmentcited by 18
  4. 2022Interpretable Mixture of Expertscited by 17
  5. 2022Subgroup Robustness Grows On Trees: An Empirical Baseline Investigationcited by 17
  6. 2024OAG-Bench: A Human-Curated Benchmark for Academic Graph Miningcited by 14
  7. 2025MVP: Winning Solution to SMP Challenge 2025 Video Trackcited by 14
  8. 2022OpBoost: A Vertical Federated Tree Boosting Framework Based on Order-Preserving Desensitizationcited by 12
  9. 2023Black-Box Batch Active Learning for Regressioncited by 12
  10. 2022Does Label Differential Privacy Prevent Label Inference Attacks?cited by 11
  11. 2022DNNR: Differential Nearest Neighbors Regressioncited by 11
  12. 2021Coresets for Decision Trees of Signalscited by 10
  13. 2022A Comprehensive Benchmark for COVID-19 Predictive Modeling Using Electronic Health Records in Intensive Carecited by 10
  14. 2026MultiModalPFN: Extending Prior-Data Fitted Networks for Multimodal Tabular Learningcited by 10
  15. 2021LEAF: Navigating Concept Drift in Cellular Networkscited by 9
  16. 2022R-GCN: The R Could Stand for Randomcited by 9
  17. 2022All You Need Is Logs: Improving Code Completion by Learning from Anonymous IDE Usage Logscited by 9
  18. 2022Federated Boosted Decision Trees with Differential Privacycited by 9
  19. 2022Extractive Summarization of Legal Decisions using Multi-task Learning and Maximal Marginal Relevancecited by 9
  20. 2025Epistemic Uncertainty in Conformal Scores: A Unified Approachcited by 9
  21. 2026AutoScientists: Self-Organizing Agent Teams for Long-Running Scientific Experimentationcited by 9
  22. 2022An Explainable Decision Support System for Predictive Process Analyticscited by 8
  23. 2024SeqNAS: Neural Architecture Search for Event Sequence Classificationcited by 8
  24. 2024SHAP values via sparse Fourier representationcited by 8
  25. 2026LimiX-2: A Contextual Mechanism Network Towards General Structured-Data Intelligencecited by 8
  26. 2022Fairness Implications of Encoding Protected Categorical Attributescited by 7
  27. 2022Do Not Sleep on Traditional Machine Learning: Simple and Interpretable Techniques Are Competitive to Deep Learning for Sleep Scoringcited by 7
  28. 2023RCT Rejection Sampling for Causal Estimation Evaluationcited by 7
  29. 2024Advancing Deep Active Learning & Data Subset Selection: Unifying Principles with Information-Theory Intuitionscited by 7
  30. 2024SANGRIA: Stacked Autoencoder Neural Networks with Gradient Boosting for Indoor Localizationcited by 7
  31. 2021Boosted Embeddings for Time Series Forecastingcited by 6
  32. 2021A Machine Learning Analysis of COVID-19 Mental Health Datacited by 6
  33. 2022Similarity learning for wells based on logging datacited by 6
  34. 2022Object-centric Process Predictive Analyticscited by 6
  35. 2022QuerySnout: Automating the Discovery of Attribute Inference Attacks against Query-Based Systemscited by 6
  36. 2023Learning When to Treat Business Processes: Prescriptive Process Monitoring with Causal Inference and Reinforcement Learningcited by 6
  37. 2024AdapTable: Test-Time Adaptation for Tabular Data via Shift-Aware Uncertainty Calibrator and Label Distribution Handlercited by 6
  38. 2024Enriching Tabular Data with Contextual LLM Embeddings: A Comprehensive Ablation Study for Ensemble Classifierscited by 6

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