mlsanityData to 7 Oct 2026

Distributional Gradient Boosting Machines

arXiv 2204.00778 ↗First posted 2 Apr 2022stat.ML cs.LGcited by 5

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Abstract (arXiv)

We present a unified probabilistic gradient boosting framework for regression tasks that models and predicts the entire conditional distribution of a univariate response variable as a function of covariates. Our likelihood-based approach allows us to either model all conditional moments of a parametric distribution, or to approximate the conditional cumulative distribution function via Normalizing Flows. As underlying computational backbones, our framework is based on XGBoost and LightGBM. Modelling and predicting the entire conditional distribution greatly enhances existing tree-based gradient boosting implementations, as it allows to create probabilistic forecasts from which prediction intervals and quantiles of interest can be derived. Empirical results show that our framework achieves state-of-the-art forecast accuracy.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 23 of its 61 references that are in the map. First posted on arXiv 2 Apr 2022.

Builds on

23 of 61 references.

References · 23

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  2. 2017A Unified Approach to Interpreting Model Predictionscited by 6,488
  3. 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,120
  4. 2015Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classificationcited by 3,590
  5. 2019Optuna: A Next-generation Hyperparameter Optimization Frameworkcited by 2,455
  6. 2019Normalizing Flows for Probabilistic Modeling and Inferencecited by 1,315
  7. 2017DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networkscited by 1,004
  8. 2019Aleatoric and Epistemic Uncertainty in Machine Learning: An Introduction to Concepts and Methodscited by 903
  9. 2016Generalized Random Forestscited by 309
  10. 2020Multivariate Probabilistic Time Series Forecasting via Conditioned Normalizing Flowscited by 142
  11. 2019NGBoost: Natural Gradient Boosting for Probabilistic Predictioncited by 94
  12. 2020Deep Conditional Transformation Modelscited by 23
11 more
  1. 2021Probabilistic Gradient Boosting Machines for Large-Scale Probabilistic Regressioncited by 20
  2. 2018Distributional Regression Forests for Probabilistic Precipitation Forecasting in Complex Terraincited by 19
  3. 2020Deep transformation models: Tackling complex regression problems with neural network based transformation modelscited by 18
  4. 2018Gradient and Newton Boosting for Classification and Regressioncited by 14
  5. 2021M5 Competition Uncertainty: Overdispersion, distributional forecasting, GAMLSS and beyondcited by 11
  6. 2019Multivariate Conditional Transformation Modelscited by 8
  7. 2021Probabilistic Time Series Forecasts with Autoregressive Transformation Modelscited by 7
  8. 2019Contrast Trees and Distribution Boostingcited by 5
  9. 2021Multivariate Probabilistic Regression with Natural Gradient Boostingcited by 5
  10. 2020Deep and interpretable regression models for ordinal outcomescited by 4
  11. 2022Bernstein Flows for Flexible Posteriors in Variational Bayescited by 3

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Cited by year

Citing papers per year: 2022: 1; 2023: 1; 2024: 2; 2025: 1.

5 papers.

  1. 2023Neural Additive Models for Location Scale and Shape: A Framework for Interpretable Neural Regression Beyond the Meancited by 18
  2. 2024Mambular: A Sequential Model for Tabular Deep Learningcited by 18
  3. 2022Sample-based Uncertainty Quantification with a Single Deterministic Neural Networkcited by 3
  4. 2024Forecasting with Hyper-Treescited by 1
  5. 2025Multivariate Uncertainty Quantification with Tomographic Quantile Forestscited by 1