mlsanityData to 14 Oct 2026

Multi-Target XGBoostLSS Regression

arXiv 2210.06831 ↗First posted 13 Oct 2022cs.LGcited by 4

Not on the map. Why?

Abstract (arXiv)

Current implementations of Gradient Boosting Machines are mostly designed for single-target regression tasks and commonly assume independence between responses when used in multivariate settings. As such, these models are not well suited if non-negligible dependencies exist between targets. To overcome this limitation, we present an extension of XGBoostLSS that models multiple targets and their dependencies in a probabilistic regression setting. Empirical results show that our approach outperforms existing GBMs with respect to runtime and compares well in terms of accuracy.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 15 of its 39 references that are in the map. First posted on arXiv 13 Oct 2022.

Builds on

15 of 39 references.

References · 15

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  2. 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,166
  3. 2019Optuna: A Next-generation Hyperparameter Optimization Frameworkcited by 2,501
  4. 2020Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networkscited by 566
  5. 2021Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series Forecastingcited by 337
  6. 2018CatBoost: gradient boosting with categorical features supportcited by 208
  7. 2019High-Dimensional Multivariate Forecasting with Low-Rank Gaussian Copula Processescited by 148
  8. 2020Multivariate Probabilistic Time Series Forecasting via Conditioned Normalizing Flowscited by 144
  9. 2019NGBoost: Natural Gradient Boosting for Probabilistic Predictioncited by 94
  10. 2022Multivariate Quantile Function Forecastercited by 19
  11. 2019GBDT-MO: Gradient Boosted Decision Trees for Multiple Outputscited by 16
  12. 2021Cholesky-based multivariate Gaussian regressioncited by 7
3 more
  1. 2021Multivariate Probabilistic Regression with Natural Gradient Boostingcited by 5
  2. 2020Multivariate Boosted Trees and Applications to Forecasting and Controlcited by 2
  3. 2015Finding structure in data using multivariate tree boostingcited by 1

Built on by

Cited by year

Citing papers per year: 2024: 2; 2025: 2.

4 papers.

  1. 2024Scaling Up Diffusion and Flow-based XGBoost Modelscited by 5
  2. 2025Online Multivariate Regularized Distributional Regression for High-dimensional Probabilistic Electricity Price Forecastingcited by 2
  3. 2024Forecasting with Hyper-Treescited by 1
  4. 2025Multivariate Uncertainty Quantification with Tomographic Quantile Forestscited by 1