mlsanityData to 14 Oct 2026

Neural Additive Models for Location Scale and Shape: A Framework for Interpretable Neural Regression Beyond the Mean

arXiv 2301.11862 ↗First posted 27 Jan 2023stat.ML cs.LGcited by 18

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Abstract (arXiv)

Deep neural networks (DNNs) have proven to be highly effective in a variety of tasks, making them the go-to method for problems requiring high-level predictive power. Despite this success, the inner workings of DNNs are often not transparent, making them difficult to interpret or understand. This lack of interpretability has led to increased research on inherently interpretable neural networks in recent years. Models such as Neural Additive Models (NAMs) achieve visual interpretability through the combination of classical statistical methods with DNNs. However, these approaches only concentrate on mean response predictions, leaving out other properties of the response distribution of the underlying data. We propose Neural Additive Models for Location Scale and Shape (NAMLSS), a modelling framework that combines the predictive power of classical deep learning models with the inherent advantages of distributional regression while maintaining the interpretability of additive models. The code is available at the following link: https://github.com/AnFreTh/NAMpy

Key facts. Cited by 18 papers in the map (data through 14 Oct 2026). Builds on 27 of its 56 references that are in the map. First posted on arXiv 27 Jan 2023.

Builds on

27 of 56 references.

References · 27

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15 more
  1. 2019NGBoost: Natural Gradient Boosting for Probabilistic Predictioncited by 94
  2. 2021NODE-GAM: Neural Generalized Additive Model for Interpretable Deep Learningcited by 88
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 2; 2024: 5; 2025: 7; 2026: 4.

18 papers.

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  1. 2024Mambular: A Sequential Model for Tabular Deep Learningcited by 21
  2. 2023Improving Neural Additive Models with Bayesian Principlescited by 15
  3. 2023Structural Neural Additive Models: Enhanced Interpretable Machine Learningcited by 6
  4. 2024Online Distributional Regressioncited by 3
  5. 2025Additive Models Explained: A Computational Complexity Approachcited by 3
  6. 2024Leveraging Graph Neural Networks to Forecast Electricity Consumptioncited by 2
  7. 2024NBMLSS: probabilistic forecasting of electricity prices via Neural Basis Models for Location Scale and Shapecited by 2
  8. 2025Beyond Black-Box Predictions: Identifying Marginal Feature Effects in Tabular Transformer Networkscited by 2
  9. 2024Neural Additive Image Model: Interpretation through Interpolationcited by 1
  10. 2025Graph Neural Networks for Electricity Load Forecastingcited by 1
  11. 2026Provably Explaining Neural Additive Modelscited by 1
  12. 2026textttfindr: Transparent and Fair Credit Risk Decisions through Semi-Structured Regressionscited by 1
6 more
  1. 2025LucidAtlas: Learning Uncertainty-Aware, Covariate-Disentangled, Individualized Atlas Representationscited by 0
  2. 2025Explainable Boosting Machine for Predicting Claim Severity and Frequency in Car Insurancecited by 0
  3. 2025From Distributional to Quantile Neural Basis Models: the case of Electricity Price Forecastingcited by 0
  4. 2025Bayesian Neural Networks for Functional ANOVA modelcited by 0
  5. 2026EviNAM: Intelligibility and Uncertainty via Evidential Neural Additive Modelscited by 0
  6. 2026Neural Additive Experts: Context-Gated Experts for Controllable Model Additivitycited by 0