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Additive models

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TitleStatusHype
How many images do I need? Understanding how sample size per class affects deep learning model performance metrics for balanced designs in autonomous wildlife monitoring—0
How to address monotonicity for model risk management?—0
Huber Additive Models for Non-stationary Time Series Analysis—0
Identification of Additive Continuous-time Systems in Open and Closed loop—0
Identification of additive multivariable continuous-time systems—0
Identification with possibly invalid IVs—0
Influenza Hospitalisations in England during the 2022/23 Season: do different data sources drive divergence in modelled waves? A comparison of surveillance and administrative data—0
Inherently Interpretable Tree Ensemble Learning—0
Injury risk increases minimally over a large range of changes in activity level in children—0
InstaSHAP: Interpretable Additive Models Explain Shapley Values Instantly—0
Interpretable Learning-to-Rank with Generalized Additive Models—0
Interpretable Low-Dimensional Regression via Data-Adaptive Smoothing—0
Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges—0
Interpretable Reinforcement Learning via Neural Additive Models for Inventory Management—0
Forecasting time series with constraintsCode0
Flexible, Non-parametric Modeling Using Regularized Neural NetworksCode0
Sparse hierarchical interaction learning with epigraphical projectionCode0
Exploring and Interacting with the Set of Good Sparse Generalized Additive ModelsCode0
Regression modelling of spatiotemporal extreme U.S. wildfires via partially-interpretable neural networksCode0
Gaussian Process Neural Additive ModelsCode0
Partial Order in Chaos: Consensus on Feature Attributions in the Rashomon SetCode0
Performance evaluation and hyperparameter tuning of statistical and machine-learning models using spatial dataCode0
Predicting Census Survey Response Rates With Parsimonious Additive Models and Structured InteractionsCode0
Generalized Minkowski sets for the regularization of inverse problemsCode0
Model compression as constrained optimization, with application to neural nets. Part V: combining compressionsCode0
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