Additive models
72 papers with code • 0 benchmarks • 0 datasets
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Use these libraries to find Additive models models and implementationsMost implemented papers
Neural Additive Models: Interpretable Machine Learning with Neural Nets
They perform similarly to existing state-of-the-art generalized additive models in accuracy, but are more flexible because they are based on neural nets instead of boosted trees.
Augmenting Interpretable Models with LLMs during Training
csinva/imodelsX
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Recent large language models (LLMs) have demonstrated remarkable prediction performance for a growing array of tasks.
Additive Approximations in High Dimensional Nonparametric Regression via the SALSA
Between non-additive models which often have large variance and first order additive models which have large bias, there has been little work to exploit the trade-off in the middle via additive models of intermediate order.
Do Not Trust Additive Explanations
Explainable Artificial Intelligence (XAI)has received a great deal of attention recently.
InterpretML: A Unified Framework for Machine Learning Interpretability
InterpretML is an open-source Python package which exposes machine learning interpretability algorithms to practitioners and researchers.
Robust Aggregation for Federated Learning
krishnap25/RFA
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arXiv preprint 2019
We present a robust aggregation approach to make federated learning robust to settings when a fraction of the devices may be sending corrupted updates to the server.
Semi-Structured Distributional Regression -- Extending Structured Additive Models by Arbitrary Deep Neural Networks and Data Modalities
We propose a general framework to combine structured regression models and deep neural networks into a unifying network architecture.
GAMI-Net: An Explainable Neural Network based on Generalized Additive Models with Structured Interactions
The lack of interpretability is an inevitable problem when using neural network models in real applications.
How Interpretable and Trustworthy are GAMs?
Generalized additive models (GAMs) have become a leading modelclass for interpretable machine learning.
NODE-GAM: Neural Generalized Additive Model for Interpretable Deep Learning
zzzace2000/nodegam
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ICLR 2022
Deployment of machine learning models in real high-risk settings (e. g. healthcare) often depends not only on the model's accuracy but also on its fairness, robustness, and interpretability.
