methodology-2

Additive models

3260 papers • 126 benchmarks • 313 datasets

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Use these libraries to find additive-models-2 models and implementations

microsoft/interpret

3 papers 5,978

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Most implemented papers

Neural Additive Models: Interpretable Machine Learning with Neural Nets

Geoffrey E. Hinton, Rishabh Agarwal, Nicholas Frosst, Xuezhou Zhang, R. Caruana•Tue Apr 28 2020

Neural Additive Models (NAMs) are proposed which combine some of the expressivity of DNNs with the inherent intelligibility of generalized additive models and are more accurate than widely used intelligible models such as logistic regression and shallow decision trees.

507

Content

interpretml/interpret

2 papers 5,978

anfreth/nampy

2 papers 12

eric-vader/HD-BO-Additive-Models

2 papers 11

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Additive Approximations in High Dimensional Nonparametric Regression via the SALSA

Yaoliang Yu, Kirthevasan Kandasamy•Sat Jan 30 2016

This work proposes SALSA, which bridges this gap by allowing interactions between variables, but controls model capacity by limiting the order of interactions, and shows that the method is competitive against other alternatives.

45 0

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Augmenting interpretable models with large language models during training

R. Caruana, Jianfeng Gao, Chandan Singh, Armin Askari•Thu Sep 22 2022

Two instantiations of Aug-imodels in natural-language processing are explored: Aug-Linear, which augments a linear model with decoupled embeddings from an LLM and Aug-Tree, which augmented a decision tree with LLM feature expansions, which outperform their non-augmented, interpretable counterparts.

68 0

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Do Not Trust Additive Explanations

P. Biecek, Alicja Gosiewska•Tue Mar 26 2019

This paper examines the behavior of the most popular instance-level explanations under the presence of interactions, introduces a new method that detects interactions for instance- level explanations, and performs a large scale benchmark to see how frequently additive explanations may be misleading.

55 0

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InterpretML: A Unified Framework for Machine Learning Interpretability

R. Caruana, Harsha Nori, Samuel Jenkins, Paul Koch•Wed Sep 18 2019

InterpretML is an open-source Python package which exposes machine learning interpretability algorithms to practitioners and researchers by exposing multiple methods under a unified API, and by having a built-in, extensible visualization platform.

581 0

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Robust Aggregation for Federated Learning

Zaïd Harchaoui, S. Kakade, Krishna Pillutla•Mon Dec 30 2019

The experiments show that RFA is competitive with the classical aggregation when the level of corruption is low, while demonstrating greater robustness under high corruption, and establishes the convergence of the robust federated learning algorithm for the stochastic learning of additive models with least squares.

874 0

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How Interpretable and Trustworthy are GAMs?

R. Caruana, A. Goldenberg, Benjamin J. Lengerich, C. Chang, S. Tan•Wed Jun 10 2020

It is found that GAMs with high feature sparsity can miss patterns in the data and be unfair to rare subpopulations, and tree-based GAMs represent the best balance of sparsity, fidelity and accuracy and thus appear to be the most trustworthy GAM models.

88 0

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GAMI-Net: An Explainable Neural Network based on Generalized Additive Models with Structured Interactions

Zebin Yang, Aijun Zhang, A. Sudjianto•Sun Mar 15 2020

Numerical experiments show that the proposed explainable GAMI-Net enjoys superior interpretability while maintaining competitive prediction accuracy in comparison to the explainable boosting machine and other benchmark machine learning models.

155 0

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NODE-GAM: Neural Generalized Additive Model for Interpretable Deep Learning

R. Caruana, A. Goldenberg, C. Chang•Wed Jun 02 2021

This work proposes a neural GAM (NODE-GAM) and neural GA$^2$M ( NODE-GA$^1$M) that scale well and perform better than other GAMs on large datasets, while remaining interpretable compared to other ensemble and deep learning models.

103 0

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