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Additive models

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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/interpretml/talktoebm

공식 코드

LLMs Understand Glass-Box Models, Discover Surprises, and Suggest Repairs

https://github.com/isvy08/aim

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An Additive Instance-Wise Approach to Multi-class Model Interpretation

https://github.com/jayjayc/scary

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The Structurally Complex with Additive Parent Causality (SCARY) Dataset

https://github.com/jbrich95/pinnev

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Regression modelling of spatiotemporal extreme U.S. wildfires via partially-interpretable neural networks

https://github.com/jdonnelly36/m-gam

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Interpretable Generalized Additive Models for Datasets with Missing Values

https://github.com/jiachangliu/fastSparse

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Fast Sparse Classification for Generalized Linear and Additive Models

https://github.com/kirthevasank/local-poly-reg

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Additive Approximations in High Dimensional Nonparametric Regression via the SALSA

https://github.com/kirthevasank/salsa

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Additive Approximations in High Dimensional Nonparametric Regression via the SALSA

https://github.com/krishnap25/RFA

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Robust Aggregation for Federated Learning

https://github.com/krzysztof-kacprzyk/shares

공식 코드

Shape Arithmetic Expressions: Advancing Scientific Discovery Beyond Closed-Form Equations

https://github.com/lemeln/nam

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Neural Additive Models: Interpretable Machine Learning with Neural Nets

https://github.com/maitraishaan/vi-and-iml-medsat

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How Your Location Relates to Health: Variable Importance and Interpretable Machine Learning for Environmental and Sociodemographic Data

https://github.com/mathiaskraus/igann

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IGANN Sparse: Bridging Sparsity and Interpretability with Non-linear Insight

https://github.com/maxhfarrell/flm2

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Deep Learning for Individual Heterogeneity

https://github.com/mayabechlerspeicher/Graph-Neural-Additive-Networks---GNAN

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The Interpretable and Effective Graph Neural Additive Networks

https://github.com/megamindhenry/judiling.jl

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How trial-to-trial learning shapes mappings in the mental lexicon: Modelling Lexical Decision with Linear Discriminative Learning

https://github.com/merantix-momentum/concurvity-regularization

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Curve Your Enthusiasm: Concurvity Regularization in Differentiable Generalized Additive Models

https://github.com/microsoft/augmented-interpretable-models

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Augmenting Interpretable Models with LLMs during Training

https://github.com/microsoft/interpret

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Axiomatic Interpretability for Multiclass Additive Models

https://github.com/nalzok/treeinner

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Individualized and Global Feature Attributions for Gradient Boosted Trees in the Presence of $\ell_2$ Regularization

https://github.com/neural-structured-additive-learning/funnel

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A Functional Extension of Semi-Structured Networks

https://github.com/nostringattached/FGAM

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A Factored Generalized Additive Model for Clinical Decision Support in the Operating Room

https://github.com/optimal-uoft/neurcam

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NeurCAM: Interpretable Neural Clustering via Additive Models

https://github.com/overtonc/nowcasting_the_2022_mpox_outbreak_in_england

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Nowcasting the 2022 mpox outbreak in England

https://github.com/pat-s/pathogen-modeling

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Performance evaluation and hyperparameter tuning of statistical and machine-learning models using spatial data

https://github.com/pedrodelicado/GhostVariables

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Understanding complex predictive models with Ghost Variables

https://github.com/poloclub/gam-coach

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GAM Coach: Towards Interactive and User-centered Algorithmic Recourse

https://github.com/qisuqi/focuslearn

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FocusLearn: Fully-Interpretable, High-Performance Modular Neural Networks for Time Series

https://github.com/rhshi/sparse-rf

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SHRIMP: Sparser Random Feature Models via Iterative Magnitude Pruning

https://github.com/shftan/distilled_additive_explanations

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Considerations When Learning Additive Explanations for Black-Box Models

https://github.com/shutong043/femam

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Multi-Level Additive Modeling for Structured Non-IID Federated Learning

https://github.com/slimgroup/SetIntersectionProjection.jl

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Generalized Minkowski sets for the regularization of inverse problems

https://github.com/solennegaucher/hierarchical-transfer-learning-covid19

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Hierarchical transfer learning with applications for electricity load forecasting

https://github.com/teddy-xionggz/protonam

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ProtoNAM: Prototypical Neural Additive Models for Interpretable Deep Tabular Learning

https://github.com/tleemann/slalom_explanations

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Attention Mechanisms Don't Learn Additive Models: Rethinking Feature Importance for Transformers

https://github.com/tml-tuebingen/nshap

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From Shapley Values to Generalized Additive Models and back

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.