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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://bitbucket.org/dmmlgeneva/nvsd_uai2018

공식 코드

Structured nonlinear variable selection

https://github.com/HanXudong/GLoM

공식 코드

Grounding learning of modifier dynamics: An application to color naming

https://github.com/ModelOriented/iBreakDown

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Do Not Trust Additive Explanations

https://github.com/MonikaZimmermann/load-forecasting

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Efficient mid-term forecasting of hourly electricity load using generalized additive models

https://github.com/NathanDoumeche/WeaKL

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Forecasting time series with constraints

https://github.com/NicoHambauer/Model-Performance-vs-Interpretability

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Challenging the Performance-Interpretability Trade-off: An Evaluation of Interpretable Machine Learning Models

https://github.com/SheffieldML/ChainedGP

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Chained Gaussian Processes

https://github.com/ShibalIbrahim/Additive-Models-with-Structured-Interactions

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Predicting Census Survey Response Rates With Parsimonious Additive Models and Structured Interactions

https://github.com/UCMerced-ML/LC-model-compression

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Model compression as constrained optimization, with application to neural nets. Part V: combining compressions

https://github.com/ZebinYang/gaminet

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

https://github.com/aagarwal1996/additive_trees

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A cautionary tale on fitting decision trees to data from additive models: generalization lower bounds

https://github.com/albietz/ckn_kernel

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Approximation and Learning with Deep Convolutional Models: a Kernel Perspective

https://github.com/allerbo/prada_net

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Flexible, Non-parametric Modeling Using Regularized Neural Networks

https://github.com/andruekonst/egbm-gam

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Interpretable Machine Learning with an Ensemble of Gradient Boosting Machines

https://github.com/anfreth/nampy

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

https://github.com/arlundborg/pcm_code

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The Projected Covariance Measure for assumption-lean variable significance testing

https://github.com/boost-R/gamboostLSS

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Stability selection for component-wise gradient boosting in multiple dimensions

https://github.com/boyiguo1/Manuscript-BHAM

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Spike-and-Slab LASSO Generalized Additive Models and Scalable Algorithms for High-Dimensional Data Analysis

https://github.com/chengzhang-uci/RNSHMC

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Hamiltonian Monte Carlo Acceleration Using Surrogate Functions with Random Bases

https://github.com/chudizhong/gamsrashomonset

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Exploring and Interacting with the Set of Good Sparse Generalized Additive Models

https://github.com/craymichael/PostHocExplainerEvaluation

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How Well Do Feature-Additive Explainers Explain Feature-Additive Predictors?

https://github.com/csinva/emb-gam

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Augmenting Interpretable Models with LLMs during Training

https://github.com/csinva/imodels

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Fast Interpretable Greedy-Tree Sums

https://github.com/csinva/imodelsX

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Augmenting Interpretable Models with LLMs during Training

https://github.com/davidruegamer/semi-structured_distributional_regression

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Semi-Structured Distributional Regression -- Extending Structured Additive Models by Arbitrary Deep Neural Networks and Data Modalities

https://github.com/efm95/dream

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Modeling non-linear Effects with Neural Networks in Relational Event Models

https://github.com/eric-vader/HD-BO-Additive-Models

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High-Dimensional Bayesian Optimization via Tree-Structured Additive Models

https://github.com/facebookresearch/nbm-spam

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Scalable Interpretability via Polynomials

https://github.com/fau-is/gam_comparison

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GAM(e) changer or not? An evaluation of interpretable machine learning models based on additive model constraints

https://github.com/fortuinlab/la-nam

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Improving Neural Additive Models with Bayesian Principles

https://github.com/gablabc/partial_order_in_chaos

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Partial Order in Chaos: Consensus on Feature Attributions in the Rashomon Set

https://github.com/gim4855744/gatsm

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Transparent Networks for Multivariate Time Series

https://github.com/gim4855744/honam

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Higher-order Neural Additive Models: An Interpretable Machine Learning Model with Feature Interactions

https://github.com/hpdeventer/kasam

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KASAM: Spline Additive Models for Function Approximation

https://github.com/interpretml/gam-changer

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GAM Changer: Editing Generalized Additive Models with Interactive Visualization

https://github.com/interpretml/interpret

공식 코드

GAM(e) changer or not? An evaluation of interpretable machine learning models based on additive model constraints

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