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2007.14528
Cited By

Surrogate Locally-Interpretable Models with Supervised Machine Learning
Algorithms

Surrogate Locally-Interpretable Models with Supervised Machine Learning Algorithms

28 July 2020

Jie Chen

ArXiv (abs)PDF HTML

Papers citing "Surrogate Locally-Interpretable Models with Supervised Machine Learning Algorithms"

10 / 10 papers shown

Surrogate Modeling and Explainable Artificial Intelligence for Complex Systems: A Workflow for Automated Simulation Exploration

Surrogate Modeling and Explainable Artificial Intelligence for Complex Systems: A Workflow for Automated Simulation Exploration

Nicolas Verstaevel

Moncef Garouani

168

5

0

19 Oct 2025

Effector: A Python package for regional explanations

Effector: A Python package for regional explanationsBehavioral and Brain Sciences (BBS), 2025

Vasilis Gkolemis

Dimitris Kyriakopoulos

Konstantinos Tsopelas

...

Maximilian Muschalik

Theodore Dalamagas

Giuseppe Casalicchio

307

4

0

03 Apr 2024

Leveraging Model-based Trees as Interpretable Surrogate Models for Model
Distillation

Leveraging Model-based Trees as Interpretable Surrogate Models for Model Distillation

Giuseppe Casalicchio

213

13

0

04 Oct 2023

Monotone Tree-Based GAMI Models by Adapting XGBoost

Monotone Tree-Based GAMI Models by Adapting XGBoost

Soroush Aramideh

196

2

0

05 Sep 2023

Interpretable Machine Learning based on Functional ANOVA Framework:
Algorithms and Comparisons

Interpretable Machine Learning based on Functional ANOVA Framework: Algorithms and Comparisons

Jie Chen

241

10

0

25 May 2023

Using Model-Based Trees with Boosting to Fit Low-Order Functional ANOVA
Models

Using Model-Based Trees with Boosting to Fit Low-Order Functional ANOVA Models

390

3

0

14 Jul 2022

Shapley Computations Using Surrogate Model-Based Trees

Shapley Computations Using Surrogate Model-Based Trees

Jie Chen

138

0

0

11 Jul 2022

REPID: Regional Effect Plots with implicit Interaction Detection

REPID: Regional Effect Plots with implicit Interaction DetectionInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2022

Giuseppe Casalicchio

265

23

0

15 Feb 2022

The Self-Simplifying Machine: Exploiting the Structure of Piecewise
Linear Neural Networks to Create Interpretable Models

The Self-Simplifying Machine: Exploiting the Structure of Piecewise Linear Neural Networks to Create Interpretable Models

156

1

0

02 Dec 2020

Supervised Machine Learning Techniques: An Overview with Applications to
Banking

Supervised Machine Learning Techniques: An Overview with Applications to BankingInternational Statistical Review (ISR), 2020

Jie Chen

132

29

0

28 Jul 2020

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