A robust algorithm for explaining unreliable machine learning survival models using the Kolmogorov-Smirnov bounds

Neural Networks (NN), 2020

5 May 2020

Papers citing "A robust algorithm for explaining unreliable machine learning survival models using the Kolmogorov-Smirnov bounds"

10 / 10 papers shown

Interpretable Machine Learning for Survival Analysis

Sophie Hanna Langbein

410

15 Mar 2024

Area-norm COBRA on Conditional Survival Prediction

Rahul Goswami

Arabin Kr. Dey

385

01 Sep 2023

SurvBeX: An explanation method of the machine learning survival models based on the Beran estimatorInternational Journal of Data Science and Analysis (IJDSA), 2023

Lev V. Utkin

Danila Eremenko

A. Konstantinov

238

07 Aug 2023

SurvSHAP(t): Time-dependent explanations of machine learning survival modelsKnowledge-Based Systems (KBS), 2022

319

101

23 Aug 2022

Explainable AI (XAI) for PHM of Industrial Asset: A State-of-The-Art, PRISMA-Compliant Systematic Review

Ahmad Nazrie Mohd Nor

S. R. Pedapati

M. Muhammad

235

08 Jul 2021

An Imprecise SHAP as a Tool for Explaining the Class Probability Distributions under Limited Training Data

432

16 Jun 2021

SurvNAM: The machine learning survival model explanationNeural Networks (NN), 2021

259

18 Apr 2021

Wasserstein-based fairness interpretability framework for machine learning models

A. Miroshnikov

Konstandinos Kotsiopoulos

Ryan Franks

Arjun Ravi Kannan

FAtt

365

06 Nov 2020

Counterfactual explanation of machine learning survival models

M. Kovalev

Lev V. Utkin

CML OffRL

306

26 Jun 2020

SurvLIME: A method for explaining machine learning survival modelsKnowledge-Based Systems (KBS), 2020

M. Kovalev

Lev V. Utkin

E. Kasimov

575

110

18 Mar 2020