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1906.02032
Cited By

c-Eval: A Unified Metric to Evaluate Feature-based Explanations via
Perturbation

v1v2 (latest)

c-Eval: A Unified Metric to Evaluate Feature-based Explanations via Perturbation

5 June 2019

Truc D. T. Nguyen

ArXiv (abs)PDF HTML

Papers citing "c-Eval: A Unified Metric to Evaluate Feature-based Explanations via Perturbation"

15 / 15 papers shown

Shaping History: Advanced Machine Learning Techniques for the Analysis
and Dating of Cuneiform Tablets over Three Millennia

Shaping History: Advanced Machine Learning Techniques for the Analysis and Dating of Cuneiform Tablets over Three Millennia

313

2

0

06 Jun 2024

Dual feature-based and example-based explanation methods

Dual feature-based and example-based explanation methods

A. Konstantinov

Boris V. Kozlov

Stanislav R. Kirpichenko

374

0

0

29 Jan 2024

Visual Explanations with Attributions and Counterfactuals on Time Series
Classification

Visual Explanations with Attributions and Counterfactuals on Time Series Classification

Mennatallah El-Assady

287

5

0

14 Jul 2023

Cross-Model Consensus of Explanations and Beyond for Image
Classification Models: An Empirical Study

Cross-Model Consensus of Explanations and Beyond for Image Classification Models: An Empirical Study

Haoyi Xiong

Siyu Huang

133

11

0

02 Sep 2021

Attention-like feature explanation for tabular data

Attention-like feature explanation for tabular dataInternational Journal of Data Science and Analysis (JDSA), 2021

A. Konstantinov

304

5

0

10 Aug 2021

SurvNAM: The machine learning survival model explanation

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

Egor D. Satyukov

A. Konstantinov

201

36

0

18 Apr 2021

Interpretable Deep Learning: Interpretation, Interpretability,
Trustworthiness, and Beyond

Interpretable Deep Learning: Interpretation, Interpretability, Trustworthiness, and BeyondKnowledge and Information Systems (KAIS), 2021

Haoyi Xiong

Jiang Bian

AAML FaML XAI HAI

291

439

0

19 Mar 2021

Interpretable Machine Learning with an Ensemble of Gradient Boosting
Machines

Interpretable Machine Learning with an Ensemble of Gradient Boosting Machines

A. Konstantinov

176

189

0

14 Oct 2020

PGM-Explainer: Probabilistic Graphical Model Explanations for Graph
Neural Networks

PGM-Explainer: Probabilistic Graphical Model Explanations for Graph Neural Networks

229

393

0

12 Oct 2020

Counterfactual explanation of machine learning survival models

Counterfactual explanation of machine learning survival models

261

24

0

26 Jun 2020

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

A robust algorithm for explaining unreliable machine learning survival models using the Kolmogorov-Smirnov boundsNeural Networks (NN), 2020

216

32

0

05 May 2020

SurvLIME-Inf: A simplified modification of SurvLIME for explanation of
machine learning survival models

SurvLIME-Inf: A simplified modification of SurvLIME for explanation of machine learning survival models

213

11

0

05 May 2020

SurvLIME: A method for explaining machine learning survival models

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

443

101

0

18 Mar 2020

Towards a Unified Evaluation of Explanation Methods without Ground Truth

Towards a Unified Evaluation of Explanation Methods without Ground Truth

202

9

0

20 Nov 2019

An explanation method for Siamese neural networks

An explanation method for Siamese neural networks

208

16

0

18 Nov 2019