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Problems with Shapley-value-based explanations as feature importance
  measures

Problems with Shapley-value-based explanations as feature importance measures

25 February 2020
Indra Elizabeth Kumar
Suresh Venkatasubramanian
C. Scheidegger
Sorelle A. Friedler
    TDI
    FAtt
ArXivPDFHTML

Papers citing "Problems with Shapley-value-based explanations as feature importance measures"

40 / 90 papers shown
Title
Supervised Learning and Model Analysis with Compositional Data
Supervised Learning and Model Analysis with Compositional Data
Shimeng Huang
Elisabeth Ailer
Niki Kilbertus
Niklas Pfister
44
5
0
15 May 2022
Towards a Responsible AI Development Lifecycle: Lessons From Information
  Security
Towards a Responsible AI Development Lifecycle: Lessons From Information Security
Erick Galinkin
SILM
26
6
0
06 Mar 2022
The Shapley Value in Machine Learning
The Shapley Value in Machine Learning
Benedek Rozemberczki
Lauren Watson
Péter Bayer
Hao-Tsung Yang
Oliver Kiss
Sebastian Nilsson
Rik Sarkar
TDI
FAtt
40
208
0
11 Feb 2022
Explainability in Music Recommender Systems
Explainability in Music Recommender Systems
Darius Afchar
Alessandro B. Melchiorre
Markus Schedl
Romain Hennequin
Elena V. Epure
Manuel Moussallam
39
48
0
25 Jan 2022
Prolog-based agnostic explanation module for structured pattern
  classification
Prolog-based agnostic explanation module for structured pattern classification
Gonzalo Nápoles
Fabian Hoitsma
A. Knoben
A. Jastrzębska
Maikel Leon Espinosa
25
13
0
23 Dec 2021
AcME -- Accelerated Model-agnostic Explanations: Fast Whitening of the
  Machine-Learning Black Box
AcME -- Accelerated Model-agnostic Explanations: Fast Whitening of the Machine-Learning Black Box
David Dandolo
Chiara Masiero
Mattia Carletti
Davide Dalle Pezze
Gian Antonio Susto
FAtt
LRM
29
23
0
23 Dec 2021
Exact Shapley Values for Local and Model-True Explanations of Decision
  Tree Ensembles
Exact Shapley Values for Local and Model-True Explanations of Decision Tree Ensembles
Thomas W. Campbell
H. Roder
R. Georgantas
J. Roder
FedML
TDI
FAtt
29
16
0
16 Dec 2021
Designing Inherently Interpretable Machine Learning Models
Designing Inherently Interpretable Machine Learning Models
Agus Sudjianto
Aijun Zhang
FaML
21
31
0
02 Nov 2021
Counterfactual Shapley Additive Explanations
Counterfactual Shapley Additive Explanations
Emanuele Albini
Jason Long
Danial Dervovic
Daniele Magazzeni
39
49
0
27 Oct 2021
Self-explaining Neural Network with Concept-based Explanations for ICU
  Mortality Prediction
Self-explaining Neural Network with Concept-based Explanations for ICU Mortality Prediction
Sayantan Kumar
Sean C. Yu
Thomas Kannampallil
Zachary B. Abrams
Andrew Michelson
Philip R. O. Payne
FAtt
19
7
0
09 Oct 2021
Opportunities for Machine Learning to Accelerate Halide Perovskite
  Commercialization and Scale-Up
Opportunities for Machine Learning to Accelerate Halide Perovskite Commercialization and Scale-Up
Rishi E. Kumar
A. Tiihonen
Shijing Sun
D. Fenning
Zhe Liu
Tonio Buonassisi
28
10
0
08 Oct 2021
Robotic Lever Manipulation using Hindsight Experience Replay and Shapley
  Additive Explanations
Robotic Lever Manipulation using Hindsight Experience Replay and Shapley Additive Explanations
Sindre Benjamin Remman
A. Lekkas
28
14
0
07 Oct 2021
Fast TreeSHAP: Accelerating SHAP Value Computation for Trees
Fast TreeSHAP: Accelerating SHAP Value Computation for Trees
Jilei Yang
FAtt
56
35
0
20 Sep 2021
Model Explanations via the Axiomatic Causal Lens
Gagan Biradar
Vignesh Viswanathan
Yair Zick
XAI
CML
30
1
0
08 Sep 2021
Instance-wise or Class-wise? A Tale of Neighbor Shapley for
  Concept-based Explanation
Instance-wise or Class-wise? A Tale of Neighbor Shapley for Concept-based Explanation
Jiahui Li
Kun Kuang
Lin Li
Long Chen
Songyang Zhang
Jian Shao
Jun Xiao
FAtt
25
18
0
03 Sep 2021
What will it take to generate fairness-preserving explanations?
What will it take to generate fairness-preserving explanations?
Jessica Dai
Sohini Upadhyay
Stephen H. Bach
Himabindu Lakkaraju
FAtt
FaML
26
14
0
24 Jun 2021
Rational Shapley Values
Rational Shapley Values
David S. Watson
28
20
0
18 Jun 2021
FairCanary: Rapid Continuous Explainable Fairness
FairCanary: Rapid Continuous Explainable Fairness
Avijit Ghosh
Aalok Shanbhag
Christo Wilson
19
20
0
13 Jun 2021
On the Lack of Robust Interpretability of Neural Text Classifiers
On the Lack of Robust Interpretability of Neural Text Classifiers
Muhammad Bilal Zafar
Michele Donini
Dylan Slack
Cédric Archambeau
Sanjiv Ranjan Das
K. Kenthapadi
AAML
16
21
0
08 Jun 2021
Shapley Counterfactual Credits for Multi-Agent Reinforcement Learning
Shapley Counterfactual Credits for Multi-Agent Reinforcement Learning
Jiahui Li
Kun Kuang
Baoxiang Wang
Furui Liu
Long Chen
Fei Wu
Jun Xiao
OffRL
36
61
0
01 Jun 2021
SHAQ: Incorporating Shapley Value Theory into Multi-Agent Q-Learning
SHAQ: Incorporating Shapley Value Theory into Multi-Agent Q-Learning
Jianhong Wang
Yuan Zhang
Yunjie Gu
Tae-Kyun Kim
OffRL
FAtt
22
19
0
31 May 2021
Cohort Shapley value for algorithmic fairness
Cohort Shapley value for algorithmic fairness
Masayoshi Mase
Art B. Owen
Benjamin B. Seiler
26
14
0
15 May 2021
Winter wheat yield prediction using convolutional neural networks from
  environmental and phenological data
Winter wheat yield prediction using convolutional neural networks from environmental and phenological data
A. Srivastava
N. Safaei
S. Khaki
Gina M. Lopez
W. Zeng
F. Ewert
T. Gaiser
Jaber Rahimi
42
101
0
04 May 2021
Towards Rigorous Interpretations: a Formalisation of Feature Attribution
Towards Rigorous Interpretations: a Formalisation of Feature Attribution
Darius Afchar
Romain Hennequin
Vincent Guigue
FAtt
52
20
0
26 Apr 2021
Local Explanations via Necessity and Sufficiency: Unifying Theory and
  Practice
Local Explanations via Necessity and Sufficiency: Unifying Theory and Practice
David S. Watson
Limor Gultchin
Ankur Taly
Luciano Floridi
27
63
0
27 Mar 2021
Explaining Black-Box Algorithms Using Probabilistic Contrastive
  Counterfactuals
Explaining Black-Box Algorithms Using Probabilistic Contrastive Counterfactuals
Sainyam Galhotra
Romila Pradhan
Babak Salimi
CML
39
106
0
22 Mar 2021
Ensembles of Random SHAPs
Ensembles of Random SHAPs
Lev V. Utkin
A. Konstantinov
FAtt
21
20
0
04 Mar 2021
Contrastive Explanations for Model Interpretability
Contrastive Explanations for Model Interpretability
Alon Jacovi
Swabha Swayamdipta
Shauli Ravfogel
Yanai Elazar
Yejin Choi
Yoav Goldberg
70
95
0
02 Mar 2021
Shapley values for feature selection: The good, the bad, and the axioms
Shapley values for feature selection: The good, the bad, and the axioms
D. Fryer
Inga Strümke
Hien Nguyen
FAtt
TDI
16
192
0
22 Feb 2021
Outcome-Explorer: A Causality Guided Interactive Visual Interface for
  Interpretable Algorithmic Decision Making
Outcome-Explorer: A Causality Guided Interactive Visual Interface for Interpretable Algorithmic Decision Making
Md. Naimul Hoque
Klaus Mueller
CML
59
30
0
03 Jan 2021
Interpretability and Explainability: A Machine Learning Zoo Mini-tour
Interpretability and Explainability: A Machine Learning Zoo Mini-tour
Ricards Marcinkevics
Julia E. Vogt
XAI
33
119
0
03 Dec 2020
Explaining by Removing: A Unified Framework for Model Explanation
Explaining by Removing: A Unified Framework for Model Explanation
Ian Covert
Scott M. Lundberg
Su-In Lee
FAtt
53
244
0
21 Nov 2020
Towards Unifying Feature Attribution and Counterfactual Explanations:
  Different Means to the Same End
Towards Unifying Feature Attribution and Counterfactual Explanations: Different Means to the Same End
R. Mothilal
Divyat Mahajan
Chenhao Tan
Amit Sharma
FAtt
CML
32
100
0
10 Nov 2020
Causal Shapley Values: Exploiting Causal Knowledge to Explain Individual
  Predictions of Complex Models
Causal Shapley Values: Exploiting Causal Knowledge to Explain Individual Predictions of Complex Models
Tom Heskes
E. Sijben
I. G. Bucur
Tom Claassen
FAtt
TDI
25
151
0
03 Nov 2020
Interpretable Machine Learning -- A Brief History, State-of-the-Art and
  Challenges
Interpretable Machine Learning -- A Brief History, State-of-the-Art and Challenges
Christoph Molnar
Giuseppe Casalicchio
B. Bischl
AI4TS
AI4CE
36
398
0
19 Oct 2020
Information-Theoretic Visual Explanation for Black-Box Classifiers
Information-Theoretic Visual Explanation for Black-Box Classifiers
Jihun Yi
Eunji Kim
Siwon Kim
Sungroh Yoon
FAtt
25
6
0
23 Sep 2020
Principles and Practice of Explainable Machine Learning
Principles and Practice of Explainable Machine Learning
Vaishak Belle
I. Papantonis
FaML
26
438
0
18 Sep 2020
On the Tractability of SHAP Explanations
On the Tractability of SHAP Explanations
Guy Van den Broeck
A. Lykov
Maximilian Schleich
Dan Suciu
FAtt
TDI
34
262
0
18 Sep 2020
Predictive Multiplicity in Classification
Predictive Multiplicity in Classification
Charles Marx
Flavio du Pin Calmon
Berk Ustun
36
138
0
14 Sep 2019
Towards A Rigorous Science of Interpretable Machine Learning
Towards A Rigorous Science of Interpretable Machine Learning
Finale Doshi-Velez
Been Kim
XAI
FaML
269
3,705
0
28 Feb 2017
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