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Gradient-based Counterfactual Explanations using Tractable Probabilistic
  Models

Gradient-based Counterfactual Explanations using Tractable Probabilistic Models

16 May 2022
Xiaoting Shao
Kristian Kersting
    BDL
ArXivPDFHTML

Papers citing "Gradient-based Counterfactual Explanations using Tractable Probabilistic Models"

28 / 28 papers shown
Title
Counterfactual State Explanations for Reinforcement Learning Agents via
  Generative Deep Learning
Counterfactual State Explanations for Reinforcement Learning Agents via Generative Deep Learning
Matthew Lyle Olson
Roli Khanna
Lawrence Neal
Fuxin Li
Weng-Keen Wong
CML
58
71
0
29 Jan 2021
Getting a CLUE: A Method for Explaining Uncertainty Estimates
Getting a CLUE: A Method for Explaining Uncertainty Estimates
Javier Antorán
Umang Bhatt
T. Adel
Adrian Weller
José Miguel Hernández-Lobato
UQCV
BDL
71
115
0
11 Jun 2020
Learning Model-Agnostic Counterfactual Explanations for Tabular Data
Learning Model-Agnostic Counterfactual Explanations for Tabular Data
Martin Pawelczyk
Johannes Haug
Klaus Broelemann
Gjergji Kasneci
OOD
CML
58
203
0
21 Oct 2019
FACE: Feasible and Actionable Counterfactual Explanations
FACE: Feasible and Actionable Counterfactual Explanations
Rafael Poyiadzi
Kacper Sokol
Raúl Santos-Rodríguez
T. D. Bie
Peter A. Flach
66
368
0
20 Sep 2019
Towards Realistic Individual Recourse and Actionable Explanations in
  Black-Box Decision Making Systems
Towards Realistic Individual Recourse and Actionable Explanations in Black-Box Decision Making Systems
Shalmali Joshi
Oluwasanmi Koyejo
Warut D. Vijitbenjaronk
Been Kim
Joydeep Ghosh
FaML
56
186
0
22 Jul 2019
Explaining Machine Learning Classifiers through Diverse Counterfactual
  Explanations
Explaining Machine Learning Classifiers through Diverse Counterfactual Explanations
R. Mothilal
Amit Sharma
Chenhao Tan
CML
106
1,005
0
19 May 2019
Counterfactual Visual Explanations
Counterfactual Visual Explanations
Yash Goyal
Ziyan Wu
Jan Ernst
Dhruv Batra
Devi Parikh
Stefan Lee
CML
73
510
0
16 Apr 2019
Diagnosing and Enhancing VAE Models
Diagnosing and Enhancing VAE Models
Bin Dai
David Wipf
DRL
55
378
0
14 Mar 2019
Learning Latent Subspaces in Variational Autoencoders
Learning Latent Subspaces in Variational Autoencoders
Jack Klys
Jake C. Snell
R. Zemel
SSL
DRL
111
139
0
14 Dec 2018
Do Deep Generative Models Know What They Don't Know?
Do Deep Generative Models Know What They Don't Know?
Eric T. Nalisnick
Akihiro Matsukawa
Yee Whye Teh
Dilan Görür
Balaji Lakshminarayanan
OOD
51
753
0
22 Oct 2018
WAIC, but Why? Generative Ensembles for Robust Anomaly Detection
WAIC, but Why? Generative Ensembles for Robust Anomaly Detection
Hyun-Jae Choi
Eric Jang
Alexander A. Alemi
OODD
56
82
0
02 Oct 2018
Actionable Recourse in Linear Classification
Actionable Recourse in Linear Classification
Berk Ustun
Alexander Spangher
Yang Liu
FaML
90
545
0
18 Sep 2018
Assessing Gender Bias in Machine Translation -- A Case Study with Google
  Translate
Assessing Gender Bias in Machine Translation -- A Case Study with Google Translate
Marcelo O. R. Prates
Pedro H. C. Avelar
Luís C. Lamb
53
345
0
06 Sep 2018
Explaining Image Classifiers by Counterfactual Generation
Explaining Image Classifiers by Counterfactual Generation
C. Chang
Elliot Creager
Anna Goldenberg
David Duvenaud
VLM
58
265
0
20 Jul 2018
Variational Autoencoder with Arbitrary Conditioning
Variational Autoencoder with Arbitrary Conditioning
Oleg Ivanov
Michael Figurnov
Dmitry Vetrov
BDL
DRL
42
146
0
06 Jun 2018
Explanations based on the Missing: Towards Contrastive Explanations with
  Pertinent Negatives
Explanations based on the Missing: Towards Contrastive Explanations with Pertinent Negatives
Amit Dhurandhar
Pin-Yu Chen
Ronny Luss
Chun-Chen Tu
Pai-Shun Ting
Karthikeyan Shanmugam
Payel Das
FAtt
99
587
0
21 Feb 2018
Adversarial Examples: Attacks and Defenses for Deep Learning
Adversarial Examples: Attacks and Defenses for Deep Learning
Xiaoyong Yuan
Pan He
Qile Zhu
Xiaolin Li
SILM
AAML
70
1,614
0
19 Dec 2017
Counterfactual Explanations without Opening the Black Box: Automated
  Decisions and the GDPR
Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR
Sandra Wachter
Brent Mittelstadt
Chris Russell
MLAU
91
2,332
0
01 Nov 2017
Towards A Rigorous Science of Interpretable Machine Learning
Towards A Rigorous Science of Interpretable Machine Learning
Finale Doshi-Velez
Been Kim
XAI
FaML
359
3,742
0
28 Feb 2017
Towards Evaluating the Robustness of Neural Networks
Towards Evaluating the Robustness of Neural Networks
Nicholas Carlini
D. Wagner
OOD
AAML
193
8,513
0
16 Aug 2016
Adversarial examples in the physical world
Adversarial examples in the physical world
Alexey Kurakin
Ian Goodfellow
Samy Bengio
SILM
AAML
512
5,878
0
08 Jul 2016
DeepFool: a simple and accurate method to fool deep neural networks
DeepFool: a simple and accurate method to fool deep neural networks
Seyed-Mohsen Moosavi-Dezfooli
Alhussein Fawzi
P. Frossard
AAML
113
4,878
0
14 Nov 2015
Explaining and Harnessing Adversarial Examples
Explaining and Harnessing Adversarial Examples
Ian Goodfellow
Jonathon Shlens
Christian Szegedy
AAML
GAN
208
18,922
0
20 Dec 2014
Very Deep Convolutional Networks for Large-Scale Image Recognition
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan
Andrew Zisserman
FAtt
MDE
1.2K
99,991
0
04 Sep 2014
Intriguing properties of neural networks
Intriguing properties of neural networks
Christian Szegedy
Wojciech Zaremba
Ilya Sutskever
Joan Bruna
D. Erhan
Ian Goodfellow
Rob Fergus
AAML
211
14,831
1
21 Dec 2013
Auto-Encoding Variational Bayes
Auto-Encoding Variational Bayes
Diederik P. Kingma
Max Welling
BDL
397
16,962
0
20 Dec 2013
A Differential Approach to Inference in Bayesian Networks
A Differential Approach to Inference in Bayesian Networks
Adnan Darwiche
TPM
75
619
0
16 Jan 2013
Sum-Product Networks: A New Deep Architecture
Sum-Product Networks: A New Deep Architecture
Hoifung Poon
Pedro M. Domingos
TPM
74
757
0
14 Feb 2012
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