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1802.07384
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Interpreting Neural Network Judgments via Minimal, Stable, and Symbolic Corrections
21 February 2018
Xin Zhang
Armando Solar-Lezama
Rishabh Singh
FAtt
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Papers citing
"Interpreting Neural Network Judgments via Minimal, Stable, and Symbolic Corrections"
29 / 29 papers shown
XAIR: A Framework of Explainable AI in Augmented Reality
International Conference on Human Factors in Computing Systems (CHI), 2023
Xuhai Xu
Anna Yu
Tanya R. Jonker
Kashyap Todi
Feiyu Lu
...
Narine Kokhlikyan
Fulton Wang
P. Sorenson
Sophie Kahyun Kim
Hrvoje Benko
160
78
0
28 Mar 2023
VeriX: Towards Verified Explainability of Deep Neural Networks
Neural Information Processing Systems (NeurIPS), 2022
Min Wu
Haoze Wu
Clark W. Barrett
AAML
434
23
0
02 Dec 2022
Cardinality-Minimal Explanations for Monotonic Neural Networks
International Joint Conference on Artificial Intelligence (IJCAI), 2022
Ouns El Harzli
Bernardo Cuenca Grau
Ian Horrocks
FAtt
303
7
0
19 May 2022
On the Robustness of Sparse Counterfactual Explanations to Adverse Perturbations
Artificial Intelligence (AIJ), 2022
M. Virgolin
Saverio Fracaros
CML
349
39
0
22 Jan 2022
From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI
ACM Computing Surveys (ACM CSUR), 2022
Meike Nauta
Jan Trienes
Shreyasi Pathak
Elisa Nguyen
Michelle Peters
Yasmin Schmitt
Jorg Schlotterer
M. V. Keulen
C. Seifert
ELM
XAI
619
577
0
20 Jan 2022
Instructive artificial intelligence (AI) for human training, assistance, and explainability
Nicholas Kantack
Nina Cohen
Nathan D. Bos
Corey Lowman
James Everett
Timothy Endres
106
4
0
02 Nov 2021
Synthesizing Pareto-Optimal Interpretations for Black-Box Models
Hazem Torfah
Shetal Shah
Supratik Chakraborty
S. Akshay
Sanjit A. Seshia
221
8
0
16 Aug 2021
Fair Normalizing Flows
International Conference on Learning Representations (ICLR), 2021
Mislav Balunović
Anian Ruoss
Martin Vechev
AAML
290
46
0
10 Jun 2021
Neuro-Symbolic Artificial Intelligence: Current Trends
AI Communications (AI Commun.), 2021
Md Kamruzzaman Sarker
Lu Zhou
Aaron Eberhart
Pascal Hitzler
NAI
273
122
0
11 May 2021
Local Explanations via Necessity and Sufficiency: Unifying Theory and Practice
Minds and Machines (Minds Mach.), 2021
David S. Watson
Limor Gultchin
Ankur Taly
Luciano Floridi
220
71
0
27 Mar 2021
Interpretable Machine Learning: Moving From Mythos to Diagnostics
Queue (ACM Queue), 2021
Valerie Chen
Jeffrey Li
Joon Sik Kim
Gregory Plumb
Ameet Talwalkar
232
32
0
10 Mar 2021
If Only We Had Better Counterfactual Explanations: Five Key Deficits to Rectify in the Evaluation of Counterfactual XAI Techniques
International Joint Conference on Artificial Intelligence (IJCAI), 2021
Mark T. Keane
Eoin M. Kenny
Eoin Delaney
Barry Smyth
CML
322
169
0
26 Feb 2021
Learning outside the Black-Box: The pursuit of interpretable models
Neural Information Processing Systems (NeurIPS), 2020
Jonathan Crabbé
Yao Zhang
W. Zame
M. Schaar
118
27
0
17 Nov 2020
Robust and Stable Black Box Explanations
International Conference on Machine Learning (ICML), 2020
Himabindu Lakkaraju
Nino Arsov
Osbert Bastani
AAML
FAtt
194
92
0
12 Nov 2020
Learning Models for Actionable Recourse
Neural Information Processing Systems (NeurIPS), 2020
Alexis Ross
Himabindu Lakkaraju
Osbert Bastani
FaML
251
20
0
12 Nov 2020
A survey of algorithmic recourse: definitions, formulations, solutions, and prospects
Amir-Hossein Karimi
Gilles Barthe
Bernhard Schölkopf
Isabel Valera
FaML
364
185
0
08 Oct 2020
Scaling Symbolic Methods using Gradients for Neural Model Explanation
Subham S. Sahoo
Subhashini Venugopalan
Li Li
Rishabh Singh
Patrick F. Riley
FAtt
276
8
0
29 Jun 2020
Generative causal explanations of black-box classifiers
Neural Information Processing Systems (NeurIPS), 2020
Matthew R. O’Shaughnessy
Gregory H. Canal
Marissa Connor
Mark A. Davenport
Christopher Rozell
CML
330
77
0
24 Jun 2020
Explaining Groups of Points in Low-Dimensional Representations
International Conference on Machine Learning (ICML), 2020
Gregory Plumb
Jonathan Terhorst
S. Sankararaman
Ameet Talwalkar
303
31
0
03 Mar 2020
Questioning the AI: Informing Design Practices for Explainable AI User Experiences
International Conference on Human Factors in Computing Systems (CHI), 2020
Q. V. Liao
D. Gruen
Sarah Miller
556
823
0
08 Jan 2020
Automated Dependence Plots
David I. Inouye
Liu Leqi
Joon Sik Kim
Bryon Aragam
Pradeep Ravikumar
206
1
0
02 Dec 2019
GRACE: Generating Concise and Informative Contrastive Sample to Explain Neural Network Model's Prediction
Thai V. Le
Suhang Wang
Dongwon Lee
257
1
0
05 Nov 2019
Synthesizing Action Sequences for Modifying Model Decisions
AAAI Conference on Artificial Intelligence (AAAI), 2019
Goutham Ramakrishnan
Yun Chan Lee
Aws Albarghouthi
450
37
0
30 Sep 2019
Model Agnostic Contrastive Explanations for Structured Data
Amit Dhurandhar
Tejaswini Pedapati
Avinash Balakrishnan
Pin-Yu Chen
Karthikeyan Shanmugam
Ruchi Puri
FAtt
269
90
0
31 May 2019
Explainability Techniques for Graph Convolutional Networks
International Conference on Machine Learning (ICML), 2019
Federico Baldassarre
Hossein Azizpour
GNN
FAtt
319
315
0
31 May 2019
Leveraging Latent Features for Local Explanations
Knowledge Discovery and Data Mining (KDD), 2019
Ronny Luss
Pin-Yu Chen
Amit Dhurandhar
P. Sattigeri
Yunfeng Zhang
Karthikeyan Shanmugam
Chun-Chen Tu
FAtt
290
37
0
29 May 2019
Explaining Explanations to Society
Leilani H. Gilpin
Cecilia Testart
Nathaniel Fruchter
Julius Adebayo
XAI
246
36
0
19 Jan 2019
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
396
651
0
21 Feb 2018
A Zero-Positive Learning Approach for Diagnosing Software Performance Regressions
Mejbah Alam
Justin Emile Gottschlich
Nesime Tatbul
Javier S. Turek
Tim Mattson
A. Muzahid
301
30
0
21 Sep 2017
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