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2011.06169
Cited By
Robust and Stable Black Box Explanations
International Conference on Machine Learning (ICML), 2020
12 November 2020
Himabindu Lakkaraju
Nino Arsov
Osbert Bastani
AAML
FAtt
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Papers citing
"Robust and Stable Black Box Explanations"
47 / 47 papers shown
Axiomatic Explainer Globalness via Optimal Transport
International Conference on Artificial Intelligence and Statistics (AISTATS), 2024
Davin Hill
Josh Bone
A. Masoomi
Max Torop
Jennifer Dy
558
2
0
13 Mar 2025
Interpretable Model Drift Detection
Pranoy Panda
Kancheti Sai Srinivas
V. Balasubramanian
Gaurav Sinha
370
2
0
09 Mar 2025
An Evaluation of Explanation Methods for Black-Box Detectors of Machine-Generated Text
Loris Schoenegger
Yuxi Xia
Benjamin Roth
FAtt
304
4
0
26 Aug 2024
On the Robustness of Global Feature Effect Explanations
Hubert Baniecki
Giuseppe Casalicchio
B. Bischl
P. Biecek
378
4
0
13 Jun 2024
Robust Explainable Recommendation
Sairamvinay Vijayaraghavan
Prasant Mohapatra
AAML
352
2
0
03 May 2024
T-Explainer: A Model-Agnostic Explainability Framework Based on Gradients
Evandro S. Ortigossa
Fábio F. Dias
Brian Barr
Claudio T. Silva
L. G. Nonato
FAtt
543
7
0
25 Apr 2024
Revealing Vulnerabilities of Neural Networks in Parameter Learning and Defense Against Explanation-Aware Backdoors
Md Abdul Kadir
G. Addluri
Daniel Sonntag
AAML
354
0
0
25 Mar 2024
X Hacking: The Threat of Misguided AutoML
Rahul Sharma
Sergey Redyuk
Sumantrak Mukherjee
Andrea Sipka
Eyke Hüllermeier
Sebastian Vollmer
David Selby
556
5
0
16 Jan 2024
Advancing Ante-Hoc Explainable Models through Generative Adversarial Networks
Tanmay Garg
Deepika Vemuri
Vineeth N. Balasubramanian
GAN
262
3
0
09 Jan 2024
Rethinking Robustness of Model Attributions
AAAI Conference on Artificial Intelligence (AAAI), 2023
Sandesh Kamath
Sankalp Mittal
Amit Deshpande
Vineeth N. Balasubramanian
276
2
0
16 Dec 2023
How Well Do Feature-Additive Explainers Explain Feature-Additive Predictors?
Zachariah Carmichael
Walter J. Scheirer
FAtt
306
9
0
27 Oct 2023
Confident Feature Ranking
International Conference on Artificial Intelligence and Statistics (AISTATS), 2023
Bitya Neuhof
Y. Benjamini
FAtt
373
5
0
28 Jul 2023
Explainable AI using expressive Boolean formulas
Machine Learning and Knowledge Extraction (MLKE), 2023
G. Rosenberg
J. K. Brubaker
M. Schuetz
Grant Salton
Zhihuai Zhu
E. Zhu
Serdar Kadioğlu
S. E. Borujeni
H. Katzgraber
262
12
0
06 Jun 2023
Adversarial attacks and defenses in explainable artificial intelligence: A survey
Information Fusion (Inf. Fusion), 2023
Hubert Baniecki
P. Biecek
AAML
634
141
0
06 Jun 2023
Post Hoc Explanations of Language Models Can Improve Language Models
Neural Information Processing Systems (NeurIPS), 2023
Satyapriya Krishna
Jiaqi Ma
Dylan Slack
Asma Ghandeharioun
Sameer Singh
Himabindu Lakkaraju
ReLM
LRM
285
78
0
19 May 2023
Robust Explanation Constraints for Neural Networks
International Conference on Learning Representations (ICLR), 2022
Matthew Wicker
Juyeon Heo
Luca Costabello
Adrian Weller
FAtt
264
26
0
16 Dec 2022
Understanding and Enhancing Robustness of Concept-based Models
AAAI Conference on Artificial Intelligence (AAAI), 2022
Sanchit Sinha
Mengdi Huai
Jianhui Sun
Aidong Zhang
AAML
278
29
0
29 Nov 2022
A.I. Robustness: a Human-Centered Perspective on Technological Challenges and Opportunities
ACM Computing Surveys (ACM CSUR), 2022
Andrea Tocchetti
Lorenzo Corti
Agathe Balayn
Mireia Yurrita
Philip Lippmann
Marco Brambilla
Jie Yang
371
33
0
17 Oct 2022
Inferring Sensitive Attributes from Model Explanations
International Conference on Information and Knowledge Management (CIKM), 2022
Vasisht Duddu
A. Boutet
MIACV
SILM
335
24
0
21 Aug 2022
A Query-Optimal Algorithm for Finding Counterfactuals
International Conference on Machine Learning (ICML), 2022
Guy Blanc
Caleb M. Koch
Jane Lange
Li-Yang Tan
329
8
0
14 Jul 2022
Explaining the root causes of unit-level changes
Kailash Budhathoki
George Michailidis
Dominik Janzing
FAtt
226
4
0
26 Jun 2022
Analyzing Explainer Robustness via Probabilistic Lipschitzness of Prediction Functions
International Conference on Artificial Intelligence and Statistics (AISTATS), 2022
Zulqarnain Khan
Davin Hill
A. Masoomi
Joshua Bone
Jennifer Dy
AAML
532
7
0
24 Jun 2022
An empirical study of the effect of background data size on the stability of SHapley Additive exPlanations (SHAP) for deep learning models
Han Yuan
Mingxuan Liu
Lican Kang
Chenkui Miao
Ying Wu
FAtt
300
14
0
24 Apr 2022
Framework for Evaluating Faithfulness of Local Explanations
International Conference on Machine Learning (ICML), 2022
S. Dasgupta
Nave Frost
Michal Moshkovitz
FAtt
482
84
0
01 Feb 2022
Locally Invariant Explanations: Towards Stable and Unidirectional Explanations through Local Invariant Learning
Neural Information Processing Systems (NeurIPS), 2022
Amit Dhurandhar
Karthikeyan N. Ramamurthy
Kartik Ahuja
Vijay Arya
FAtt
356
6
0
28 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
810
640
0
20 Jan 2022
Provably efficient, succinct, and precise explanations
Neural Information Processing Systems (NeurIPS), 2021
Guy Blanc
Jane Lange
Li-Yang Tan
FAtt
324
42
0
01 Nov 2021
A Survey on the Robustness of Feature Importance and Counterfactual Explanations
Saumitra Mishra
Sanghamitra Dutta
Jason Long
Daniele Magazzeni
AAML
337
67
0
30 Oct 2021
Making Corgis Important for Honeycomb Classification: Adversarial Attacks on Concept-based Explainability Tools
Davis Brown
Henry Kvinge
AAML
316
12
0
14 Oct 2021
Self-learn to Explain Siamese Networks Robustly
Chao Chen
Yifan Shen
Guixiang Ma
Xiangnan Kong
S. Rangarajan
Xi Zhang
Sihong Xie
220
8
0
15 Sep 2021
A Framework for Learning Ante-hoc Explainable Models via Concepts
Computer Vision and Pattern Recognition (CVPR), 2021
Anirban Sarkar
Deepak Vijaykeerthy
Anindya Sarkar
V. Balasubramanian
LRM
BDL
292
68
0
25 Aug 2021
Perturbing Inputs for Fragile Interpretations in Deep Natural Language Processing
BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP (BlackBoxNLP), 2021
Sanchit Sinha
Hanjie Chen
Arshdeep Sekhon
Yangfeng Ji
Yanjun Qi
AAML
FAtt
345
49
0
11 Aug 2021
Extending LIME for Business Process Automation
Sohini Upadhyay
Vatche Isahagian
Vinod Muthusamy
Sadhana Kumaravel
FAtt
260
5
0
09 Aug 2021
Probing GNN Explainers: A Rigorous Theoretical and Empirical Analysis of GNN Explanation Methods
Chirag Agarwal
Marinka Zitnik
Himabindu Lakkaraju
292
66
0
16 Jun 2021
Taxonomy of Machine Learning Safety: A Survey and Primer
ACM Computing Surveys (CSUR), 2021
Sina Mohseni
Haotao Wang
Zhiding Yu
Chaowei Xiao
Zinan Lin
J. Yadawa
366
50
0
09 Jun 2021
On the Lack of Robust Interpretability of Neural Text Classifiers
Findings (Findings), 2021
Muhammad Bilal Zafar
Michele Donini
Dylan Slack
Cédric Archambeau
Sanjiv Ranjan Das
K. Kenthapadi
AAML
158
25
0
08 Jun 2021
Evaluating Local Explanations using White-box Models
Amir Hossein Akhavan Rahnama
Judith Butepage
Pierre Geurts
Henrik Bostrom
FAtt
301
0
0
04 Jun 2021
Towards Robust and Reliable Algorithmic Recourse
Neural Information Processing Systems (NeurIPS), 2021
Sohini Upadhyay
Shalmali Joshi
Himabindu Lakkaraju
385
127
0
26 Feb 2021
Do Input Gradients Highlight Discriminative Features?
Neural Information Processing Systems (NeurIPS), 2021
Harshay Shah
Prateek Jain
Praneeth Netrapalli
AAML
FAtt
411
69
0
25 Feb 2021
Attribution Mask: Filtering Out Irrelevant Features By Recursively Focusing Attention on Inputs of DNNs
Jaehwan Lee
Joon‐Hyuk Chang
TDI
FAtt
248
0
0
15 Feb 2021
Connecting Interpretability and Robustness in Decision Trees through Separation
International Conference on Machine Learning (ICML), 2021
Michal Moshkovitz
Yao-Yuan Yang
Kamalika Chaudhuri
191
26
0
14 Feb 2021
Towards Robust Explanations for Deep Neural Networks
Pattern Recognition (Pattern Recognit.), 2020
Ann-Kathrin Dombrowski
Christopher J. Anders
K. Müller
Pan Kessel
FAtt
357
67
0
18 Dec 2020
Learning Models for Actionable Recourse
Neural Information Processing Systems (NeurIPS), 2020
Alexis Ross
Himabindu Lakkaraju
Osbert Bastani
FaML
314
20
0
12 Nov 2020
A Framework to Learn with Interpretation
Neural Information Processing Systems (NeurIPS), 2020
Jayneel Parekh
Pavlo Mozharovskyi
Florence dÁlché-Buc
AI4CE
FAtt
471
34
0
19 Oct 2020
Counterfactual Explanations for Machine Learning on Multivariate Time Series Data
E. Ates
Burak Aksar
V. Leung
A. Coskun
AI4TS
301
99
0
25 Aug 2020
The best way to select features?
The Journal of Financial Data Science (JFDS), 2020
Xin Man
Ernest P. Chan
135
68
0
26 May 2020
Adversarial Discriminative Domain Adaptation
Computer Vision and Pattern Recognition (CVPR), 2017
Eric Tzeng
Judy Hoffman
Kate Saenko
Trevor Darrell
GAN
OOD
1.5K
5,156
0
17 Feb 2017
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