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On Guaranteed Optimal Robust Explanations for NLP Models

On Guaranteed Optimal Robust Explanations for NLP Models

8 May 2021
Emanuele La Malfa
A. Zbrzezny
Rhiannon Michelmore
Nicola Paoletti
Marta Z. Kwiatkowska
    FAtt
ArXivPDFHTML

Papers citing "On Guaranteed Optimal Robust Explanations for NLP Models"

15 / 15 papers shown
Title
When to Trust AI: Advances and Challenges for Certification of Neural
  Networks
When to Trust AI: Advances and Challenges for Certification of Neural Networks
Marta Kwiatkowska
Xiyue Zhang
AAML
37
8
0
20 Sep 2023
Finding Minimum-Cost Explanations for Predictions made by Tree Ensembles
Finding Minimum-Cost Explanations for Predictions made by Tree Ensembles
John Törnblom
Emil Karlsson
Simin Nadjm-Tehrani
FAtt
54
0
0
16 Mar 2023
Robust Explanation Constraints for Neural Networks
Robust Explanation Constraints for Neural Networks
Matthew Wicker
Juyeon Heo
Luca Costabello
Adrian Weller
FAtt
29
18
0
16 Dec 2022
VeriX: Towards Verified Explainability of Deep Neural Networks
VeriX: Towards Verified Explainability of Deep Neural Networks
Min Wu
Haoze Wu
Clark W. Barrett
AAML
42
11
0
02 Dec 2022
Emergent Linguistic Structures in Neural Networks are Fragile
Emergent Linguistic Structures in Neural Networks are Fragile
Emanuele La Malfa
Matthew Wicker
Marta Kiatkowska
22
1
0
31 Oct 2022
Feature Necessity & Relevancy in ML Classifier Explanations
Feature Necessity & Relevancy in ML Classifier Explanations
Xuanxiang Huang
Martin C. Cooper
António Morgado
Jordi Planes
Sasha Rubin
FAtt
35
18
0
27 Oct 2022
Logic-Based Explainability in Machine Learning
Logic-Based Explainability in Machine Learning
Sasha Rubin
LRM
XAI
50
39
0
24 Oct 2022
On Computing Relevant Features for Explaining NBCs
On Computing Relevant Features for Explaining NBCs
Yacine Izza
Sasha Rubin
36
5
0
11 Jul 2022
Eliminating The Impossible, Whatever Remains Must Be True
Eliminating The Impossible, Whatever Remains Must Be True
Jinqiang Yu
Alexey Ignatiev
Peter J. Stuckey
Nina Narodytska
Sasha Rubin
22
23
0
20 Jun 2022
Fooling Explanations in Text Classifiers
Fooling Explanations in Text Classifiers
Adam Ivankay
Ivan Girardi
Chiara Marchiori
P. Frossard
AAML
30
20
0
07 Jun 2022
On Tackling Explanation Redundancy in Decision Trees
On Tackling Explanation Redundancy in Decision Trees
Yacine Izza
Alexey Ignatiev
Sasha Rubin
FAtt
48
58
0
20 May 2022
The King is Naked: on the Notion of Robustness for Natural Language
  Processing
The King is Naked: on the Notion of Robustness for Natural Language Processing
Emanuele La Malfa
Marta Z. Kwiatkowska
20
28
0
13 Dec 2021
Certified Robustness to Adversarial Word Substitutions
Certified Robustness to Adversarial Word Substitutions
Robin Jia
Aditi Raghunathan
Kerem Göksel
Percy Liang
AAML
183
291
0
03 Sep 2019
Generating Natural Language Adversarial Examples
Generating Natural Language Adversarial Examples
M. Alzantot
Yash Sharma
Ahmed Elgohary
Bo-Jhang Ho
Mani B. Srivastava
Kai-Wei Chang
AAML
254
915
0
21 Apr 2018
Efficient Estimation of Word Representations in Vector Space
Efficient Estimation of Word Representations in Vector Space
Tomáš Mikolov
Kai Chen
G. Corrado
J. Dean
3DV
284
31,267
0
16 Jan 2013
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